Artificial intelligence system for comprehensive medical diagnosis, prognosis, and treatment optimization through medical imaging

Abstract

Systems and methods for comprehensive medical diagnosis, prognosis, and treatment optimization are provided. A neural network is trained on a large dataset of medical images or raw data from various imaging modalities, such as ultrasound, MRI, CT, and X-ray, which are labeled with ground truth diagnoses of a wide range of medical conditions. The trained neural network can then be provided with medical images of a patient, and the neural network can make predictions and provide insights related to the presence, absence, severity, progression, or risk of various medical conditions. These predictions and insights can support clinical decision-making and enable early intervention, personalized treatment, and improved patient outcomes. The system can be continually updated with new data to improve its performance over time, and can be integrated into healthcare workflows to enhance the accuracy, efficiency, and effectiveness of medical diagnosis and treatment.

Description

Artificial Intelligence System for Comprehensive Medical Diagnosis, Prognosis, and Treatment Optimization through Medical Imaging CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a Continuation-In-Part of U.S. Patent Application No. 17 / 573,246 filed on January 11, 2022 which is a Continuation of U.S. Patent Application No. 17 / 352,290 filed June 19, 2021, now U.S. Patent No.11,266,376, which claims the benefit of U.S. Provisional Patent Application No.63 / 041,360 filed on June 19, 2020; U.S. Patent Application No.17 / 573,246 also is a Continuation of International Application PCT / US21 / 38164 filed on June 20, 2021. This application also claims the benefit of U.S. Provisional Patent Application No.63 / 451,844 filed March 13, 2023. The disclosures of each of the aforementioned patent applications are incorporated herein by reference. BACKGROUND OF THE INVENTION

[0002] Field of the Invention

[0003] The present invention relates to the field of medical imaging and artificial intelligence (AI). More specifically, it pertains to the integration of AI algorithms with ultrasound imaging for comprehensive medical diagnosis, prognosis, treatment, and monitoring. The invention encompasses a wide range of medical applications, including early detection and prediction of diseases, objective symptom measurement, treatment optimization, surgical planning, and real-time monitoring.

[0004] Description of the Prior Art

[0005] Ultrasound imaging is a well-established, non-invasive medical imaging modality that has been widely used for decades in various healthcare settings. It allows healthcare professionals to visualize internal body structures in real-time, without exposing patients to ionizing radiation. Ultrasound imaging has been particularly valuable in areas such as obstetrics, cardiology, and oncology, among others.

[0006] However, traditional ultrasound imaging relies heavily on the expertise of the operator for image acquisition and interpretation. This can lead to variability and subjectivity in diagnosis and treatment decisions, as well as a potential for missing subtle abnormalities or early signs of disease. Moreover, the vast amount of data generated by ultrasound imaging is often underutilized, as manual analysis can be time-consuming and may not fully capture complex patterns and anomalies. 1 9354.06PCT

[0007] In recent years, there has been a growing interest in applying AI techniques to medical imaging, including ultrasound. AI algorithms, such as machine learning and deep learning, have shown promise in automating image analysis tasks, detecting anomalies, and providing quantitative assessments.

[0008] However, existing AI-assisted ultrasound systems often focus on narrow, specific applications, such as detecting a single type of pathology or optimizing a particular aspect of the imaging process.

[0009] Furthermore, the development of AI-assisted ultrasound systems faces challenges in terms of data availability, quality, and diversity. Large, well-annotated datasets are essential for training accurate and robust AI models that can generalize across different patient populations and imaging conditions. However, the creation of such datasets is often hindered by privacy concerns, data sharing restrictions, and the need for manual expert annotation.

[0010] There is a need for a comprehensive AI-assisted ultrasound system that can address a wide range of medical applications, from early disease detection and prediction to treatment planning and monitoring. Such a system should be able to integrate state-of-the-art AI algorithms with ultrasound imaging, continuously learn and improve its performance by leveraging large, diverse datasets, and provide healthcare professionals with real-time insights and decision support.

[0011] The present invention addresses these limitations by proposing an AI-assisted ultrasound system that encompasses multiple medical domains and applications, while incorporating advanced AI techniques for improved accuracy, adaptability, and generalizability. By combining the power of AI with the non-invasive and widely accessible modality of ultrasound imaging, this invention aims to revolutionize the field of medical diagnosis, treatment, and monitoring, ultimately leading to improved patient outcomes and enhanced quality of care. SUMMARY

[0012] Ultrasound imaging, and optionally other medical imaging techniques, are utilized to generate predictions and insights for comprehensive medical diagnosis, prognosis, and treatment optimization. These predictions involve analyzing various physiological conditions and biomarkers indicative of a wide range of health issues, offering an innovative method for early detection, proactive care planning, and personalized treatment strategies. 2 9354.06PCT

[0013] Various embodiments include using medical images or the raw data from which images can be produces, e.g., ultrasound images or raw ultrasound data, as inputs to a machine learning system configured to produce quantitative predictions and qualitative assessments regarding numerous medical conditions. These machine learning systems may leverage regression, classification approaches, and / or other algorithms suited for predictive analytics in healthcare. For instance, quantile regression or any suitable regression algorithm that outputs predictions in a range could be employed. In some embodiments, multiple algorithms are integrated within a single AI framework to enhance the predictive accuracy and reliability.

[0014] Preprocessing of images and / or pre-training of machine learning systems are also aspects of various embodiments. Preprocessing is particularly beneficial for dealing with medical images of poor quality, variable quality, or of different sizes, such as those commonly encountered with ultrasound imaging.

[0015] Various embodiments of the invention comprise a comprehensive medical diagnostic system designed to predict, diagnose, and monitor a wide range of health conditions, including: an image storage for retaining medical images showcasing relevant physiological conditions; image analysis logic for generating predictions and insights based on the medical images; a user interface for presenting these predictions to healthcare providers; and a microprocessor to facilitate the execution of image analysis logic. The image analysis logic optionally includes mechanisms for refining predictions based on specific indicators identified in ultrasound images. It will be appreciated that in various embodiments the image storage need nor store images, but can instead store raw data. Where raw data is used in place of images, instead of image analysis logic, analysis logic is employed.

[0016] The methodological aspects of the invention cover generating quantitative or qualitative predictions and assessments of various medical conditions. An exemplary method includes: acquiring a set of medical images or raw data; analyzing these images or raw data with a machine learning system to yield predictions and insights on health issues; and delivering these predictions to healthcare professionals for informed decision-making.

[0017] Training a medical determination system is another key methodological embodiment, involving: collecting a diverse range of medical images; optionally filtering these images for quality and relevance; optionally classifying images based on specific anatomical views or features; optionally pretraining a neural network to recognize distinct image features or types; training the neural network to make quantitative or qualitative 3 9354.06PCTpredictions and assessments of medical conditions; and optionally evaluating the trained network's predictive performance. Such training can be performed analogously using raw data in place of images.

[0018] Acquiring medical images or raw data for neural network training encompasses: creating a comprehensive dataset of medical images or raw data; identifying images or raw data that are particularly indicative of physiological conditions relevant to various health issues; and using these images or raw data to fine-tune a neural network for accurate predictions and insights, enhancing early detection, prognosis, and treatment optimization strategies.

[0019] It will be appreciated that the AI-assisted methodologies described herein can be applied to a wide range of medical conditions and applications, including disease detection, drug use, allergy prediction, early disease diagnosis (diabetes, arthritis, IBD, etc.), pain and symptom measurement, drug selection and dosage optimization, clinical trial optimization, pregnancy and gynecological monitoring, abdominal, cardiovascular, renal, musculoskeletal, thyroid, testicular, breast, vascular, pulmonary, neurological, ophthalmic, skin, pediatric, digestive, respiratory, endocrine, lymphatic, joint, dental, metabolic, rheumatologic, hematologic, oncologic conditions, and continuous monitoring, such as with ultrasound patches.

[0020] Various embodiments of the invention are directed to a system configured to make a medical determination. In these embodiments the system comprises a storage storing images, or raw data, of body parts of a patient and / or a fetus, and analysis logic comprising a trained neural network in communication with the storage and configured to provide a prediction of an Apgar score at 1, 5 and 10 minutes after birth, based on the stored images or raw data. The systems of these embodiments further comprise a user interface configured to provide the determination to a user, and a microprocessor configured to execute at least the image analysis logic.

[0021] Additional embodiments are directed to a method for training a neural network to make a medical prediction. In these embodiments the method comprises a step of receiving a set of images, or raw data, of body parts of a plurality of patients and / or fetuses, where the images or raw data are tagged with Apgar scores at time of birth. The set of images, or the raw data, is then divided into a training set and a validation set, the images or raw data of the training set, and their tags, are provided to a neural network to train the neural network to predict Apgar scores based on the images or raw data of the training set. Then, images, or 4 9354.06PCTraw data, of a single patient from the validation set are provided to the neural network to make a determination of an Apgar score, and the determination is compared to the tag associated with the images or raw data.

[0022] Further embodiments are directed to a method for making a medical determination. In these embodiments the method comprises generating an image, or raw data, of a body part of a patient and / or a fetus, providing the image to a neural network that has been trained to predict an Apgar score from the image or raw data of the body part, and receiving the prediction of the Apgar score.

[0023] It will be appreciated that the systems and methods disclosed herein can be readily adapted to many other medical applications. These include calculating lab values from medical imaging, determining drug use, predicting allergies, early detection of diabetes, prediction of arthritis, early detection of inflammatory bowel disease, objective pain measurement and localization, objective symptom measurement and localization, objective symptom measurement and drug selection therefor, objective medication dosage determination, objective administration route determination for medications, objective symptom measurement and drug administration optimization in clinical trials, prediction of symptom onset and severity, diagnosis, medication selection, and treatment of infections, embryo selection for IVF, personalized ovarian stimulation, monitoring of follicle development, diagnosing infertility, patient selection for IVF, real-time monitoring during IVF, predicting IVF success rates, determining endometrial receptivity, hormone monitoring, determining personalized treatments, diagnosis and treatment of hypertension, diagnosis and treatment of pancreatic cancer, determination of blood type and blood transfusion compatibility, emergency medical triage, surgical planning, organ transplant planning, rehabilitation and physical therapy, predicting surgery complications, determining post- surgery activity resumption, detection of foreign bodies, identifying and predicting blood clots, predicting anesthesia complications, detecting internal bleeding and strokes, detecting nerve damage, detecting dehiscence, detecting hematomas, detecting anemia, detecting renal failure, detecting septicemia, detecting obstructions, detecting hernias, detecting pathogens, predicting failures in clinical trials, medication regimen optimization, making lab test recommendations, frequency optimization, focus optimization, predicting pre-eclampsia for induction timing, determining the effects of a medication, performing virtual biopsies, determining thyroid treatment, determining prostate treatment, and making quantitative predictions of psychiatric disorders. In additional embodiments an ultrasound patch can be 5 9354.06PCTemployed to continuously monitor medication administration, to monitor pain and optimize medication dosage, to continuously monitor, diagnose, prognosticate, and treat, and to create model systems for continuous monitoring of medical changes. BRIEF DESCRIPTION OF DRAWINGS

[0024] FIG.1 illustrates comprehensive medical diagnostic systems designed to predict, diagnose, and monitor a wide range of health conditions using ultrasound and potentially other medical imaging techniques, according to various embodiments of the invention.

[0025] FIG.2 illustrates methods of generating predictive assessments and insights for various medical conditions through the analysis of medical imaging data, according to various embodiments of the invention.

[0026] FIG.3 illustrates methods of training a comprehensive medical diagnostic system to accurately predict, diagnose, and monitor health issues based on physiological conditions discernable in raw data or visible in ultrasound images, according to various embodiments of the invention. DETAILED DESCRIPTION

[0027] The systems and methods disclosed herein offer either quantitative or qualitative assessments of a wide range of medical conditions. Here, a "quantitative determination" includes elements like probability assessments, classifications into various health-based categories, or estimates of disease progression at different time intervals. The advantage of quantitative determination lies in its capacity to provide considerably more actionable information compared to a mere qualitative classification.

[0028] FIG.1 depicts a Medical Determination System 100, tailored for comprehensive medical diagnosis, prognosis, and treatment optimization, in accordance with various embodiments of the invention. Determination System 100 may comprise multiple devices, including an ultrasound system and a computing device geared towards image processing. Optionally, the components of the Determination System 100 communicate with each other and with external devices through a communication network like the Internet.

[0029] Image or raw data processing, aimed at generating determinations, encompasses the generation of estimates pertaining to various medical conditions. These estimates can be presented as absolute or relative probabilities and may incorporate a temporal component. For 6 9354.06PCTinstance, an estimate could signify a 66% likelihood that a patient will have an initially severe condition that will then likely improve with treatment.

[0030] Determination System 100 incorporates an optional Image Generator 110 responsible for producing medical images. Image Generator 110 may include a conventional ultrasound system or another imaging mechanism, configured to deliver images to other components within Determination System 100 for subsequent processing, such as via a computer network. In various embodiments, Image Generator 110 comprises a system that combines an image generation device with one or more elements of Determination System 100. For instance, Image Generator 110 can consist of an ultrasound device equipped with Storage 120, Image Analysis Logic 130, User Interface 150, and Feedback Logic 170, as discussed in greater detail elsewhere in this document. Image Generator 110 may also encompass diverse imaging technologies, such as radiographic (e.g., X-rays), magnetic resonance, nuclear, ultrasound, elastography, photoacoustic, tomography, echocardiography, magnetic particle imaging, spectroscopic (e.g., near-infrared), or similar devices and techniques. It should be noted that in this disclosure, references to ultrasound images are by way of example, and images from any of these other imaging techniques can be readily substituted for ultrasound images in these descriptions. In various embodiments the raw data produced by the Image Generator 110 can be used without having to first process that raw data into one or more images.

[0031] In some embodiments, various components of Determination System 100 are closely integrated with or housed within the Image Generator 110. To illustrate, consider Image Generator 110, which might encompass an ultrasound machine, housing Image Analysis Logic 130. This setup is adept at providing real-time feedback through Feedback Logic 170, effectively guiding the acquisition of ultrasound data and images.

[0032] In some embodiments, Image Generator 110 may include a sound source, a sound detector, and accompanying logic, all orchestrated to generate ultrasound images based on the sounds detected by the sensor. Image Generator 110 is designed to adapt the generation of these ultrasound images based on feedback received from Image Analysis Logic 130. A practical example of this adaptability involves fine-tuning the sound generation, focus, and processing parameters to enhance the detection of specific physiological conditions. Such adjustments respond to cues from Image Analysis Logic 130, which can signal that images or raw data containing such information would yield more accurate determinations and estimates. 7 9354.06PCT

[0033] It's important to note that Image Generator 110 becomes an optional component in scenarios where externally acquired images or raw data are fed into Determination System 100. In embodiments where raw ultrasound (sonogram) data is processed to generate medical assessments, Image Analysis Logic 130 obtains the raw data from the Image Generator 110 and the actual generation of images may be avoided. In some configurations, images and / or raw data are relayed to Determination System 100 via a communication network, such as the Internet. The imagery produced by Image Generator 110 could encompass a sequence illustrating the dynamic movements of various anatomical structures. This sequence, or the raw data behind it, can reveal details like blood flow patterns, tissue perfusion, heartbeat rhythm, and more. It can even incorporate Doppler information, providing insights into the direction and velocity of these movements.

[0034] Determination System 100 also includes Storage 120. This component contains digital memory capabilities for storing an array of data types or raw data types. Its storage capacity can accommodate raw sensor data, medical images, medical records, executable code (logic), neural networks, and other related data. For instance, it can house raw sensor data captured by a photon or acoustic detector, a dataset instrumental in generating images like X-rays or ultrasound scans. Storage 120, as discussed throughout this document, comprises memory circuits and data structures designed to manage and store the mentioned data categories. When dealing with ultrasound images, these images may optionally be configured at 600x600 pixels, optionally encompassing randomly selected crops of 400x400 pixels, which can be used in the training and determinations outlined herein. The term "ultrasound images" in this context may also encompass three-dimensional renderings created from ultrasound data.

[0035] In certain embodiments, Storage 120 is designed with circuits for the storage of raw data or ultrasound images acquired from patients. These can be collected in one or more separate acquisition sessions. For instance, during the first session, a sonographer might gather an initial set of ultrasound images, and subsequently, a second set of images may be procured in a subsequent session taking place at intervals of at least 1, 2, 5, 7, 15, 21, 30, 60, 90, or 365 days, or within any range between these durations. The raw data or ultrasound images for a particular patient may span over a duration encompassing any of the mentioned timeframes. In some instances, a patient could undergo weekly ultrasound sessions. These ultrasound images may encompass Doppler data and even sequences of images (e.g., videos), or the raw data thereof, illustrating the motion of various anatomical structures. Examples 8 9354.06PCTinclude images that reveal heartbeats or blood flow, and they may also incorporate data pertaining to the density of tissues, fluids, or bone. Images or raw data deemed valuable for making medical determinations encompass a broad spectrum, such as those depicting heart rate, liver condition, uterine status, intestinal health, skeletal structure, endometrial features (e.g., thickness and vascularization), kidney function, placental status, adnexa assessment, and so forth.

[0036] Determination System 100 further contains Image Analysis Logic 130. It is useful in providing either quantitative or qualitative predictions regarding various medical conditions, derived from the raw data or ultrasound images and optionally supplemented by clinical data. These predictions come in various formats. For instance, these predictions might incorporate estimates that encompass a wide array of patient-specific factors, such as potential co-morbidities.

[0037] In some embodiments, Image Analysis Logic 130 comprises multiple distinct logic components. Each logic component is designed to predict a specific aspect of a medical condition based on the raw data or ultrasound images. For example, one logic component could analyze ultrasound images to predict the severity of a condition, while another logic focuses on predicting the progression of the condition over time. These logic components can be housed within the same machine learning system for cohesive analysis.

[0038] Image Analysis Logic 130 exhibits adaptability when selecting machine learning algorithms for its calculations. For instance, in some embodiments, Image Analysis Logic 130 is configured to employ a regression algorithm, such as quantile regression, to furnish predictions of various aspects of a medical condition. These regression methods predict a range within which the actual answer is likely to fall, as opposed to a single point value. Any regression system capable of predicting ranges rather than specific values may be employed in Image Analysis Logic 130. This use of a range-based estimation helps to prevent overfitting of the data and proves valuable when the ultrasound images used for training may have mislabeled attributes. Image Analysis Logic 130 typically bases determinations and estimates on sets of ultrasound images or raw data, rather than relying on the analysis of a single ultrasound image or raw data suitable for constructing a single image.

[0039] In alternative embodiments, Image Analysis Logic 130 is programmed to predict various aspects of medical conditions based on classifications utilizing classification algorithms. These classifications can include categories such as "Mild," "Moderate," and "Severe," among others, depending on the specific condition being assessed. When 9 9354.06PCTclassification algorithms are deployed, a "label smoothing" function may be applied. This smoothing function is particularly useful because certain training images or raw data may contain incorrect labels due to human error. The smoothing function may take a specific form, possibly involving epsilon (^) values of 0.05, 0.1, 0.3, or even greater.

[0040] Sample Label Smoothing:

[0041] Instead of using one-hot encoded vector, a noise distribution u(y|x) is introduced. The new ground truth label for data (xi, yi) becomes:This new ground truth label is used as a replacement for the one-hot encoded ground-truth label in a loss function.One can see that for each example in the training dataset, the loss contribution is a mixture of the cross entropy between the one-hot encoded distribution and the predicted distribution and the cross entropy between the noise distribution and the predicted distributionDuring training, if the model learns to predict the distribution confidently,increase dramatically. Therefore, with label smoothing, one introduces a regularizerto prevent the model from predicting too confidently. 10 9354.06PCT

[0042] In some embodiments, label smoothing is used when the loss function is cross entropy, and the model applies the softmax function to the penultimate layer’s logit vectors z to compute its output probabilities p. Label smoothing is a regularization technique for classification problems to prevent the model from predicting the labels too confidently during training and generalizing poorly. See, for example, https: / / leimao.github.io / blog / Label- Smoothing / .

[0043] In some embodiments, both a regression algorithm and a classification algorithm are used to predict various aspects of medical conditions. For example, Image Analysis Logic 130 can include two separate neural networks, one configured to apply the regression algorithm (that outputs a range) and the other configured to apply the classification algorithm. In this case, the classification algorithm may be applied before the regression algorithm and the regression algorithm is optionally applied separately to each class.

[0044] Alternatively, both the regression algorithm and the classification algorithm may be applied by the same neural network. In such embodiments, the neural network is trained to produce both a classification and a regression-based prediction, both of which are quantitative. A regression algorithm outputs one or more values for each percentile chosen. For example, some embodiments use 10%, 25%, 50%, 75%, and 90% percentiles for outputs (which represent percentiles of a quantitative prediction), and each of these percentiles may be associated with a probability and / or a confidence measure. From a set of image inputs or corresponding raw data, the neural network of Image Analysis Logic 130 typically generates one or more values for each percentile chosen. Multiple outputs from distinct algorithms may be used to confirm a prediction of a set of medical condition parameters. This scenario is optionally used to establish confidence in the overall prediction since the regression and classification algorithms should produce the same result.

[0045] Image Analysis Logic 130 may employ other machine learning algorithms or combinations thereof, in addition to or as an alternative to regression and classification. For example, Image Analysis Logic 130 may be configured to apply a regression that outputs an estimated range, a range being more accurate and / or useful than single point predictions. However, single point predictions can be used if many neural networks are generated (each trained on a different subset of the images / raw data) from different subsets of the data, which are then statistically analyzed to form an average and / or distribution. In some embodiments, a Bayesian neural network is used to capture the epistemic uncertainty, which is the uncertainty about the model fitness due to limited training data. Specifically, instead of learning specific 11 9354.06PCTweight (and bias) values in the neural network, the Bayesian approach learns weight distributions, from which it samples to produce an output for a given input, to encode weight uncertainty. Bayesian networks can also be used in a similar fashion in the classification approaches to prediction discussed herein.

[0046] As previously highlighted, Image Analysis Logic 130 can be configured, employing the aforementioned algorithmic and machine learning techniques, to predict various aspects of medical conditions. These predictions are grounded in the comprehensive analysis of ultrasound images or raw data, potentially complemented by additional patient- related factors. For instance, Image Analysis Logic 130 may be tailored to derive the aforementioned evaluations from clinical data. This clinical dataset can encompass a range of variables, such as genetics, body weight, patient medical history, blood glucose levels, heart functionality, kidney performance, blood pressure readings, infection status, nutritional profiles, substance use (including smoking and alcohol consumption habits), patient age, socioeconomic status, living environment, income levels, and even ancestral background. Image Analysis Logic 130 can be strategically configured to accept any singular element from this clinical dataset or combine multiple factors to generate the predictions discussed herein, partly predicated on this clinical information.

[0047] Optionally, Determination System 100 integrates Calculation Logic 140, designed to derive outputs based on the predictions generated by Image Analysis Logic 130. For instance, Calculation Logic 140 can be programmed to compute the severity of a condition, alongside an estimate of its progression over time. Calculation Logic 140 may achieve this by calculating a prediction through the utilization of a probability distribution, represented, for instance, in percentiles. Furthermore, Calculation Logic 140 can ascertain the probability of a particular set of outcomes by employing a distribution function on the estimates provided by Image Analysis Logic 130 and subsequently generating a probability distribution based on this data. In certain embodiments, Image Analysis Logic 130 is equipped to generate specific characteristics associated with this distribution function. For example, in select instances, an estimation of prediction reliability can be factored into the determination, allowing for the calculation of a width parameter (e.g., standard deviation) of the distribution function. It's important to note that Calculation Logic 140 may be integrated within Image Analysis Logic 130, offering an alternative approach to processing and analysis.

[0048] Determination System 100 optionally further includes a User Interface 150 configured to provide to a user estimates and / or determinations made using Image Analysis 12 9354.06PCTLogic 130. User Interface 150 optionally includes a graphical user interface (and the logic associated therewith) and may be displayed on an instance of Image Generator 110, a mobile device (in which case User Interface 150 can include a mobile app) or on a computing device remote from Image Generator 110 and / or Image Analysis Logic 130. For example, User Interface 150 may be configured to display predictions and insights related to various medical conditions. In some embodiments, User Interface 150 is configured for a remote user to upload one or more ultrasound images or raw data for processing by Image Analysis Logic 130.

[0049] As is discussed further herein, in some embodiments, User Interface 150 is configured to provide feedback to a user in real-time. For example, User Interface 150 may be used to give instructions to an ultrasound technician during an ultrasound session, to generate raw data or images which result in a better prediction of a medical condition.

[0050] Determination System 100 optionally further includes a Data Input 160 configured to receive data regarding a patient, e.g., clinical data regarding the patient. Data Input 160 is optionally configured to receive any of the clinical data discussed herein, which may be used by Image Analysis Logic 130 to generate the estimates and / or probabilities discussed herein. For example, this data can include any of the clinical data discussed herein, or inputs from a user of Image Generator 110. In some embodiments, Data Input 160 is configured to receive medical images, such as ultrasound images, or receive the corresponding raw data, from remote sources.

[0051] Determination System 100 optionally further includes Feedback Logic 170. Feedback Logic 170 is configured to guide acquisition of ultrasound images or raw data based on a quality of the estimate and / or determinations related to the patient. For example, if analysis of ultrasound images, using Image Analysis Logic 130, obtained during an imaging session, results in determinations and / or estimates having inadequate precision, accuracy, and / or reliability, then Feedback Logic 170 may use User Interface 150 to inform a user that additional ultrasound raw data or images are desirable.

[0052] Further, in some embodiments, feedback logic is configured to direct a user to obtain ultrasound raw data or images of specific features such as motion of a heartbeat, heart rate, the liver, the kidneys, blood flow, bone development, spine, tissue perfusion, uterus, ovaries, testicles, femur, humerus, endometrium, endometrium vascularization, the adnexa, and / or the like. In some embodiments, Image Analysis Logic 130 is configured to classify ultrasound raw data or images according to subject matter and / or objects included within the 13 9354.06PCTimages or raw data. For example, separate subject matter classes may include any of the views and / or features discussed herein. In such embodiments, Image Analysis Logic 130 may be configured to identify objects in the ultrasound images or raw data and determine that there are sufficient quality images or data of objects in each subject matter classification. (Subject matter classification is not to be confused with classification of ultrasound images relating to medical conditions.) If there are not sufficient raw data or images, then the User Interface 150 may be used to request that the operator of Image Generator 110 obtain additional images or raw data including the additional objects. Feedback Logic 170, thus, may be configured to indicate a need to acquire additional ultrasound images or raw data useful in the prediction of various medical conditions. In a specific example, Image Analysis Logic 130 may be configured to request at least one set of images indicative of a specific condition. In some instances, Feedback Logic 170 is configured to guide the positioning of the image generator (e.g., an ultrasound head) so as to generate images or raw data that are more useful in the prediction of medical conditions. Such guidance may include positioning of an ultrasound probe in a specific position or a written / audio request such as "obtain images showing the liver."

[0053] In various embodiments, Feedback Logic 170 is configured to guide or request acquisition of new images or raw data that would be beneficial to training future models to obtain greater accuracy.

[0054] Determination System 100 optionally further includes Training Logic 180. Training Logic 180 is configured for training Image Analysis Logic 130, Feedback Logic 170, Image Acquisition Logic 190, and / or any other machine learning system discussed herein. Such training is typically directed at the end goal of learning to make quantitative determinations and / or estimates relating to various medical conditions. For example, Training Logic 180 may be configured to train Image Analysis Logic 130 to make a quantitative prediction of the severity of a condition. As described elsewhere herein, this determination may be made using both a quantile regression algorithm and a classification algorithm, together or separately.

[0055] While Training Logic 180 may use any applicable selections of the commonly known neural network training algorithms, Training Logic 180 optionally includes a variety of improvements to better train the neural networks disclosed herein. For example, in some embodiments Training Logic 180 is configured to pretrain a neural network of Image Analysis Logic 130 to better recognize features in ultrasound images or raw data. This 14 9354.06PCTpretraining can include training on images with varying orientation, contrast, resolution, point of view, etc., and can be directed at recognizing anatomical features within the ultrasound images. Pretraining is optionally performed using unlabeled data.

[0056] In some embodiments, Training Logic 180 is configured to generate additional training images or raw data, in cases where training images or raw data for a specific condition are sparse or infrequent. For example, once it is known which images and features are most predictive, Training Logic 180 can take subsets of the images, and use a GAN (Generative Adversarial Network) to generate new training images including the features found in rare clinical situations.

[0057] In some embodiments, Training Logic 180 is configured to train on multiple sets of images or corresponding raw data, the images optionally being from different patients. By training on multiple sets of images, rather than on single images, overfitting of the data can be reduced. Preferably, each of the sets is large enough to assure that there are at least some images, or corresponding raw data within the set including information useful for making the predictions discussed herein.

[0058] In some embodiments, Training Logic 180 is configured to train Image Analysis Logic 130 to enhance images. For example, Image Analysis Logic 130 may be pretrained to enhance poor quality ultrasound images, or to reveal features, such as tissue perfusion and / or vascularization, that would not normally be visible in the ultrasound images being processed. Such enhancement may allow the use of a handheld ultrasound image to generate the images processed to make predictions of various medical conditions.

[0059] In some embodiments, Training Logic 180 is configured to train neural networks in multiple stages, e.g., as in transfer learning. For example, a neural network may first be trained to recognize relevant patient features, then be trained to predict categories of medical conditions, and then be trained to provide a quantitative prediction of the severity or progression of those conditions.

[0060] Determination System 100 typically further includes a Microprocessor 195 configured to execute some or all of the logic described herein. For example, Microprocessor 195 may be configured to execute parts of Image Analysis Logic 130, Calculation Logic 140, Feedback Logic 170, Training Logic 180, and / or Image Acquisition Logic 190. Microprocessor 195 may include circuits and / or optical components configured to perform these functions. 15 9354.06PCT

[0061] FIG.2 illustrates methods of making a quantitative (optionally medical) prediction, according to various embodiments of the invention.

[0062] In an Obtain Images Step 210, a set of one or more images, or corresponding raw data, are obtained. These images or data are typically related to a specific patient. These images or data may be obtained from a source external to Determination System 100 or may be obtained using Image Generator 110. For example, in some embodiments, ultrasound images are uploaded to Storage 120 via a computer network such as the internet. Images may be received from an electronic medical records system. In other embodiments, images are generated using a medical imaging system such as any of those discussed herein. The images or raw data are optionally stored in Storage 120. The images can include any combination of the views and / or features discussed herein and are optionally classified based on their respective views and features (subject matter classification).

[0063] In an optional Receive Data Step 220, additional clinical data regarding the patient is received. Again, this data may be received from an electronic medical records system, provided by the patient, and / or provided by a caregiver. The received clinical data can include any of the clinical data discussed herein and is optionally received via Data Input 160.

[0064] In an Analyze Images Step 230 the images or raw data obtained in Obtain Images Step 210 are analyzed using Image Analysis Logic 130. The images or data are analyzed to produce one or more quantitative or qualitative predictions. For example, a quantitative prediction can include an estimate of the severity or progression of a medical condition. The determination is a "quantitative determination" as defined elsewhere herein. In addition to the images or raw data, the quantitative prediction is optionally further based on the clinical data received in Receive Data Step 220. The methods of analysis in Analyze Images Step 230 can include any combination of the algorithms and / or machine learning systems discussed elsewhere herein, including those discussed with reference to Image Analysis Logic 130. For example, analyzing medical images can include using a quantile regression algorithm and / or a classification algorithm to make a quantitative determination relating to a medical condition. In another example, analyzing the medical images includes using a regression algorithm to provide a prediction of the severity or progression of a condition.

[0065] In an optional Provide Feedback Step 240, a user (e.g., a caregiver, or possibly the patient) is provided with feedback regarding acquisition of the images or raw data. This feedback can be based on, for example, a quality of the quantitative determination and / or a classification of images or data already acquired. In specific examples, during an ultrasound 16 9354.06PCTsession, a caregiver may be asked to acquire additional images of different resolution, of different views, of different features, and / or the like. Obtain Images Step 210 and Analyze Images Step 230 are optionally repeated following Provide Feedback Step 240.

[0066] In a Provide Determination Step 250 the quantitative or qualitative determination(s) generated in Analyze Images Step 230 is provided to a user, e.g., a patient or caregiver. The predictions are optionally also placed in Storage 120 and / or an electronic medical records (EMR) system. In various embodiments, the predictions are provided via a web interface, via the EMR system, via a mobile application, on a display of Image Generator 110, on a display of a computing device, and / or the like.

[0067] FIG.3 illustrates methods of training a medical prediction system, according to various embodiments of the invention. The methods illustrated by FIG.3 are optionally used to train Image Analysis Logic 130, Feedback Logic 170, and / or Image Acquisition Logic 190. The methods may be performed using Training Logic 180.

[0068] In a Receive Images Step 310, a plurality of medical images or corresponding raw data are received as a training set. The received medical images optionally include ultrasound images or raw data of a patient before or after an intervention to establish the effect on the prediction of various medical conditions. The received images or data are optionally stored in Storage 120.

[0069] In an optional Classify Step 320, the received images or raw data are classified according to the views or features included within the images. For example, an image may be classified as showing the heart or classified as showing the liver. Classify Step 320 is optionally performed by a neural network included in Image Analysis Logic 130 and / or trained by Training Logic 180. Classify Step 320 may also include classifying images or raw data according to (actual or estimated) the various anatomy that may have differing impacts in a particular medical condition prediction. For example, images may be classified as having been generated during a follow up of specific anatomy. It should be noted that as used herein, "neural network" specifically excludes human brains.

[0070] In an optional Filter Step 330, the received images or raw data are filtered. Filtering can include removing images that lack features or views that have been determined to have little or no determinative value. For example, a class of images of a bladder may be determined to have little value in determining a quantitative or qualitative determination, and this class of images may be removed from the training set. Images or raw data may also be filtered according to their quality or resolution, etc. 17 9354.06PCT

[0071] In some embodiments, Filter Step 330 includes balancing of a number of images or raw data in various classes. For example, for training purposes, it may be desirable to have roughly equal numbers of rare medical conditions, unusual clinical circumstances, or uncommon hardware. Specifically, balancing may be used to adjust quantities of the images or raw data based on ground truth medical diagnoses. Likewise, for training purposes, it may be desirable to balance numbers of images within the training set based on classification of views and / or features as determined in Classify Step 320.

[0072] In an optional Pretrain Step 340, the neural network is optionally pretrained to recognize features within the images or types of images. For example, a neural network within Image Analysis Logic 130 may be pretrained to recognize features in ultrasound images of varying orientation, resolution, and / or quality.

[0073] In a Train Step 350, the neural network is trained to provide a quantitative or qualitative determination regarding predicted medical conditions.

[0074] In an optional Test Step 360, predictions made by the neural network trained in Train Step 350 are tested using test images or raw data. This testing may be performed to determine the accuracy and / or precision of the quantitative or qualitative determinations generated by the neural network.

[0075] The quantitative or qualitative predictions of medical conditions are optionally used to selectively select a population for a clinical trial. For example, assuming that a certain rare condition occurs in less than 1% of patients, it would be inefficient to give a general population of patients a candidate therapy in order to detect a benefit to the unidentified 1% of patients. However, by identifying the 1% of the patients most likely to develop this condition, a study can be made of the benefit seen within this population for a candidate therapy. Such a study is much more efficient and more likely to reveal benefits with better statistical relevance. The systems and methods disclosed herein may be used to identify such preferred populations for clinical studies. This approach is particularly beneficial for conditions that begin development (and could benefit from therapy) well before clear symptoms appear.

[0076] The various methods illustrated in FIGS.2 and 3 are optionally used in combination and are optionally performed using the systems illustrated by FIG.1. For example, the methods of FIGs.2 and 3 may be used together.

[0077] It should be understood that the present invention is not limited to only the prediction of specific medical conditions; methods described herein can be used for 18 9354.06PCTidentifying a wide range of diseases and medical conditions that may happen in the future. Those of ordinary skill will be able to identify still other conditions that AI can be trained to determine future medical events or conditions. Example

[0078] The following is an illustrative example, which may be included in any of the embodiments discussed herein:

[0079] All ultrasounds from any patient are used to train the neural networks discussed herein. However, different models can be created for predicting Apgar scores at different time intervals.

[0080] Note: The following models are simply individual embodiments that show how to create a working model. Many of the hyperparameters such as the learning rate, batch size, and number of epochs can be adjusted without issue.

[0081] A machine learning algorithm (the code), set forth below, was created and used to train a neural network to make predictions of Apgar scores at 1, 5, and 10 minutes after birth, based on ultrasound images acquired from the patient. Both transformer and CNN architectures were successfully used. The training was conducted using a dataset of ultrasound images, totaling 276,577 images labeled with the Apgar score at time of birth. The dataset representing 14,302 individual ultrasound sessions. The images were acquired of female reproductive anatomy and fetal anatomy. The dataset was split into a training set and a validation set. Ten percent of the studies were selected randomly to comprise the validation set, the remaining images comprised the training set. Next, using the labeled data in the training set, the algorithm gradually learned to extract relevant features from these images, such as anatomical structures that may be different depending on different Apgar scores, but not necessarily visible to the human eye. For Apgar scores, the relevant anatomical structures were found to be primarily the fetal anatomy for normal deliveries and the anatomy of the mother in cases where the Apgar score is driven primarily by a preterm birth situation. During the training session the algorithm was repeatedly tested against the validation set which does not participate in the training. The algorithm's performance was evaluated on the validation set of ultrasound images that had not been used for training, and the algorithm’s predictions were compared against the actual labels. The training was halted once the validation accuracy began to decrease after reaching a peak accuracy, indicating the algorithm was likely no longer learning generalized features, but more likely memorizing 19 9354.06PCTfeatures of the training set. The algorithm proved accurate on the validation data, yielding an accuracy of 85%.

[0082] Import all the libraries used in the code

[0083] from fastai.vision.all import *

[0084] Any imaging study where the Apgar score was between 0-3 can be labeled “Low”, and “Moderately Abnormal” for 4-6, and “Reassuring” for 7-10. However, this labeling is not strictly necessary; other classification labels may be used.

[0085] The following path is a folder containing one subfolder for each class to be predicted which are “Low”, “Moderately Abnormal”, and “Reassuring”. This is only one embodiment; any number of different classifications would also work. For example, there may be two, three, four or more classes. For example “No Response to Stimuli” could be added.

[0086] A score of Low is about 5% of the total dataset used, however training may be more effective when using a balanced dataset which is the case here. The validation set is not balanced in this embodiment which reflects the accuracy of the distribution in the real world, but could be balanced in other embodiments. In some embodiments the neural network has an equal chance of being fed an instance of one of the categories. In various embodiments, at least 50% or 75% of the training set includes images balanced among the possible classes.

[0087] A large dataset containing over 276,577 million ultrasound images with annotations about the Apgar scores was used for the training and validation of this invention. Optionally, the image size can be set to 400 by 400 pixels. Most of the images in this dataset are several times this size and are reduced in size for training and inference efficiency. Accuracy can increase with increasing image size, however there can be diminishing returns. Alternative embodiments use images of at least 224×224 pixels, 640×640 pixels, or 2048×2048 pixels, or any range therebetween.

[0088] This will create an object to feed data to the neural network during training and validation. In this example, 10% of the studies are put into a validation set which is used to monitor training. The validation set contains the same distribution of Apgar scores that exists in the original dataset. The data is balanced for training; however, the validation set is the natural distribution and not balanced in any way. The batch size is arbitrarily set to 48 images but can be adjusted if necessary. Adding the function aug_transforms causes each image to be randomly augmented which can reduce overfitting. Examples of augmentation include but are not limited to adjusting brightness, contrast, and flipping the image horizontally. This 20 9354.06PCTembodiment uses Cross Entropy as the loss function and it is training as a label classification problem where there is exactly one label for each image. Other embodiments could use other loss functions such as mean squared error if the data is viewed as a regression problem.

[0089] from fastai.vision.all import * # Define data transformations tfms = aug_transforms() image_size = 400 # Get image file names from the specified path fnames = get_image_files(path) # Create a DataBlock for label classification dblock = DataBlock( blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=Pipeline([parent_label]), splitter=GrandparentSplitter(), item_tfms=Resize(image_size), batch_tfms=[*tfms]) # Create DataLoader dls = dblock.dataloaders(path, bs=48)

[0090] Create an object which will control training and inference at a high level. Using a high value for weight decay is one example. Other forms of regularization of the weight values may have similar effects.

[0091] Obtain a pretrained network for use in transfer learning, however training a neural network from an initial randomized form will also work. In this case ResNet-152 is used. Other variations of resnet will work and the more layers the better the accuracy. Many other neural networks will also give usable results. The example below illustrates an example of training. The parameters and steps may be varied in alternative embodiments.

[0092] Create a learner object with a CNN architecture (however vision transformers may also be used) learn = cnn_learner(dls,

[0093] resnet152,

[0094] cbs=[ShowGraphCallback()],

[0095] wd=0.1, 21 9354.06PCT

[0096] metrics=[accuracy],

[0097] loss_func=CrossEntropyWithLogitsLossFlat())

[0098] This will freeze the parameters of the convolutional part of the neural network and will allow training of just the linear layers. Other embodiments will not necessarily require the step of freezing layers. learn.freeze( )

[0099] Train the neural network for 10 epochs while gradually increasing then decreasing the learning rate with a scheduler. The maximum learning rate will be 1e-3. Other embodiments may use an alternative training schedule. learn.fit_one_cycle(10, 1e-3)

[0100] This will allow the entire neural network to be trained including the convolutional layers. learn.unfreeze( )

[0101] Further train the neural network for 5 epochs while gradually increasing then decreasing the learning rate with a scheduler. The maximum learning rate will be 1e-5. learn.fit_one_cycle(5, 1e-5)

[0102] Further train the neural network for 5 epochs while gradually increasing then decreasing the learning rate with a scheduler. The maximum learning rate will be 1e-5. learn.fit_one_cycle(5, 1e-5)

[0103] Best validation accuracy of this embodiment is 85%.

[0104] This network allows for the identification of the specific anatomical views of the study that are most useful for making this prediction. Specific anatomical views or any other information fed to the network can be excluded if it is commonly uninformative, or the various views can be weighted in calculating the final determination.

[0105] Each determination outputs a score of how confident the model is which can be used in real time on the ultrasound machine to inform the ultrasound technician when a view which will improve accuracy has been obtained, or the software can automatically capture high confidence views as they are found with no effort from the technician required. This technique can be used to create training sets for continuous improvements in updated models which is a feedback loop for capturing better data for training future models.

[0106] The trained system is optionally used to provide an ultrasound technician with real-time feedback. For example, the system may inform the technician when images expected to be the most predictive have or have not been obtained. The system can request 22 9354.06PCTthat the technician obtain images or raw data of specific anatomy of the patient and / or fetus. When an obtained image or raw data is identified as being predictive of the Apgar score, the system may request that the technician obtain additional images or raw data to confirm or negate the prediction. For example, if an image of one part of the anatomy (e.g., a heart) confidently predicts an Apgar score the system may request that the technician obtain further images of that anatomy or further images of another part of the patient's anatomy (e.g., cervix or uterus).

[0107] Making determinations based on individual ultrasounds and then aggregating these individual determinations can be useful in itself, but the method can also be improved upon. One concern with this method is that within a single ultrasound recording session some ultrasound images or data may predict one Apgar score while ultrasound images or raw data of another part of the anatomy will predict a different Apgar score, making a simple aggregation less effective. Accordingly, neural networks have been created that allow multiple images or corresponding raw data to be classified in a single pass through a neural network or sequence of networks. As the number of images or quantity of raw data simultaneously fed into a neural network increases, the accuracy also typically increases. Therefore, more efficient methods of passing (in parallel, or sequentially) multiple images or data through a single network (or sequence of networks) were developed.

[0108] This does not necessarily have to be a classification problem. The values to determine could be numeric values representing the desired target and a neural network to perform a regression is created instead.

[0109] Training data can be created by training a neural network to modify images or raw data from one class into another if the data is limited for a particular class. An example of a neural network which can convert images from one class to another is a cyclegan.

[0110] As provided above, it is not necessary for images to be used. The underlying raw data in the form of sound waves captured by the ultrasound machine before conversion into an image can also be used as an alternative to or in addition to images for this determination. Transformer Aggregation Architecture

[0111] A neural network can be created which can extract the useful information from each image or corresponding raw data which is then aggregated using multiple neural networks combined into a single neural network using a transformer style architecture, however these networks could be separate in other embodiments. An ultrasound session is one in which a technician takes a set of images or raw data in one interaction with a patient. 23 9354.06PCTConsidering that many ultrasound sessions exceed 100 images, this is useful for processing efficiency and accuracy. Transformers can be applied to sequential (or time series) data where data points are correlated in a sequence. The ultrasound images or raw data in an ultrasound session don't have a significant amount of order to them, if any, however this type of network can carry information from a previous step of processing a single image forward and this combined information can be classified after each image in an ultrasound session is processed. Ultrasound sessions do not have a significant amount of order because an ultrasound technician typically jumps from viewing one anatomical feature to another anatomical feature. It is possible to record a video of the entire session and in these embodiments the images in the video have a sequential nature and this general technique can be employed. from fastai.vision.all import * from pathlib import Path

[0112] Get the data which is divided into training, validation, and test sets. The training data is created in an unusual way. Instead of just using each study as a separate folder of images, folders are created which randomly sample a range of images from pools of all studies with the same Apgar score. It is also possible to sample images from a pool of each class of the desired sequence length at run time. Other embodiments use 2, 3 or more classes or numeric values. There is one main reason to do this.

[0113] The training data can contain a significant number of mislabeled studies. By taking a random selection it makes it possible for each training sample to have some ultrasounds which contain the needed information to make a correct determination and to prevent overfitting of incorrectly labeled studies.

[0114] It should also be noted that multiple ultrasound sessions are commonly performed on a single pregnancy. Multiple sessions from a single pregnancy could also be combined when performing a prediction of Apgar scores.

[0115] A single session may indicate a predicted low Apgar score but may not have a high confidence. Therefore, the system may indicate that a follow up session is desired and potentially when the follow up session should be conducted. Data Acquisition

[0116] To obtain the paths to folders containing images from individual studies, the following function can be used: def get_sequence_paths(path): 24 9354.06PCTsequence_paths = [] for folder in path.ls(): for c in folder.ls(): sequence_paths += c.ls() return sequence_paths folders = get_sequence_paths(IMAGES_PATH)

[0117] In this example, the sequence length is set to 36 images, but a smaller or larger range can be used. More images may improve results, but there can be diminishing returns. The image size used here is 400x400 pixels, which is larger than the typical 224x224 pixels used in image classification problems. However, smaller or larger images can also be utilized: seq_len = 36 image_size = 400

[0118] A custom ImageTuple class is defined to handle image sequences and display them: class ImageTuple(Tuple): def show(self, ctx=None, **kwargs): n = len(self) img0, img1, img2 = self[0], self[n / / 2], self[n-2] if not isinstance(img1, Tensor): t0, t1, t2 = tensor(img0), tensor(img1), tensor(img2) t0, t1, t2 = t0.permute(2, 0, 1), t1.permute(2, 0, 1), t2.permute(2, 0, 1) else: t0, t1, t2 = img0, img1, img2 return show_image(torch.cat([t0, t1, t2], dim=2), ctx=ctx, **kwargs)

[0119] An ImageTupleTransform is also created to encode images into tuples for our dataset: class ImageTupleTransform(Transform): def __init__(self, seq_len=36): self.seq_len = seq_len def encodes(self, path): images = path.ls() 25 9354.06PCTreturn ImageTuple(tuple(PILImage.create(f) for f in L(random.choices(list(images), k=self.seq_len)) if os.path.isfile(f))) tfms = aug_transforms(flip_vert=False) grandparent_splitter = GrandparentSplitter()(files) itfm = ImageTupleTransform(seq_len=seq_len) ds = Datasets(files, tfms=[[itfm], [parent_label, Categorize]], splits=grandparent_splitter) dls = ds.dataloaders(bs=bs, after_item=[Resize(image_size), ToTensor], after_batch=[*tfms, IntToFloatTensor], drop_last=True) Encoder Class

[0120] The Encoder class loads a model pretrained on single ultrasound images, removes the final classification layer, and returns 512 features for each image: class Encoder(Module): def __init__(self, **kwargs): model = load_learner("davit_large").model self.body = model[0] self.head = model[1][:-4] def forward(self, x): return self.head(self.body(x))

[0121] This class allows features to be extracted from images using a pretrained model. The number of features can be adjusted as needed for specific use cases.

[0122] The following module takes the features from each image and classifies the entire sequence of images. import torch import torch.nn as nn import torch.nn.functional as F class TransformerEncoder(Module): def __init__(self, num_classes=2, d_model=512, nhead=8, num_encoder_layers=6, dim_feedforward=2048, dropout=0.1): self.transformer_encoder = nn.TransformerEncoder( nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout), num_encoder_layers ) self.fc = nn.Sequential( 26 9354.06PCTnn.Linear(d_model, num_classes), ) def forward(self, x): x = self.transformer_encoder(x) x = x.mean(dim=1) # Pooling operation, you can replace this with another method if needed x = self.fc(x) return x Example usage: dls_c = 2 model = TransformerEncoder(num_classes=dls_c, d_model=512, nhead=8, num_encoder_layers=6, dim_feedforward=2048, dropout=0.1)

[0123] Create a Learner object which will control training and inference at a high level. Using a high value for weight decay is helpful in making a correct determination.

[0124] GradientAccumulation can be used if GPU memory constraints require it. This will accumulate gradients for 32 items before making an update to the weights of the network. import torch.nn as nn from fastai.learner import cnn_learner, Learner from fastai.vision.all import ShowGraphCallback, GradientAccumulation, SaveModelCallback class TransformerModel(nn.Module): def __init__(self, num_classes, d_model=512, nhead=8, num_encoder_layers=6, dim_feedforward=2048, dropout=0.1): # transformer architecture here def forward(self, x): # Forward pass through the transformer # Custom splitter function to separate transformer encoder from other layers def transformer_splitter(model): transformer_layers = [] other_layers = [] for name, param in model.named_parameters(): if 'transformer_encoder' in name: 27 9354.06PCTtransformer_layers.append(param) else: other_layers.append(param) return [transformer_layers, other_layers] # Create your data loaders (dls) here # Replace 'dls' with the actual data loaders # Instantiate your TransformerModel model = TransformerModel(num_classes=dls.c) # Create a Learner with the model and custom splitter learn = Learner(dls, model, wd=0.3, metrics=[accuracy], cbs=[ShowGraphCallback(), GradientAccumulation(), SaveModelCallback(monitor='valid_loss', fname='Transformer_Model')], splitter=transformer_splitter).to_fp16()

[0125] Train the neural network for 5 epochs while gradually increasing then decreasing the learning rate with a scheduler. The maximum learning rate will be 1e-3. These parameters are simply chosen for this embodiment, many other choices would also work. learn. freeze( ) learn.fit_one_cycle(5, 1e-3) Alternative Embodiments:

[0126] To aggregate the individual determinations for a study run each through a tournament to determine final classification.

[0127] Reinforcement learning can be used in place of the transformer shown.

[0128] Use multiple models trained on different subsets of the data to create ensembles which typically increases accuracy.

[0129] Use additional data about the patient in combination with the images or raw data such as age or relevant events in the medical history of a patient.

[0130] Additional outputs in addition to the predicted medical condition can be used. Some examples are age, comorbidities, sex, among others. Having multiple outputs for determination can improve the overall accuracy of the determination due to the inherent relationships between the various items being determined. Any of these alternative determinations can be performed without the primary condition prediction if desired. 28 9354.06PCT

[0131] The systems and methods disclosed herein have been applied to real world data obtained from a clinical context and have been shown to consistently produce an accuracy of at least 85% for predicting various medical conditions.

[0132] Several embodiments are specifically illustrated and / or described herein. However, it will be appreciated that modifications and variations are covered by the above teachings and within the scope of the appended claims without departing from the spirit and intended scope thereof. For example, while ultrasound images and raw ultrasound data are taught herein by way of example, the systems and methods described herein may be applied to other medical imaging modalities and conditions. Examples include disease detection, drug use, allergy prediction, early disease diagnosis (diabetes, arthritis, IBD, etc.), pain and symptom measurement, drug selection and dosage optimization, clinical trial optimization, pregnancy and gynecological monitoring, abdominal, cardiovascular, renal, musculoskeletal, thyroid, testicular, breast, vascular, pulmonary, neurological, ophthalmic, skin, pediatric, digestive, respiratory, endocrine, lymphatic, joint, dental, metabolic, rheumatologic, hematologic, oncologic conditions, and / or any other medical condition the precursors of which may be present in a medical image or the underlying raw data. The methods and systems disclosed may be used to determine a current clinical state separately or in combination with the (optionally quantitative or qualitative) determination of a future state. The systems and methods disclosed herein may also be used to predict future health conditions of a patient. For example, the aforementioned conditions and applications could be predicted. It will be understood that the terms "predict" and "determine" are used interchangeably herein.

[0133] While the teachings herein include use of medical images, e.g., ultrasound images, in various embodiments, the systems and methods may use raw data other than in the form of images. For example, Image Analysis Logic 130 is optionally trained to process raw ultrasound data rather than or in addition to images generated from such data.

[0134] The embodiments discussed herein are illustrative of the present invention. As these embodiments of the present invention are described with reference to illustrations, various modifications or adaptations of the methods and or specific structures described may become apparent to those skilled in the art. All such modifications, adaptations, or variations that rely upon the teachings of the present invention, and through which these teachings have advanced the art, are considered to be within the spirit and scope of the present invention. 29 9354.06PCTHence, these descriptions and drawings should not be considered in a limiting sense, as it is understood that the present invention is in no way limited to only the embodiments illustrated.

[0135] The "logic" discussed herein is explicitly defined to include hardware, firmware or software stored on a non-transient computer readable medium, or any combinations thereof. This logic may be implemented in an electronic and / or digital device to produce a special purpose computing system. Any of the systems discussed herein optionally include a microprocessor, including electronic and / or optical circuits, configured to execute any combination of the logic discussed herein. The methods discussed herein optionally include execution of the logic by said microprocessor.

[0136] Computing systems and / or logic referred to herein can comprise an integrated circuit, a microprocessor, a personal computer, a server, a distributed computing system, a communication device, a network device, or the like, and various combinations of the same. A computing system or logic may also comprise volatile and / or non-volatile memory such as random-access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), magnetic media, optical media, nano-media, a hard drive, a compact disk, a digital versatile disc (DVD), optical circuits, and / or other devices configured for storing analog or digital information, such as in a database. A computer-readable medium, as used herein, expressly excludes paper. Computer-implemented steps of the methods noted herein can comprise a set of instructions stored on a computer readable medium that when executed cause the computing system to perform the steps. A computing system programmed to perform particular functions pursuant to instructions from program software is a special purpose computing system for performing those particular functions. Data that is manipulated by a special purpose computing system while performing those particular functions is at least electronically saved in buffers of the computing system, physically changing the special purpose computing system from one state to the next with each change to the stored data. Determination or Prediction of Allergies from Ultrasound Images Using AI A system and method for predicting allergies from ultrasound images using AI are disclosed. The system includes a database of ultrasound images from patients with and without various allergies, and an AI algorithm that analyzes the images to predict the likelihood of allergies. The algorithm is trained using machine learning techniques and can predict allergies with high accuracy. The system bas wide applications in the medical field, including the identification and treatment of allergies. 30 9354.06PCTThe present invention relates to the field of medical diagnosis and treatment. Specifically, the invention relates to a system and method for predicting allergies from ultrasound images using AI. Allergies are a common medical condition that affects millions of people worldwide. Identifying and treating allergies is crucial for the overall health and well-being of patients. However, current methods for identifying allergies are often time-consuming and invasive. The present invention provides a system and method for predicting allergies from ultrasound images using AI. The system includes a database of ultrasound images from patients with and without various allergies, and an AI algorithm that analyzes the images to predict the likelihood of allergies. The algorithm is trained using machine learning techniques and can predict allergies with high accuracy. The system has wide applications in the medical field, including the identification and treatment of allergies. To collect the data to train the AI described in this patent, the following process can be followed: Identify and recruit patients: Patients with and without various allergies need to be recruited to obtain ultrasound images. Patients with allergies should be selected based on their known allergy history or positive results on allergy tests, while patients without allergies should be selected based on their negative allergy history and test results. Obtain consent: All patients must provide informed consent before participating in the study. TI1e consent form should explain the purpose of the study, the types of data to be collected, and the potential risks and benefits of participating. Collect ultrasound images: Ultrasound images of the relevant body parts (e.g., skin, lungs) should be collected from both groups of patients using standard ultrasound equipment and techniques. The images should be of high quality and include various angles and views to capture as much detail as possible. Collect patient data: In addition to ultrasound images, patient data should also be collected, including demographic inforn1ation, allergy history, and allergy test results. This information can be used to identify the patients with and without allergies and to evaluate the accuracy of the Al algorithm. 31 9354.06PCTLabel and annotate the images: Each ultrasound image should be labeled and annotated to indicate whether the patient has allergies or not. This information will be used to train the AI algorithm to predict the likelihood of allergies. Train the AI algorithm: The labeled and annotated ultrasound images should be used to train the AI algorithm using machine learning techniques. The algorithm should be optimized to predict the likelihood of allergies with high accuracy. Test the AI algorithm: Once the AI algorithm has been trained, it should be tested on a separate set of ultrasound images to evaluate its accuracy. The predicted likelihood of allergies should be compared with the ground truth (i.e., patient data) to determine the algorithm's accuracy. Provide treatment options: Based on the predicted likelihood of allergies, treatment options can be provided to patients. For example, patients with high likelihood of allergies may be prescribed allergy medications or advised to avoid allergens. Update the AI algorithm: As new ultrasound images and patient data become available, the AI algorithm should be updated and retrained to improve its accuracy over time. To collect the data to train the AI, the following process can be followed: Identify and recruit patients: Patients with and without various allergies need to be recruited to obtain ultrasound images. Patients with allergies should be selected based on their known allergy history or positive results on allergy tests while patients without allergies should be selected based on their negative allergy history and test results. Obtain consent: All patients must provide informed consent before participating in the study. The consent form should explain the purpose of the study, the types of data to be collected, and the potential risks and benefits of participating. Collect ultrasound images: Ultrasound images of the relevant body parts (e.g., skin, lungs) should be collected from both groups of patients using standard ultrasound equipment and techniques. The images should be of high quality and include various angles and views to capture as much detail as possible. Collect patient data: In addition to ultrasound images, patient data should also be collected, including demographic information, allergy history, and allergy test results. This information can be used to identify the patients with and without allergies and to evaluate the accuracy of the AI algorithm. Label and annotate the images: Each ultrasound image should be labeled and annotated to indicate whether the patient has allergies or not. This information will be 32 9354.06PCTused to train the AI algorithm to predict the likelihood of allergies. Train the AI algorithm: The labeled and annotated ultrasound images should be used to train the AI algorithm using machine learning techniques. The algorithm should be optimized to predict the likelihood of allergies with high accuracy. Test the AI algorithm: Once the AI algorithm has been trained, it should be tested on a separate set of ultrasound images to evaluate its accuracy. The predicted likelihood of allergies should be compared with the ground truth (i.e., patient data) to determine the algorithm's accuracy. Provide treatment options: Based on the predicted likelihood of allergies, treatment options can be provided to patients. For example, patients with high likelihood of allergies may be prescribed allergy medications or advised to avoid allergens. Update the AI algorithm: As new ultrasound images and patient data become available, the AI algorithm should be updated and retrained to improve its accuracy over time. A method for predicting allergies from ultrasound images comprising: a. Collecting ultrasound images from patients with and without various allergies b. Training an Al algorithm using machine learning techniques to analyze the images and predict the likelihood of allergies c. Applying the AI algorithm to ultrasound images of patients to predict the likelihood of allergies. The method of 1, wherein the AI algorithm is trained using a convolutional neural network. The method of 1, wherein the ultrasound images are collected using a high- frequency transducer. The method of 1, further comprising comparing the predicted likelihood of allergies with a ground truth to evaluate the accuracy of the AI algorithm. The method of 1, further comprising providing treatment options based on the predicted likelihood of allergies. A computer program product for predicting allergies from ultrasound images comprising: a. A computer-readable medium b. Computer-executable instructions stored on the computer- readable medium that, when executed, cause a computer to perform the steps of: i. Collecting ultrasound images from patients with and without various allergies ii. Training an AI algorithm using machine learning techniques to analyze the images and predict the likelihood of allergies 33 9354.06PCTiii. Applying the AI algorithm to ultrasound images of patients to predict the likelihood of allergies. The computer program product of 6, wherein the Al algorithm is trained using a convolutional neural network. The computer program product of 6, wherein the ultrasound images are collected using a high-frequency transducer. The computer program product of 6, further comprising updating the AI algorithm based on new ultrasound images and ground truth data to improve its accuracy over time. The computer program product of 6, wherein the predicted likelihood of allergies is used to provide treatment options for patients. A method for identifying allergies from ultrasound images comprising: a. Analyzing an ultrasound image of a patient using an AI algorithm trained to identify allergies from ultrasound images b. Predicting the likelihood of allergies based on the analysis of the ultrasound image. The method of 11, wherein the AI algorithm is trained using a convolutional neural network. The method of 11, further comprising providing treatment options based on the predicted likelihood of allergies. The method of 11, further comprising updating the AI algorithm based on new ultrasound images and ground truth data to improve its accuracy over time. A computer program product for identifying allergies from ultrasound images comprising: a. A computer-readable medium b. Computer-executable instructions stored on the computer-readable medium that, when executed, cause a computer to perform the steps of: i. Analyzing an ultrasound image of a patient using an AI algorithm trained to identify allergies from ultrasound images ii. Predicting the likelihood of allergies based on the analysis of the ultrasound image. The computer program product of 15, wherein the AI algorithm is trained using a convolutional neural network. The computer program product of 15, further comprising updating the AI algorithm based on new ultrasound images and ground truth data to improve its accuracy over time. 34 9354.06PCTThe computer program product of 15, wherein the predicted likelihood of allergies is used to provide treatment options for patients. AI-Enabled Method for Early Detection of Diabetes or Quantitative Prediction of Diabetes using Ultrasound Imaging The present invention relates to a method for predicting the development of diabetes in patients using ultrasound imaging and artificial intelligence (AI) technology. The method involves analyzing specific features of the pancreas and other relevant organs from ultrasound images to identify early indicators of diabetes, such as changes in insulin production or glucose metabolism. The AI model is trained on a large dataset of ultrasound images from patients with and without diabetes to accurately predict the risk of developing diabetes in individual patients. The invention has the potential to enable earlier intervention and treatment, potentially preventing the development of full-blown diabetes and improving patient outcomes. To collect the data to train the AI described in this patent, the following process can be followed: Identify and recruit patients: Patients with and without diabetes need to be recruited to obtain ultrasound images. Patients with diabetes should be selected based on their known diabetes history or positive results on diabetes tests, while patients without diabetes should be selected based on their negative diabetes history and test results. Obtain consent: All patients must provide informed consent before participating in the study. The consent form should explain the purpose of the study, the types of data to be collected, and the potential risks and benefits of participating. Collect ultrasound in1ages: Ultrasound images of the pancreas and other relevant organs should be collected from both groups of patients using standard ultrasound equipment and techniques. The images should be of high quality and include various angles and views to capture as much detail as possible. Collect patient data: In addition to ultrasound images, patient data should also be collected, including demographic information, diabetes history, and diabetes test results. This information can be used to identify the patients with and without diabetes and to evaluate the accuracy of the Al algorithm. Label and annotate the images: Each ultrasound image should be labeled and annotated to indicate whether the patient has diabetes or not. This infom1ation will be used to train the AI algorithm to predict the risk of developing diabetes. 35 9354.06PCTTrain the AI algorithm: The labeled and annotated ultrasound images should be used to train the AI algorithm using deep learning techniques. The algorithm should be optimized to predict the risk of developing diabetes with high accuracy. Test the AI algorithm: Once the AI algorithm has been trained, it should be tested on a separate set of ultrasound images to evaluate its accuracy. The predicted risk of developing diabetes should be compared with the ground truth (i.e., patient data) to determine the algorithm's accuracy. Provide early warning and recommendation systems: Based on the predicted risk of developing diabetes, early warning and recommendation systems can be provided to patients and healthcare providers. For example, patients with high predicted risk may be advised to make lifestyle changes or start medication to prevent or delay the onset of diabetes. These systems should be evaluated to detem1ine their effectiveness in improving patient outcomes. Update the AI algorithm: As new ultrasound images and patient data become available, the AI algorithm should be updated and retrained to improve its accuracy over time. A method for predicting the development of diabetes in a patient comprising: obtaining one or more ultrasound images of the pancreas and other relevant organs; analyzing specific features of the ultrasound images using an AI model trained on a large dataset of ultrasound images from patients with and without diabetes; and predicting the risk of developing diabetes in the patient based on the analysis of the ultrasound images. 2: The method of 1, wherein the specific features analyzed from the ultrasound images include changes in insulin production and glucose metabolism. 3: The method of 1, wherein the AI model is trained using deep learning techniques. 4: The method of 1, further comprising providing an early warning system to alert the patient and healthcare provider of the predicted risk of developing diabetes. 5: The method of 1, further comprising recommending interventions or treatments to prevent the development of diabetes or delay its progression based on the predicted risk. 6: A system for predicting the development of diabetes in a patient comprising: an ultrasound imaging device to obtain one or more ultrasound images of the pancreas and other relevant organs; and an AI model trained on a large dataset of ultrasound images 36 9354.06PCTfrom patients with and without diabetes to analyze specific features of the ultrasound images and predict the risk of developing diabetes in the patient based on the analysis. 7: The system of 6, further comprising an early warning system to alert the patient and healthcare provider of the predicted risk of developing diabetes. 8: The system of 6, further comprising a recommendation system to provide interventions or treatments to prevent the development of diabetes or delay its progression based on the predicted risk. 9: A computer-readable medium comprising instructions for predicting the development of diabetes in a patient comprising: obtaining one or more ultrasound images of the pancreas and other relevant organs; analyzing specific features of the ultrasound images using an AI model trained on a large dataset of ultrasound images from patients with and without diabetes; and predicting the risk of developing diabetes in the patient based on the analysis of the ultrasound images. 10: The computer-readable medium of 9, further comprising instructions for providing an early warning system to alert the patient and healthcare provider of the predicted risk of developing diabetes. 11: The computer-readable medium of 9, further comprising instructions for recommending interventions or treatments to prevent the development of diabetes or delay its progression based on the predicted risk. 12: The method of 1, wherein the ultrasound images are obtained non-invasively and without radiation exposure, thereby reducing the risk to the patient. 13: The system of 6, wherein the ultrasound imaging device is configured to obtain ultrasound images non-invasively and without radiation exposure, thereby reducing the risk to the patient. 14: The method of 1, wherein the AI model is trained on a diverse dataset of ultrasound images from patients with varying demographics, medical histories, and ultrasound equipment to increase the accuracy of the predictions. 15: The system of 6, wherein the AI model is trained on a diverse dataset of ultrasound images from patients with varying demographics, medical histories, and ultrasound equipment to increase the accuracy of the predictions. 16: The method of 1, further comprising analyzing additional medical data from the patient, such as blood glucose levels and family history, to enhance the accuracy of the predicted risk. 37 9354.06PCT17: The system of 6, further comprising analyzing additional medical data from the patient, such as blood glucose levels and family history, to enhance the accuracy of the predicted risk. 18: The method of 1, wherein the AI model is trained using a combination of supervised and unsupervised learning techniques to identify previously unknown patterns and relationships in the ultrasound images. 19: The system of 6, wherein the AI model is trained using a combination of supervised and unsupervised learning techniques to identify previously unknown patterns and relationships in the ultrasound images. 20: The method of 1, wherein the predicted risk of developing diabetes is continuously updated based on new ultrasound images and patient data, allowing for ongoing monitoring and adjustment of treatment plans. 21: The system of 6, wherein the predicted risk of developing diabetes is continuously updated based on new ultrasound images and patient data, allowing for ongoing monitoring and adjustment of treatment plans. AI Quantitative Prediction of Arthritis Using Ultrasound This invention relates to the use of AI technology and ultrasound images to predict the development of arthritis in patients. The invention analyzes specific features of the joints and other relevant organs to identify early indicators of arthritis, allowing for earlier intervention and treatment to potentially prevent the development of full-blown arthritis. The invention uses a combination of AI technology and ultrasound imaging to analyze and identify specific features of the joints and other relevant organs that are indicative of the development of arthritis. The AI model is trained on a large dataset of ultrasound images from patients with and without arthritis, allowing it to learn to recognize patterns and make accurate predictions. The AI model can predict the likelihood of developing arthritis based on the presence or absence of specific features, such as joint inflammation or changes in bone density. This allows for earlier intervention and treatment to potentially prevent the development of full-blown arthritis and improve patient outcomes. The invention can be applied in a variety of settings, including clinical practice, research, and drug development. By identifying patients at high risk for developing arthritis, the invention can inform treatment decisions and improve patient outcomes. Process to collect training data for the Al described in the patent: 38 9354.06PCTIdentify and recruit patients: Patients with and without arthritis need to be recruited to obtain ultrasound images. Patients with arthritis should be selected based on their known arthritis history or positive results on arthritis tests, while patients without arthritis should be selected based on their negative arthritis history and test results. Obtain consent: All patients must provide informed consent before participating in the study. The consent form should explain the purpose of the study, the types of data to be collected, and the potential risks and benefits of participating. Collect ultrasound images: Ultrasound images of the relevant joints and other organs should be collected from both groups of patients using standard ultrasound equipment and techniques. The images should be of high quality and include various angles and views to capture as much detail as possible. Collect patient data: In addition to ultrasound images, patient data should also be collected, including demographic information, arthritis history, and arthritis test results. This information can be used to identify the patients with and without arthritis and to evaluate the accuracy of the AI algorithm. Label and annotate the images: Each ultrasound image should be labeled and annotated to indicate whether the patient has arthritis or not. This information will be used to train the AI algorithm to predict the likelihood of developing arthritis. Train the AI algorithm: The labeled and annotated ultrasound images should be used to train the AI algorithm using machine learning techniques. The algorithm should be optimized to predict the likelihood of developing arthritis with high accuracy. Test the Al algorithm: Once the AI algorithm has been trained, it should be tested on a separate set of ultrasound images to evaluate its accuracy. The predicted likelihood of developing arthritis should be compared with the ground truth (i.e., patient data) to determine the algorithm's accuracy. Use the AI algorithm for prediction and treatment: The AI algorithm can be used to predict the likelihood of developing arthritis in individual patients based on their ultrasound images. This can inform treatment decisions, such as early intervention or lifestyle changes, to potentially prevent the development of full-blown arthritis and improve patient outcomes. Update the AI algorithm: As new ultrasound images and patient data become available, the AI algorithm should be updated and retrained to improve its accuracy 39 9354.06PCTover time. A method for predicting the likelihood of developing arthritis in a patient using ultrasound images and Al technology, comprising: a. obtaining ultrasound images of the patient's joints and other relevant organs; b. analyzing the ultrasound images using an AI model trained to recognize features indicative of the development of arthritis; c. predicting the likelihood of developing arthritis based on the presence or absence of specific features in the ultrasound images. The method of 1, wherein the AI model is trained on a dataset of ultrasound images from patients with and without arthritis. The method of 1, wherein the AI model uses machine learning algorithms to recognize patterns and make accurate predictions. The method of 1, wherein the specific features analyzed in the ultrasound images include joint inflammation and changes in bone density. The method of 1, wherein the prediction of the likelihood of developing arthritis informs treatment decisions for the patient. The method of 1, wherein the prediction of the likelihood of developing arthritis is used in research or drug development. By using AI technology and ultrasound images to predict the development of arthritis, this invention has the potential to improve patient outcomes and inform treatment decisions. Further research and development are necessary to refine the technology and validate its accuracy and reliability for this application. Ultrasound and AI for the Early Detection of Inflammatory Bowel Disease The present invention relates to a method of using ultrasound imaging combined with AI to predict the development of inflammatory bowel disease (IBD) in patients. The AI model analyzes ultrasound images of the gastrointestinal tract to identify early signs of inflammation, such as changes in intestinal wall thickness or blood flow. This early detection allows for earlier intervention and treatment, potentially preventing the development of full-blown IBO and improving patient outcomes. A method for the early detection of inflammatory bowel disease in a patient, comprising: a. obtaining ultrasound images of the gastrointestinal tract of the patient; b. processing the ultrasound images with an AI model to identify early signs of inflammation; c. predicting the likelihood oftbe development of inflammatory bowel disease based on the identified early signs of inflammation; d. providing the patient with 40 9354.06PCTearly intervention and treatment based on the predicted likelihood of the development of inflammatory bowel disease. The method of I, wherein the AI model is trained on a dataset of ultrasound images from patients with and without inflammatory bowel disease. The method of I, wherein the early signs of inflammation include changes in intestinal wall thickness and blood flow. The method of I, wherein the early intervention and treatment includes dietary and lifestyle modifications, medication, or surgical procedures. The method of I, wherein the method is used to monitor patients with a history of inflammatory bowel disease to detect disease recurrence or progression. The method of 1, wherein the AI model is updated and refined based on new ultrasound images and patient outcomes. This invention relates to a method for using ultrasound imaging and AI to predict the development of inflammatory bowel disease (IBD) in patients. By analyzing ultrasound images of the gastrointestinal tract, the AI model can identify early signs of inflammation, such as changes in intestinal wall thickness and blood flow. The AI model is trained on a dataset of ultrasound images from patients with and without IBD to accurately predict the likelihood of the development of IBD in patients. The method provides for early detection of IBD, allowing for earlier intervention and treatment to potentially prevent the development of full-blown IBD and improve patient outcomes. The early intervention and treatment may include dietary and lifestyle modifications, medication, or surgical procedures. The method may also be used to monitor patients with a history of IBD to detect disease recurrence or progression. The AI model can be updated and refined based on new ultrasound images and patient outcomes, increasing the accuracy and reliability of the predictions. However, further research and development is necessary to validate the technology for this application, and regulatory approvals may be required before it can be widely implemented in clinical settings. Objective Pain Measurement and Localization using Ultrasound and AI The present invention relates to the field of pain management, and more specifically to a process for objectively measuring pain and identifying its exact location in the body. Accurate pain assessment and localization are essential for proper diagnosis and treatment of various medical conditions. 41 9354.06PCTThe present invention proposes a process for objectively measuring pain and identifying its exact location in the body using ultrasound imaging and AI. The process involves collecting a dataset of ultrasound scans from individuals who have intermittent pain in the same area. The ultrasound scans are performed while the patients are actively experiencing pain and when they are not experiencing pain. The patients rate the severity of their pain on a scale during each scan. The dataset is used to train an AI that can accurately identify which images show the patient in pain and which ones do not. The AI can then output an objective pain measurement based on the ultrasound images. Additionally, after a scan is performed while the patient is experiencing pain, pain medication is administered, and a subsequent ultrasound scan is taken once the medication has taken effect. The patient rates their pain again, and this data is used to train an AI to predict the eventual pain ranking before each medication is given. The AI can also be trained to identify the exact location of the pain in the body, which may help speed up the diagnosis of medical conditions. The present invention proposes a process for objectively measuring pain and identifying its exact location in the body using ultrasound imaging and AI. The process involves the following steps: Step l: Dataset collection - A dataset is collected from individuals who have intermittent pain in the same area. The ultrasound scans are performed while the patients are actively experiencing pain and when they are not experiencing pain. The patients rate the severity of their pain on a scale during each scan. Step 2: AI training for pain identification - The dataset is used to train an AI that can accurately identify which images show the patient in pain and which ones do not. The AI can then output an objective pain measurement based on the ultrasound images. Step 3: Medication administration and scan -After a scan is performed while the patient is experiencing pain, pain medication is administered, and a subsequent ultrasound scan is taken once the medication has taken effect. The patient rates their pain again, and this data is used to train an AI to predict the eventual pain ranking before each medication is given. Step 4: AI training for pain localization - The AI can also be trained to identify the exact location of the pain in the body, which may help speed up the diagnosis of medical conditions. 42 9354.06PCTAdvantages: The proposed process has several advantages over existing pain assessment methods: Objective pain measurement - The process provides an objective pain measurement, which is not influenced by subjective factors such as the patient's mood, personality, or culture. Accurate pain localization - The process can accurately identify the exact location of the pain in the body, which may help speed up the diagnosis of medical conditions. Personalized pain medication - The process can predict which pain medication would be most effective for any particular pain, based on the patient's pain ranking and medication history. The present invention proposes a process for objectively measuring pain and identifying its exact location in the body using ultrasound imaging and AI. The process has several advantages over existing pain assessment methods, including objective pain measurement, accurate pain localization, and personalized pain medication. The invention has potential applications in various medical fields, including pain management, diagnosis, and treatment of medical conditions. A process for objectively measuring pain and identifying its exact location in the body, comprising the steps of: collecting a dataset of ultrasound scans from individuals who have intermittent pain in the same area; performing ultrasound scans while the patients are actively experiencing pain and when they are not experiencing pain; rating the severity of the pain on a scale during each scan; training an AI that can accurately identify which images show the patient in pain and which ones do not; outputting an objective pain measurement based on the ultrasound images; administering pain medication after a scan is performed while the patient is experiencing pain; taking a subsequent ultrasound scan once the medication bas taken effect; rating the pain again; and training an AI to predict the eventual pain ranking before each medication is given. The process of 1, wherein the AI is also trained to identify the exact location of the pain in the body. The process of 1, wherein the AI is trained to predict which pain medication would be most effective for any particular pain, based on the patient's pain ranking and medication history. 43 9354.06PCTThe process of 1, wherein the dataset is collected from a plurality of patients, and the AI is trained using machine learning techniques. A system for objectively measuring pain and identifying its exact location in the body, comprising: an ultrasound machine configured to perform ultrasound scans while the patients are actively experiencing pain and when they are not experiencing pain; a rating scale for the patients to rate the severity of the pain during each scan; a data storage unit for storing the ultrasound images and pain ratings; an AI module trained to accurately identify which images. show the patient in pain and which ones do not and to output an objective pain measurement based on the ultrasound images; and a medication administration module configured to administer pain medication after a scan is performed while the patient is experiencing pain, and to take a subsequent ultrasound scan once the medication has taken effect. The system of 5, wherein the AI module is also trained to identify the exact location of the pain in the body. The system of 5, wherein the Al module is trained to predict which pain medication would be most effective for any particular pain, based on the patient's pain ranking and medication history. The system of 5, wherein the data storage unit is further configured to store the medication administered and the pain rating after medication, and the AI module is trained to predict the eventual pain ranking before each medication is given. A method for identifying the effectiveness of pain medication, comprising the steps of: administering pain medication after a scan is performed while the patient is experiencing pain; taking a subsequent ultrasound scan once the medication has taken effect; rating the pain again; and using an Al to predict the eventual pain ranking before each medication is given, based on the patient's pain ranking and medication history. The method of 9, wherein the Al is trained to predict which pain medication would be most effective for any particular pain, based on the patient's pain ranking and medication history. Objective Symptom Measurement and Localization using Ultrasound and Al The present invention relates to the field of medical diagnosis and treatment, and more specifically to a process for objectively measuring medical symptoms and identifying 44 9354.06PCTtheir exact location in the body. Accurate symptom assessment and localization are essential for proper diagnosis and treatment of various medical conditions. The present invention proposes a process for objectively measuring medical symptoms and identifying their exact location in the body using ultrasound imaging and AI. The process involves collecting a dataset of ultrasound scans from individuals who have intermittent symptoms in the same area. The ultrasound scans are performed while the patients are experiencing the symptoms and when they are not experiencing the symptoms. The patients rate the severity of their symptoms on a scale during each scan. The dataset is used to train an AI that can accurately identify which images show the patient with symptoms and which ones do not. The AI can then output an objective measurement based on the ultrasound images. Additionally, after a scan is performed while the patient is experiencing symptoms, appropriate treatment is administered, and a subsequent ultrasound scan is taken once the treatment has taken effect. The patient rates their symptoms again, and this data is used to train an AI to predict the eventual symptom ranking before each treatment is given. The AI can also be trained to identify the exact location of the symptoms in the body, which may help speed up the diagnosis of medical conditions. The present invention proposes a process for objectively measuring medical symptoms and identifying their exact location in the body using ultrasound imaging and AI. The process involves the following steps: Step I: Dataset collection - A dataset is collected from individuals who have intermittent symptoms in the same area. The ultrasound scans are performed while the patients are experiencing the symptoms and when they are not experiencing the symptoms. The patients rate the severity of their symptoms on a scale during each scan. Step 2: AI training for symptom identification - The dataset is used to train an AI that can accurately identify which images show the patient with symptoms and which ones do not. The Al can then output an objective measurement based on the ultrasound images. Step 3: Treatment administration and scan - After a scan is performed while the patient is experiencing symptoms, appropriate treatment is administered, and a subsequent ultrasow1d scan is taken once the treatment has taken effect. The patient rates their 45 9354.06PCTsymptoms again, and this data is used to train an AI to predict the eventual symptom ranking before each treatment is given. Step 4: AI training for symptom localization - The AI can also be trained to identify the exact location of the symptoms in the body, which may help speed up the diagnosis of medical conditions. Advantages: The proposed process has several advantages over existing symptom assessment methods: Objective symptom measurement - The process provides an objective measurement of medical symptoms, which is not influenced by subjective factors such as the patient's mood, personality, or culture. Accurate symptom localization - The process can accurately identify the exact location of the symptoms in the body, which may help speed up the diagnosis of medical conditions. Personalized treatment - The process can predict which treatment would be most effective for any particular symptom, based on the patient's symptom ranking and treatment history. The present invention proposes a process for objectively measuring medical symptoms and identifying their exact location in the body using ultrasound imaging and AI. The process has several advantages over existing symptom assessment methods, including objective symptom measurement, accurate symptom localization, and personalized treatment. The invention has potential applications in various medical fields, including diagnosis and treatment of medical conditions. A process for objectively measuring medical symptoms and identifying their exact location in the body, comprising the steps of: collecting a dataset of ultrasound scans from individuals who have intermittent symptoms in the same area; performing ultrasound scans while the patients are experiencing the symptoms and when they are not experiencing the symptoms; rating the severity of the symptoms on a scale during each scan; training an AI that can accurately identify which images show the patient with symptoms and which ones do not; outputting an objective measurement based on the ultrasound images; administering appropriate treatment after a scan is performed while the patient is experiencing symptoms; taking a subsequent ultrasound scan once the treatment has taken effect; rating the symptoms again; and training an AI to predict the eventual symptom ranking before each treatment is given. 46 9354.06PCTThe process of 1, wherein the AI is also trained to identify the exact location of the symptoms in the body. The process of 1, wherein the AI is trained to predict which treatment would be most effective for any particular symptom, based on the patient's symptom ranking and treatment history. The process of 1, wherein the dataset is collected from a plurality of patients, and the AI is trained using machine learning techniques. A system for objectively measuring medical symptoms and identifying their exact location in the body, comprising: an ultrasound machine configured to perfom1 ultrasound scans while the patients are experiencing the symptoms and when they are not experiencing the symptoms; a rating scale for the patients to rate the severity of the symptoms during each scan; a data storage unit for storing the ultrasound images and symptom ratings; an AI module trained to accurately identify which images show the patient with symptoms and which ones do not and to output an objective measurement based on the ultrasound images; and a treatment administration module configured to administer appropriate treatment after a scan is performed while the patient is experiencing symptoms, and to take a subsequent ultrasound scan once the treatment has taken effect. The system of 5, wherein the AI module is also trained to identify the exact location of the symptoms in the body. The system of 5, wherein the AI module is trained to predict which treatment would be most effective for any particular symptom, based on the patient's symptom ranking and treatment history. The system of 5, wherein the data storage unit is further configured to store the treatment administered and the symptom rating after treatment, and the Al module is trained to predict the eventual symptom ranking before each treatment is given. A method for identifying the effectiveness of medical treatment, comprising the steps of: administering appropriate treatment after a scan is performed while the patient is experiencing symptoms; taking a subsequent ultrasound scan once the treatment has taken effect; rating the symptoms again; and using an AI to predict the eventual symptom ranking before each treatment is given, based on the patient's symptom ranking and treatment history. The method of 9, wherein the Al is trained to predict which treatment would 47 9354.06PCTbe most effective for any particular symptom, based on the patient's symptom ranking and treatment history. Objective Symptom Measurement and Drug Selection using Ultrasound and AI The present invention relates to the field of medical diagnosis and treatment, and more specifically to a process for objectively measuring medical symptoms and identifying the most effective drug for treatment. Accurate symptom assessment and drug selection are essential for proper diagnosis and treatment of various medical conditions. The present invention proposes a process for objectively measuring medical symptoms and identifying the most effective drug for treatment using ultrasound imaging and AI. The process involves collecting a dataset of ultrasound scans from individuals who have intermittent symptoms in the same area. The ultrasound scans are performed while the patients are experiencing the symptoms and when they are not experiencing the symptoms. The patients rate the severity of their symptoms on a scale during each scan. The dataset is used to train an Al that can accurately identify which images show the patient with symptoms and which ones do not. The AI can then output an objective measurement based on the ultrasound images. Additionally, after a scan is performed while the patient is experiencing symptoms, various drugs are administered, and a subsequent ultrasound scan is taken once the drugs have taken effect. The patient rates their symptoms again, and this data is used to train an AI to predict the most effective drug for each symptom. The AI can also be trained to identify the exact location of the symptoms in the body, which may help speed up the diagnosis of medical conditions. The present invention proposes a process for objectively measuring medical symptoms and identifying the most effective drug for treatment using ultrasound imaging and AI. The process involves the following steps: Step I: Dataset collection - A dataset is collected from individuals who have intermittent symptoms in the same area. The ultrasound scans are performed while the patients are experiencing the symptoms and when they are not experiencing the symptoms. The patients rate the severity of their symptoms on a scale during each scan. Step 2: AI training for symptom identification - The dataset is used to train an AI that can accurately identify which images show the patient with symptoms and which ones 48 9354.06PCTdo not. The AI can then output an objective measurement based on the ultrasound images. Step 3: Drug administration and scan - After a scan is performed while the patient is experiencing symptoms, various drugs are administered, and a subsequent ultrasound scan is taken once the drugs have taken effect. The patient rates their symptoms again, and this data is used to train an AI to predict the most effective drug for each symptom. Step 4: AI training for symptom localization - The AI can also be trained to identify the exact location of the symptoms in the body, which may help speed up the diagnosis of medical conditions. Advantages: The proposed process has several advantages over existing symptom assessment methods: Objective symptom measurement - The process provides an objective measurement of medical symptoms, which is not influenced by subjective factors such as the patient's mood, personality, or culture. Effective drug selection - The process can predict the most effective drug for each symptom, based on the patient's symptom ranking and drug history. Accurate symptom localization - The process can accurately identify the exact location of the symptoms in the body, which may help speed up the diagnosis of medical conditions. The present invention proposes a process for objectively measuring medical symptoms and identifying the most effective drug for treatment using ultrasound imaging and AL The process has several advantages over existing symptom assessment methods, including objective symptom measurement, effective drug selection, and accurate symptom localization. The invention has potential applications in various medical fields, including diagnosis and treatment of medical conditions. A process for objectively measuring medical symptoms and identifying the most effective drug for treatment, comprising the steps of: collecting a dataset of ultrasound scans from individuals who have intermittent symptoms in the same area; performing ultrasound scans while the patients are experiencing the symptoms and when they are not experiencing the symptoms; rating the severity of the symptoms on a scale during each scan; training an AI that can accurately identify which images show the patient with symptoms and which ones do not; outputting an objective measurement based on the ultrasound images; administering various drugs after a scan is performed while the 49 9354.06PCTpatient is experiencing symptoms; taking a subsequent ultrasound scan once the drugs have taken effect; rating the symptoms again; and training an AI to predict the most effective drug for each symptom. The process of 1, wherein the AI is also trained to identify the exact location of the symptoms in the body. The process of I, wherein the dataset is collected from a plurality of patients, and the AI is trained using machine learning techniques. The process of 1, wherein the drugs are selected from a group consisting of analgesics, anti- inflammatory drugs, antibiotics, antihistamines, antivirals, and other therapeutic agents. A system for objectively measuring medical symptoms and identifying the most effective drug for treatment, comprising: an ultrasound machine configured to perform ultrasound scans while the patients are experiencing the symptoms and when they are not experiencing the symptoms; a rating scale for the patients to rate the severity of the symptoms during each scan; a data storage unit for storing the ultrasound images and symptom ratings; an AI module trained to accurately identify which images show the patient with symptoms and which ones do not and to output an objective measurement based on the ultrasound images; a drug administration module configured to administer various drugs after a scan is performed while the patient is experiencing symptoms, and to take a subsequent ultrasound scan once the drugs have taken effect; and a symptom rating module configured to rate the symptoms again after drug administration. The system of 5, wherein the AI module is also trained to identify the exact location of the symptoms in the body. The system of 5, wherein the dataset is collected from a plurality of patients, and the AI module is trained using machine learning techniques. The system of 5, wherein the drugs are selected from a group consisting of analgesics, anti- inflammatory drugs, antibiotics, antihistamines, antivirals, and other therapeutic agents. The system of 5, further comprising a drug selection module configured to predict the most effective drug for each symptom based on the patient's symptom ranking and drug history. A method for predicting the most effective drug for medical symptoms, comprising the steps of: administering various drugs after a scan is performed while the patient is experiencing symptoms; taking a subsequent ultrasound scan once the drugs have taken 50 9354.06PCTeffect; rating the symptoms again; and using an AI to predict the most effective drug for each symptom, based on the patient's symptom ranking and drug history. The method of 10, wherein the AI is also trained to identify the exact location of the symptoms in the body. The method of 10, wherein the drugs are selected from a group consisting of analgesics, anti- inflammatory drugs, antibiotics, antihistamines, antivirals, and other therapeutic agents. The method of I0, further comprising the step of collecting a dataset of ultrasound scans from individuals who have intermittent symptoms in the same area, and training the AI using machine learning techniques. Objective Dosage Determination of Medications using ultrasound and AI The present invention relates to the field of medical diagnosis and treatment, and more specifically to a process for objectively determining the optimum dosage of a medication to alleviate a specific symptom or disease. Accurate dosage determination is essential for proper diagnosis and treatment of various medical conditions. The present invention proposes a process for objectively determining the optimum dosage of a medication to alleviate a specific symptom or disease using ultrasound imaging and Al. The process involves collecting a dataset of ultrasound scans from individuals who have intermittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. The dataset is used to train an AI that can identify which images show the patient symptomatic and which ones are not. The Al or a related one can output the objective symptom measurement. Additionally, after a scan is performed while the patient is having symptoms, some medication is administered with a subsequent ultrasound scan taken once the medication has taken effect. The patient gives the new symptom ranking after the medication. This data is used to train an AI to predict what the eventual rank will be before each medication is given, allowing a physician to determine which medication would be most effective for any particular symptom, condition, or disease. The Al can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. 51 9354.06PCTThe present invention proposes a process for objectively determining the optimum dosage of a medication to alleviate a specific symptom or disease using ultrasound imaging and AI. The process involves the following steps: Step l: Dataset collection - A dataset is collected from individuals who have intermittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. Step 2: Al training for symptom identification - The dataset is used to train an Al that can identify which images show the patient symptomatic and which ones are not. The AI or a related one can output the objective symptom measurement. Step 3: Medication administration and scan -After a scan is performed while the patient is having symptoms, some medication is administered with a subsequent ultrasound scan taken once the medication has taken effect. The patient gives the new symptom ranking after the medication. This data is used to train an AI to predict what the eventual rank will be before each medication is given, allowing a physician to determine which medication would be most effective for any particular symptom, condition, or disease. Step 4: AI training for symptom localization - The Al can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. Advantages: The proposed process has several advantages over existing dosage determination methods: Objective symptom measurement - The process provides an objective measurement of medical symptoms, which is not influenced by subjective factors such as the patient's mood, personality, or culture. Effective dosage determination - The process can predict the optimum dosage of a medication to alleviate a specific symptom or disease based on the patient's symptom ranking and medication history. Accurate symptom localization - The process can accurately identify the specific location of the symptoms in the body, which may speed up diagnosis of conditions. The present invention proposes a process for objectively determining the optimum dosage of a medication to alleviate a specific symptom or disease using ultrasound imaging and AI. The process has several advantages over existing dosage 52 9354.06PCTdetermination methods, including objective symptom measurement, effective dosage determination, and accurate A process for objectively determining the optimum dosage of a medication to alleviate a specific symptom or disease, comprising: a. Collecting a dataset of ultrasound scans from individuals who have intermittent symptoms or variations of severity in the same area; b. Performing an ultrasound scan while the individuals are actively symptomatic and while there is no symptoms or a change in severity; c. Rating the severity of the symptoms on a scale during each scan; d. Training an AI that can identify which images show the patient symptomatic and which ones are not; e. Administering some medication after a scan is performed while the patient is having symptoms, and taking a subsequent ultrasound scan once the medication has taken effect; f Collecting the new symptom ranking after the medication is administered; g. Training an AI on the collected data to predict the eventual symptom ranking before each medication is given. The process of I, wherein the ultrasound scans are performed on the exact same area for numerous individuals. The process of 1, wherein the AI can output the objective symptom measurement. The process of 1, wherein the AI can identify the specific location of the symptoms in the body. The process of 1, wherein the trained AI can predict the most effective medication for each symptom, condition, or disease based on the patient's symptom ranking and medication history. The process of 1, wherein the process provides an objective measurement of medical symptoms that is not influenced by subjective factors such as the patient's mood, personality, or culture. The process of 1, wherein the process can accurately identify the exact location of the symptoms in the body, which may help speed up the diagnosis of medical conditions. The process of I, wherein the process can be used in various medical fields, including diagnosis and treatment of medical conditions. Objective Administration Route Determination of Medications using Ultrasound and AI The present invention relates to the field of medical diagnosis and treatment, and more specifically to a process for objectively determining the optimum administration route 53 9354.06PCTof a medication to alleviate a specific symptom or disease. Accurate administration route determination is essential for proper diagnosis and treatment of various medical conditions. The present invention proposes a process for objectively determining the optimum administration route of a medication to alleviate a specific symptom or disease using ultrasound imaging and AI. The process involves collecting a dataset of ultrasound scans from individuals who have intermittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. The dataset is used to train an AI that can identify which images show the patient symptomatic and which ones are not. The AI or a related one can output the objective symptom measurement. Additionally, after a scan is performed while the patient is having symptoms, some medication is administered with a subsequent ultrasound scan taken once the medication has taken effect. The patient gives the new symptom ranking after the medication. This data is used to train an AI to predict what the eventual rank will be before each medication is given, allowing a physician to determine which administration route of medication would be most effective for any particular symptom, condition, or disease. The AI can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. The present invention proposes a process for objectively determining the optimum administration route of a medication to alleviate a specific symptom or disease using ultrasound imaging and AI. The process involves the following steps: Step l: Dataset collection - A dataset is collected from individuals who have intermittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. Step 2: AI training for symptom identification - The dataset is used to train an AI that can identify which images show the patient symptomatic and which ones are not. The AI or a related one can output the objective symptom measurement. 54 9354.06PCTStep 3: Medication administration and scan -After a scan is performed while the patient is having symptoms, some medication is administered with a subsequent ultrasound scan taken once the medication bas taken effect. The patient gives the new symptom ranking after the medication. This data is used to train an Al to predict what the eventual rank will be before each medication is given, allowing a physician to determine which administration route of medication would be most effective for any particular symptom, condition, or disease. Step 4: AI training for symptom localization - The AI can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. Advantages: The proposed process has several advantages over existing administration route determination methods: Objective symptom measurement - The process provides an objective measurement of medical symptoms, which is not influenced by subjective factors such as the patient's mood, personality, or culture. Effective administration route determination - The process can predict the optimum administration route of a medication to alleviate a specific symptom or disease based on the patient's symptom ranking and medication history. Accurate symptom localization - The process can accurately identify the specific location of the symptoms in the body, which may speed up diagnosis of conditions. The present invention proposes a process for objectively determining the optimum administration route of a medication to alleviate a specific symptom or disease using ultrasound imaging and AI. The process has several advantages over existing administration A method for objectively determining the optimum administration route of a medication to alleviate a specific symptom or disease, comprising: collecting a dataset of ultrasound scans from individuals who have intermittent symptoms or variations of severity in the same area, performing ultrasound scans on the exact same area when there is no symptoms or a change in severity, training an AI that can identify which images show the patient symptomatic, administering medication after a scan is performed while the patient is having symptoms, taking a subsequent ultrasound scan once the medication has taken effect, and training an AI to predict the optimal administration route of a medication for a particular symptom, condition, or disease. 55 9354.06PCTThe method of I, wherein the dataset of ultrasound scans includes ratings of the severity of the symptoms on a scale by the patient. The method of I, wherein the AI is trained to predict the optimum administration route of a medication based on the patient's symptom ranking and medication history. The method of 1, wherein the AI is trained to identify the specific location of the symptoms in the body, which may speed up diagnosis of conditions. The method of I, wherein the ultrasound scans are performed using high-frequency sound waves that are safe and non-invasive. The method of 1, wherein the medication is administered through various administration routes such as oral, injection, or topical. The method of 1, wherein the AI is trained to predict the optimum administration route of a medication for different medical conditions. The method of 1, wherein the AI is trained to predict the optimum administration route of a medication based on demographic factors such as age, sex, and medical history. The method of 1, wherein the AI is trained to predict the optimum administration route of a medication based on the specific symptom or disease being treated. The method of 1, wherein the AI is trained using deep learning algorithms to improve the accuracy of the predictions. Objective Symptom Measurement and Drug Administration Optimization in Clinical Trials using Ultrasound and AI Clinical trials are a vital part of the drug development process, but they can be costly and time- consuming. Accurate symptom measurement and drug administration optimization are essential to ensure the success of clinical trials. However, traditional methods of symptom assessment and drug selection may be subjective and not adequately capture individual variations in symptom severity and response to treatment. The present invention proposes a process for improving clinical trials through objective symptom measurement and drug administration optimization using ultrasound imaging and Al. The process involves collecting a dataset of ultrasound scans from individuals who have intermittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. 56 9354.06PCTThe dataset is used to train an AI that can identify which images show the patient symptomatic and which ones are not. The AI or a related one can output the objective symptom measurement. Additionally, after a scan is performed while the patient is having symptoms, some medication is administered with a subsequent ultrasound scan taken once the medication has taken effect. The patient gives the new symptom ranking after the medication. This data is used to train an AI to predict what the eventual rank will be before each medication is given, allowing a physician to determine which medication would be most effective for any particular symptom, condition, or disease. The Al can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. Furthermore, the AI can be trained to predict the optimal dosage and administration route for each individual patient. The present invention proposes a process for improving clinical trials through objective symptom measurement and drug administration optimization using ultrasound imaging and AI. The process involves the following steps: Step I: Dataset collection - A dataset is collected from individuals who have intem1ittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. Step 2: AI training for symptom identification - The dataset is used to train an AI that can identify which images show the patient symptomatic and which ones are not. The AI or a related one can output the objective symptom measurement. Step 3: Medication administration and scan - After a scan is performed while the patient is having symptoms, some medication is administered with a subsequent ultrasound scan taken once the medication has taken effect. The patient gives the new symptom ranking after the medication. This data is used to train an AI to predict what the eventual rank will be before each medication is given, allowing a physician to determine which medication would be most effective for any particular symptom, condition, or disease. Step 4: AI training for symptom localization - The AI can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. 57 9354.06PCTStep 5: AI training for dosage and administration optimization - The AI can be trained to predict the optimal dosage and administration route for each individual patient. Advantages: The proposed process has several advantages over traditional clinical trial methods: Objective symptom measurement - The process provides an objective measurement of medical symptoms, which is not influenced by subjective factors such as the patient's mood, personality, or culture. Effective drug administration optimization - The process can predict the most effective medication and administration route for each individual patient based on their symptom severity and medication history. Accurate symptom localization -The process can accurately identify the specific location of the symptoms in the body, which may speed up. A method for improving clinical trials, comprising: a. collecting objective symptom data from a plurality of individuals who have intermittent symptoms or variations of severity in the same area using ultrasound imaging and an artificial intelligence (AI) system; b. training the AI system to identify the exact location and severity of the symptoms and predict the optimal medication and administration route for each individual based on the symptom data and medication history; c. administering the predicted medication and administration route to the individuals, monitoring the symptom response using ultrasound imaging, and updating the AI system with the response data; d. using the updated AI system to refine the prediction for the subsequent individuals in the clinical trial, thereby improving the efficacy and efficiency of the trial. The method of 1, wherein the objective symptom data is collected by performing ultrasound scans on the individuals while they are actively symptomatic and while they are not symptomatic or experiencing a change in severity. The method of 1, wherein the AI system is trained to predict the optimal administration route for each individual based on the symptom data, medication history, and individual patient characteristics. The method of 1, wherein the AI system is trained to identify which medication is most effective for each symptom based on the symptom data, medication history, and individual patient characteristics. The method of I, wherein the AI system is trained to identify the specific location of the 58 9354.06PCTsymptoms in the body, which may speed up diagnosis of conditions and improve the precision of the clinical trial. The method of I, wherein the AI system is updated continuously with the symptom response data, allowing for real-time adjustments to the medication and administration route for subsequent individuals in the clinical trial. The method of 1, wherein the improved clinical trial leads to a reduction in the number of individuals needed for the trial, a reduction in the trial duration, and an improvement in the accuracy and precision of the results. Ultrasound and AI Prediction of Symptom Onset and Severity The present invention relates to the field of medical diagnosis and treatment, and more specifically to a process for predicting the onset and severity of medical symptoms using ultrasound imaging and AI. Accurate prediction of symptom onset and severity can help patients and healthcare providers better manage medical conditions. The present invention proposes a process for predicting the onset and severity of medical symptoms using ultrasound imaging and AI. The process involves collecting a dataset of ultrasound scans from individuals who have intermittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. The dataset is used to train an AI that can identify which images show the patient symptomatic and which ones are not. The AI can also output the objective symptom measurement. Additionally, the dataset is used to train an AI to predict the onset and severity of symptoms based on the patient's symptom ranking and ultrasound data. The AI can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. The present invention proposes a process for predicting the onset and severity of medical symptoms using ultrasound imaging and AI. The process involves the following steps: Step l: Dataset collection - A dataset is collected from individuals who have intermittent symptoms or variations of severity in the same area. The ultrasound scans are performed while the patients are actively symptomatic, which they rate in severity on some scale. Ultrasound scans are also performed on the exact same area when there are no symptoms or a change in severity. This is done for numerous individuals. 59 9354.06PCTStep 2: AI training for symptom identification - The dataset is used to train an Al that can identify which images show the patient symptomatic and which ones are not. The AI can also output the objective symptom measurement. Step 3: AI training for symptom prediction - The dataset is also used to train an AI to predict the onset and severity of symptoms based on the patient's symptom ranking and ultrasound data. Step 4: AI training for symptom localization - The AI can also be trained to identify the specific location of the symptoms, which may speed up diagnosis of conditions. Advantages: The proposed process has several advantages over existing symptom prediction methods: Objective symptom measurement - The process provides an objective measurement of medical symptoms, which is not influenced by subjective factors such as the patient's mood, personality, or culture. Accurate symptom prediction - The process can predict the onset and severity of symptoms based on the patient's symptom ranking and ultrasound data, which may help patients and healthcare providers better manage medical conditions. Accurate symptom localization - The process can accurately identify the specific location of the symptoms in the body, which may speed up diagnosis of conditions. The present invention proposes a process for predicting the onset and severity of medical symptoms using ultrasound imaging and AI. The process has several advantages over existing symptom prediction methods, including objective symptom measurement, accurate symptom prediction, and accurate symptom localization. The invention has potential applications in various medical fields, including diagnosis and treatment of medical conditions. A method for predicting the time until the next onset of a symptom or pain in a patient, comprising: collecting a dataset of ultrasound scans from individuals who experience intermittent symptoms or pain in the same area; training an Al using the dataset to identify images that show the patient with symptoms or pain; administering medication or therapy to the patient and collecting a subsequent ultrasound scan; using the dataset to train the AI to predict the time until the next onset of symptoms or pain, as well as the objective severity at that time. The method of 1, wherein the medication or therapy administered to the patient is chosen based on the Al's prediction of the time until the next onset of symptoms or 60 9354.06PCTpain. A computer program for predicting the time until the next onset of a symptom or pain in a patient, comprising: code for collecting a dataset of ultrasound scans from individuals who experience intermittent symptoms or pain in the same area; code for training an AI using the dataset to identify images that show the patient with symptoms or pain; code for administering medication or therapy to the patient and collecting a subsequent ultrasound scan; code for using the dataset to train the Al to predict the time until the next onset of symptoms or pain, as well as the objective severity at that time. A system for predicting the time until the next onset of a symptom or pain in a patient, comprising: an ultrasound scanner for collecting a dataset of ultrasound scans from individuals who experience intermittent symptoms or pain in the same area; an AI for identifying images that show the patient with symptoms or pain; a medication or therapy administration device for administering medication or therapy to the patient and collecting a subsequent ultrasound scan; and a processor for using the dataset to train the AI to predict the time until the next onset of symptoms or pain, as well as the objective severity at that time. AI-assisted Ultrasound Diagnosis and Treatment of Infections The present invention provides an AI-assisted system for the identification of infections and determination of the best type of antibiotics to use on the infection. The system utilizes ultrasound imaging and a database of ultrasound images and antibiotic treatment outcomes to provide accurate and efficient diagnosis and treatment recommendations. The system is capable of analyzing ultrasound images to detect signs of infection and comparing them to a database of previous images to determine the most effective antibiotic treatment. Infections are a common medical condition that can be difficult to diagnose and treat. Antibiotic resistance is a growing concern and there is a need for better methods to identify infections and determine the most effective antibiotic treatment. Ultrasound imaging bas been used to diagnose and monitor infections, but traditional diagnostic methods can be time-consuming and often require invasive procedures. AI-assisted diagnosis and treatment can help improve the accuracy and efficiency of infection diagnosis and treatment. The AI-assisted ultrasound diagnosis and treatment system includes an ultrasound imaging device, a database of ultrasound images and antibiotic treatment outcomes, and 61 9354.06PCTan AI algorithm that analyzes ultrasound images to detect signs of infection and recommend the best antibiotic treatment. The AI algorithm is trained on a dataset of ultrasound images and corresponding antibiotic treatment outcomes, which are used to develop predictive models for infection diagnosis and treatment. The system operates as follows: a patient with a suspected infection is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which compares them to the database of previous images to identify signs of infection. The algorithm then recommends the most effective antibiotic treatment based on the patient's individual characteristics and the characteristics of the infection. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound diagnosis and treatment system provides several advantages over traditional infection diagnosis and treatment methods. It is non- invasive and does not require blood or tissue samples, which can be uncomfortable and time-consuming for the patient. It is also more accurate and efficient than traditional methods, allowing for faster and more effective treatment. Additionally, it can help reduce the overuse of antibiotics by recommending the most effective treatment for each individual patient, reducing the risk of antibiotic resistance. The AI-assisted ultrasound diagnosis and treatment system provides a novel and effective method for the diagnosis and treatment of infections. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient diagnosis and treatment recommendations, improving patient outcomes and reducing the risk of antibiotic resistance. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for diagnosing and treating infections, comprising: • an ultrasound imaging device for generating ultrasound images of a patient; • a database of ultrasound images and corresponding antibiotic treatment outcomes; • an AI algorithm trained on the database of ultrasound images and antibiotic treatment outcomes for identifying signs of infection in the ultrasound images and recommending the most effective antibiotic treatment for the infection. 62 9354.06PCT2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of l, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 4. The system of 1, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and recommend antibiotic treatments. 5. The system of l, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when recommending antibiotic treatments. 6. The system of l, wherein the AI algorithm is capable of recommending a combination of antibiotics for treating complex infections. 7. The system of 1, wherein the database of ultrasound images and corresponding antibiotic treatment outcomes includes images and treatment outcomes from a variety of medical settings. 8. The system of l, wherein the AI algorithm is capable of updating its recommendations based on new data entered into the database. 9. The system of l, wherein the AI algorithm is capable of providing a probability of success for each recommended antibiotic treatment. A method for diagnosing and treating infections, comprising: • generating ultrasound images of a patient using an ultrasound imaging device; • analyzing the ultrasound images using an AI algorithm trained on a database of ultrasound images and corresponding antibiotic treatment outcomes; • recommending the most effective antibiotic treatment for the infection based on the analysis of the ultrasound images and the database of antibiotic treatment outcomes. 11. The method of 10, further comprising administering the recommended antibiotic treatment to the patient. 12. The method of l 0, wherein the AI algorithm is capable of recommending a combination of antibiotics for treating complex infections. 13. The method of 10, wherein the AI algorithm is capable of updating its recommendations based on new data entered into the database. 14. The method of 10, wherein the AI algorithm is capable of providing a probability of success for each recommended antibiotic treatment. The method of 10, wherein the ultrasound imaging device is a handheld device. 16. The method of 10, wherein the ultrasound imaging device is integrated 63 9354.06PCTinto a hospital or clinic's existing medical equipment. 17. The method of 10, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and recommend antibiotic treatments. 18. The method of 10, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when recommending antibiotic treatments. System and Method for Embryo Selection using AI and Ultrasound In vitro fertilization (IVF) is a widely used technique for treating infertility. One of the critical steps in the IVF process is selecting the most viable embryos for implantation. Conventionally, this process is done by embryologists manually examining the embryos under a microscope. However, this method is subjective and can lead to errors in embryo selection. The present invention aims to address these issues by providing a system and method for embryo selection using AI algorithms and ultrasound. The present invention provides a system and method for selecting the most viable embryos for implantation in IVF using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the embryos during the !VF process. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The AI algorithm is integrated into the computer system and analyzes the ultrasound images to select the most viable embryos for implantation. The Al algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the embryos. The algorithm compares the embryos to a pre-defined set of criteria to identify the most viable ones for implantation. The system and method of the present invention provide several advantages over conventional embryo selection methods. It reduces the subjectivity and variability associated with manual embryo selection and increases the accuracy and precision of the selection process. This, in turn, can lead to increased success rates of lVF and a reduced risk of multiple pregnancies. 1. A system for embryo selection using AI and ultrasound, comprising: • An ultrasound imaging device for capturing images of embryos during the IVF process; 64 9354.06PCT• A computer system connected to the ultrasound imaging device for receiving the ultrasound images; • An AI algorithm integrated into the computer system for analyzing the ultrasound images to select the most viable embryos for implantation. 2. The system of 1, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the embryos. 3. The system of 1, wherein the AI algorithm compares the embryos to a pre-defined set of criteria to identify the most viable ones for implantation. 4. A method for embryo selection using Al and ultrasound, comprising: • Capturing ultrasound images of embryos during the IVF process using an ultrasound imaging device; • Transmitting the ultrasound images to a computer system connected to the ultrasound imaging device; • Analyzing the ultrasound images using an AI algorithm integrated into the computer system to select the most viable embryos for implantation. 5. The method of 4, wherein the AJ algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the embryos. 6. The method of 4, wherein the Al algorithm compares the embryos to a pre-defined set of criteria to identify the most viable ones for implantation. 7. The method of 4, further comprising implanting the selected embryos into the uterus of the patient undergoing IVF. System and Method for Personalized Ovarian Stimulation using AI and Ultrasound Ovarian stimulation is a critical step in the IVF process, where hormones are administered to stimulate the ovaries to produce multiple eggs. However, the response to ovarian stimulation can vary from patient to patient, and conventional stimulation protocols are often not tailored to individual patient characteristics. This can result in inefficient treatment and increased risk of side effects. The present invention aims to address these issues by providing a system and method for personalized ovarian stimulation using AI algorithms and ultrasound. The present invention provides a system and method for personalizing ovarian stimulation using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an Al algorithm. 65 9354.06PCTThe ultrasound imaging device is used to capture images of the ovaries before and during the ovarian stimulation process. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The AI algorithm is integrated into the computer system and analyzes the ultrasound images to predict the response to ovarian stimulation. The AI algorithm analyzes the ultrasound images based on various parameters, including the size and number of follicles in the ovaries. The algorithm compares the patient's ultrasound images to a pre- defined database of images and predicts the patient's response to ovarian stimulation. Based on the prediction, the AI algorithm suggests a personalized stimulation protocol for the patient. The system and method of the present invention provide several advantages over conventional ovarian stimulation methods. It allows for the personalization of ovarian stimulation protocols, which can increase the efficiency of the treatment and reduce the risk of side effects. Additionally, it reduces the trial-and-error process involved in ovarian stimulation and can lead to improved patient outcomes. 1. A system for personalized ovarian stimulation using AI and ultrasound, comprising: • An ultrasound imaging device for capturing images of the ovaries before and during ovarian stimulation; • A computer system connected to the ultrasound imaging device for receiving the ultrasound images; • An AI algorithm integrated into the computer system for analyzing the ultrasound images to predict the response to ovarian stimulation. 2. The system of 1, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the size and number of follicles in the ovaries. 3. The system of 1, wherein the AI algorithm compares the patient's ultrasound images to a pre-defined database of images and predicts the patient's response to ovarian stimulation. 4. The system of 1, wherein the Al algorithm suggests a personalized stimulation protocol for the patient based on the prediction. 5. A method for personalized ovarian stimulation using AI and ultrasound, comprising: • Capturing ultrasound images of the ovaries before and during ovarian stimulation using an ultrasound imaging device; • Transmitting the ultrasound images to a computer system connected to the ultrasound 66 9354.06PCTimaging device; • Analyzing the ultrasound images using an AI algorithm integrated into the computer system to predict the response to ovarian stimulation; • Suggesting a personalized stimulation protocol for the patient based on the prediction. 6. The method of 5, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the size and number of follicles in the ovaries. 7. The method of 5, wherein the AI algorithm compares the patient's ultrasound images to a pre-defined database of images and predicts the patient's response to ovarian stimulation. 8. The method of 5, further comprising administering the personalized stimulation protocol to the patient undergoing ovarian stimulation. System and Method for Monitoring Follicle Development using AI and Ultrasound In the IVF process, monitoring follicle development is crucial for determfaing the optimal time for egg retrieval. However, conventional monitoring methods rely on manual examination of ultrasound images, which can be time-consuming and prone to errors. The present invention aims to address these issues by providing a system and method for monitoring follicle development using AI algorithms and ultrasound. The present invention provides a system and method for monitoring follicle development using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the ovaries during the IVF process. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The AI algorithm is integrated into the computer system and analyzes the ultrasound images to monitor follicle development. The AI algorithm analyzes the ultrasound images based on various parameters, including the size and number of follicles in the ovaries. The algorithm predicts the optimal time for egg retrieval based on the follicle development data. The system and method of the present invention provide several advantages over conventional follicle development monitoring methods. It reduces the need for manual examination of ultrasound images and increases the accuracy and precision of the monitoring process. This can lead to improved success rates of IVF cycles and reduce the need for additional procedures. 67 9354.06PCT1. A system for monitoring follicle development using AI and ultrasound, comprising: • An ultrasound imaging device for capturing images of the ovaries during the IVF process; • A computer system connected to the ultrasound imaging device for receiving the ultrasound images; • An AI algorithm integrated into the computer system for analyzing the ultrasound images to monitor follicle development. 2. The system of 1, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the size and number of follicles in the ovaries. 3. The system of 1, wherein the Al algorithm predicts the optimal time for egg retrieval based on the follicle development data. 4. A method for monitoring follicle development using AI and ultrasound, comprising: • Capturing ultrasound images of the ovaries during the IVF process using an ultrasound imaging device; • Transmitting the ultrasound images to a computer system connected to the ultrasound imaging device; • Analyzing the ultrasound images using an AI algorithm integrated into the computer system to monitor follicle development; • Predicting the optimal time for egg retrieval based on the follicle development data. 5. The method of 4, wherein the Al algorithm analyzes the ultrasound images based on various parameters, including the size and number of follicles in the ovaries. 6. The method of 4, wherein the AI algorithm predicts the optimal time for egg retrieval based on the follicle development data. 7. The method of 4, further comprising retrieving eggs from the patient's ovaries at the predicted optimal time for egg retrieval. System and Method for Diagnosing Infertility using AI and Ultrasound Infertility is a common issue that affects many couples worldwide. The diagnosis of infertility often involves manual examination of ultrasound images of the reproductive system. However, this method can be subjective and prone to errors. The present invention aims to address these issues by providing a system and method for diagnosing infertility using AI algorithms and ultrasound. 68 9354.06PCTThe present invention provides a system and method for diagnosing infertility using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the reproductive system. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The AI algorithm is integrated into the computer system and analyzes the ultrasound images to diagnose infertility. The AI algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. The algorithm compares the ultrasound images to a pre-defined set of criteria to identify any abnormalities or issues that may be causing infertility. The system and method of the present invention provide several advantages over conventional infertility diagnosis methods. It reduces the subjectivity and variability associated with manual examination of ultrasound images and increases the accuracy and precision of the diagnosis process. This, in turn, can lead to more targeted and effective treatments. 1. A system for diagnosing infertility using AI and ultrasound, comprising: • An ultrasound imaging device for capturing images of the reproductive system; • A computer system connected to the ultrasound imaging device for receiving the ultrasound images; • An AI algorithm integrated into the computer system for analyzing the ultrasound images to diagnose infertility. 2. The system of 1, wherein the Al algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. 3. The system of 1, wherein the AI algorithm compares the ultrasound images to a pre- defined set of criteria to identify any abnormalities or issues that may be causing infertility. 4. A method for diagnosing infertility using Al and ultrasound, comprising: • Capturing ultrasound images of the reproductive system using an ultrasound imaging device; • Transmitting the ultrasound images to a computer system connected to the ultrasound imaging device; 69 9354.06PCT• Analyzing the ultrasound images using an AI algorithm integrated into the computer system to diagnose infertility; • Identifying any abnom1alities or issues that may be causing infertility. 5. The method of 4, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. 6. The method of 4, wherein the AI algorithm compares the ultrasound images to a pre- defined set of criteria to identify any abnormalities or issues that may be causing infertility. 7. The method of 4, further comprising providing targeted and effective treatments based on the diagnosis of infertility. System and Method for Patient Selection using AI and Ultrasound for IVF IVF is a complex medical procedure that requires careful patient selection to ensure successful outcomes. Patient selection involves various factors, such as age, medical history, and ultrasound images. However, conventional patient selection methods can be subjective and prone to errors. The present invention aims to address these issues by providing a system and method for patient selection using AI algorithms and ultrasound. The present invention provides a system and method for patient selection using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the reproductive system of the patient. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The Al algorithm is integrated into the computer system and analyzes the ultrasound images to select the most suitable patients for IVF. The AI algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. The algorithm compares the ultrasound images to a pre-defined set of criteria to identify any factors that may affect the success of the treatment. Based on the analysis, the AI algorithm suggests the most suitable patients for IVF. The system and method of the present invention provide several advantages over conventional patient selection methods. It reduces the subjectivity and variability 70 9354.06PCTassociated with manual examination of ultrasound images and increases the accuracy and precision of the selection process. This, in tum, can help avoid unnecessary procedures and increase the overall success rates of IVF. 1. A system for patient selection using AI and ultrasound for IVF, comprising: • An ultrasound imaging device for capturing images of the reproductive system of the patient; • A computer system connected to the ultrasound imaging device for receiving the ultrasound images; • An AI algorithm integrated into the computer system for analyzing the ultrasound images to select the most suitable patients for IVF. 2. The system of 1, wherein the Al algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. 3. The system of 1, wherein the AI algorithm compares the ultrasound images to a pre- defined set of criteria to identify any factors that may affect the success of the treatment. 4. A method for patient selection using AI and ultrasound for IVF, comprising: • Capturing ultrasound images of the reproductive system of the patient using an ultrasound imaging device; • Transmitting the ultrasound images to a computer system connected to the ultrasound imaging device; • Analyzing the ultrasound images using an AI algorithm integrated into the computer system to select the most suitable patients for IVF; • Suggesting the most suitable patients for IVF based on the analysis. 5. The method of 4, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. 6. The method of 4, wherein the AI algorithm compares the ultrasound images to a pre- defined set of criteria to identify any factors that may affect the success of the treatment. 7. The method of 4, further comprising performing IVF on the selected patients. 71 9354.06PCTSystem and Method for Real-time Monitoring using AI and Ultrasound during the IVF Process The IVF process involves several critical steps that require careful monitoring to ensure successful outcomes. Real-time monitoring of ultrasound images during the IVF process is necessary to identify any potential issues or complications. However, conventional monitoring methods can be time- consuming and prone to errors. The present invention aims to address these issues by providing a system and method for real-time monitoring using AI algorithms and ultrasound during the IVF process. The present invention provides a system and method for real-time monitoring using AI algorithms and ultrasound during the IVF process. The system comprises an ultrasound imaging device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the reproductive system during the IVF process. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The AI algorithm is integrated into the computer system and analyzes the ultrasound images in real-time to identify any potential issues or complications. The AI algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. The algorithm compares the ultrasound images to a pre-defined set of criteria and alerts doctors to any potential issues or complications. The system and method of the present invention provide several advantages over conventional monitoring methods. It allows for real-time monitoring of ultrasound images during tbe IVF process, which can lead to faster and more effective interventions. This, in turn, can reduce the risk of complications and improve patient outcomes. 1. A system for real-time monitoring using AI and ultrasound during the IVF process, comprising: • An ultrasound imaging device for capturing images of the reproductive system during the IVF process; • A computer system connected to the ultrasound imaging device for receiving the ultrasound images; • An AI algorithm integrated into the computer system for analyzing the ultrasound images in real-time to identify any potential issues or complications. 72 9354.06PCT2. The system of 1, wherein the Al algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. 3. The system of 1, wherein the AI algorithm compares the ultrasound images to a pre- defined set of criteria and alerts doctors to any potential issues or complications. 4. A method for real-time monitoring using AI and ultrasound during the TVF process, comprising: • Capturing ultrasound images of the reproductive system during the NF process using an ultrasound imaging device; • Transmitting the ultrasound images to a computer system connected to the ultrasound imaging device; • Analyzing the ultrasound images in real-time using an Al algorithm integrated into the computer system to identify any potential issues or complications; • Alerting doctors to any potential issues or complications based on the analysis. 5. The method of 4, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the size, shape, and morphology of the reproductive system. 6. The method of 4, wherein the Al algorithm compares the ultrasound images to a pre- defined set of criteria and alerts doctors to any potential issues or complications. 7. The method of 4, further comprising performing faster and more effective interventions to reduce the risk of complications and improve patient outcomes. System and Method for Predicting IVF Success Rates using AI and Ultrasound IVF is a complex medical procedure that requires careful consideration of various factors, such as age, medical history, and ultrasound images, to predict success rates. However, conventional prediction methods can be subjective and prone to errors. The present invention aims to address these issues by providing a system and method for predicting IVF success rates using AI algorithms and ultrasound. The present invention provides a system and method for predicting IVF success rates using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an Al algorithm. 73 9354.06PCTThe ultrasound imaging device is used to capture images of the reproductive system. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The AI algorithm is integrated into the computer system and analyzes the ultrasound images and other patient data to predict the success rates of IVF cycles. The AI algorithm analyzes the ultrasound images and other patient data based on various parameters, including the age of the patient, medical history, and the size, shape, and morphology of the reproductive system. The algorithm compares the data to a pre- defined set of criteria to predict the success rates of IVF cycles. The system and method of the present invention provide several advantages over conventional prediction methods. It reduces the subjectivity and variability associated with manual examination of ultrasound images and increases the accuracy and precision of the prediction process. This, in tum, can help patients and doctors make more informed decisions about treatment options and improve the overall success rates of IVF. 1. A system for predicting IVF success rates using AI and ultrasound, comprising: • An ultrasound imaging device for capturing images of the reproductive system; • A computer system connected to the ultrasound imaging device for receiving the ultrasound images and other patient data; • An AI algorithm integrated into the computer system for analyzing the ultrasound images and other patient data to predict the success rates of IVF cycles. 2. The system of 1, wherein the Al algorithm analyzes the ultrasound images and other patient data based on various parameters, including the age of the patient, medical history, and the size, shape, and morphology of the reproductive system. 3. The system of 1, wherein the Al algorithm compares the data to a pre-defined set of criteria to predict the success rates of IVF cycles. 4. A method for predicting IVF success rates using AI and ultrasound, comprising: • Capturing ultrasound images of the reproductive system using an ultrasound imaging device; • Transmitting the ultrasound images and other patient data to a computer system connected to the ultrasound imaging device; • Analyzing the ultrasound images and other patient data using an Al algorithm integrated into the computer system to predict the success rates of IVF cycles; 74 9354.06PCT• Predicting the success rates of IVF cycles based on the analysis. 5. The method of 4, wherein the AI algorithm analyzes the ultrasound images and other patient data based on various parameters, including the age of the patient, medical history, and the size, shape, and morphology of the reproductive system. 6. The method of 4, wherein the AI algorithm compares the data to a pre-defined set of criteria to predict the success rates of lVF cycles. 7. The method of 4, further comprising providing patients and doctors with more informed decisions about treatment options based on the prediction of success rates. System and Method for Endometrial Receptivity Analysis using AI and Ultrasound Embryo transfer timing is a critical factor in IVF success rates. The optimal time for embryo transfer is determined by the endometrial receptivity of the uterus. Conventional methods for assessing endometria] receptivity rely on subjective visual interpretation of ultrasound images. However, such methods can be prone to errors. The present invention aims to address these issues by providing a system and method for endometrial receptivity analysis using AI algorithms and ultrasound. The present invention provides a system and method for endometrial receptivity analysis using Al algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the uterine lining. The computer system is connected to the ultrasound imaging device and receives the ultrasound images. The AI algorithm is integrated into the computer system and analyzes the ultrasound images to determine the optimal time for embryo transfer. The AI algorithm analyzes the ultrasound images based on various parameters, including the thickness, pattern, and vascularity of the uterine lining. The algorithm compares the ultrasound images to a pre- defined set of criteria to determine the optimal time for embryo transfer. The system and method of the present invention provide several advantages over conventional methods for assessing endometrial receptivity. Tt reduces the subjectivity and variability associated with manual examination of ultrasound images and increases the accuracy and precision of the analysis. This, in turn, can improve the chances of successful implantation and reduce the risk of miscarriage. 1. A system for endometrial receptivity analysis using AI and ultrasound, comprising: • An ultrasound imaging device for capturing images of the uterine lining; 75 9354.06PCT• A computer system connected to the ultrasound imaging device for receiving the ultrasound images; • An AI algorithm integrated into the computer system for analyzing the ultrasound images to determine the optimal time for embryo transfer. 2. The system of 1, wherein the AI algorithm analyzes the ultrasound images based on various parameters, including the thickness, pattern, and vascularity of the uterine lining. 3. The system of 1, wherein the AI algorithm compares the ultrasound images to a pre- defined set of criteria to determine the optimal time for embryo transfer. 4. A method for endometrial receptivity analysis using AI and ultrasound, comprising: • Capturing ultrasound images of the uterine lining using an ultrasound imaging device; • Transmitting the ultrasound images to a computer system connected to the ultrasound imaging device; • Analyzing the ultrasound images using an AI algorithm integrated into the computer system to determine the optimal time for embryo transfer; • Determining the optimal time for embryo transfer based on the analysis. 5. The method of 4, wherein the AJ algorithm analyzes the ultrasound images based on various parameters, including the thickness, pattern, and vascularity of the uterine lining. 6. The method of 4, wherein the AI algorithm compares the ultrasound images to a pre- defined set of criteria to determine the optimal time for embryo transfer. 7. The method of 4, further comprising performing embryo transfer at the optimal time determined by the analysis to improve the chances of successful implantation and reduce the risk of miscarriage. System and Method for Hormone Monitoring using AI and Ultrasound Monitoring hormone levels during IVF pregnancy is critical for detecting any issues or complications early on and allowing for prompt intervention to prevent miscarriage. Convention.al hormone monitoring methods rely on manual analysis of ultrasound images and blood tests, which can be time- consuming and prone to errors. The present invention aims to address these issues by providing a system and method for hormone monitoring using AI algorithms and ultrasound. 76 9354.06PCTThe present invention provides a system and method for hormone monitoring using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a blood testing device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the reproductive system. The blood testing device is used to measure hom1one levels in the patient's blood. The computer system is connected to the ultrasound imaging device and the blood testing device and receives the ultrasound images and hormone level data. The AI algorithm is integrated into the computer system and analyzes the ultrasound images and hormone level data to determine if the pregnancy is progressing as expected. The AI algorithm analyzes the ultrasound images and hormone level data based on various parameters, including the growth of the fetus, the thickness of the uterine lining, and the levels of hormones such as estrogen and progesterone. The algorithm compares the data to a pre-defined set of criteria to detect any issues or complications early on. The system and method of the present invention provide several advantages over conventional hormone monitoring methods. It reduces the subjectivity and variability associated with manual examination of ultrasound images and blood tests and increases the accuracy and precision of the analysis. This, in turn, can help detect any issues or complications early on and allow for prompt intervention to prevent miscarriage. 1. A system for hormone monitoring using Al and ultrasound, comprising: • An ultrasound imaging device for capturing images of the reproductive system; • A blood testing device for measuring hormone levels in the patient's blood; • A computer system connected to the ultrasound imaging device and the blood testing device for receiving the ultrasound images and hormone level data; • An AI algorithm integrated into the computer system for analyzing the ultrasound images and hormone level data to determine if the pregnancy is progressing as expected. 2. The system of 1, wherein the Al algorithm analyzes the ultrasound images and hormone level data based on various parameters, including the growth of the fetus, the thickness of the uterine lining, and the levels of hormones such as estrogen and progesterone. 3. The system of 1, wherein the Al algorithm compares the data to a pre-defined set of criteria to detect any issues or complications early on. 4. A method for hormone monitoring using Al and ultrasound, comprising: 77 9354.06PCT• Capturing ultrasound images of the reproductive system using an ultrasound imaging device; • Measuring hormone levels in the patient's blood using a blood testing device; • Transmitting the ultrasound images and hormone level data to a computer system connected to the ultrasound imaging device and the blood testing device; • Analyzing the ultrasound images and hormone level data using an AI algorithm integrated into the computer system to determine if the pregnancy is progressing as expected; • Detecting any issues or complications early on based on the analysis. 5. The method of 4, wherein the AI algorithm analyzes the ultrasound images and hormone level data based on various parameters, including the growth of the fetus, the thickness of the uterine lining, and the levels of hormones such as estrogen and progesterone. 6. The method of 4, wherein the Al algorithm compares the data to a pre-defined set of criteria to detect any issues or complications early on. 7. The method of 4, further comprising intervening promptly to prevent miscarriage if any issues or complications System and Method for Personalized Treatment using AI and Ultrasound IVF treatments can vary greatly depending on the patient's individual characteristics and circumstances. Personalized treatment plans can improve the chances of successful pregnancy and reduce the risk of miscarriage. However, developing personalized treatment plans can be time-consuming and challenging. The present invention aims to address these issues by providing a system and method for personalized treatment using AI algorithms and ultrasound. The present invention provides a system and method for personalized treatment using AI algorithms and ultrasound. The system comprises an ultrasound imaging device, a computer system, and an AI algorithm. The ultrasound imaging device is used to capture images of the reproductive system. The computer system is connected to the ultrasound imaging device and receives the ultrasound images and other patient data, such as medical history and genetic information. The AI algorithm is integrated into the computer system and analyzes the ultrasound images and patient data to develop personalized treatment plans. 78 9354.06PCTThe Al algorithm analyzes the ultrasound images and patient data based on various parameters, such as the patient's age, medical history, and genetic information. The algorithm compares the data to a pre- defined set of criteria to develop personalized treatment plans that take into account any factors that may increase the risk of miscarriage. The system and method of the present invention provide several advantages over conventional methods for developing personalized treatment plans. It reduces the time and effort required to develop personalized treatment plans and increases the accuracy and precision of the analysis. This, in turn, can improve the chances of successful pregnancy and reduce the risk of miscarriage. 1. A system for personalized treatment using AI and ultrasound, comprising: • An ultrasound imaging device for capturing images of the reproductive system; • A computer system connected to the ultrasound imaging device for receiving the ultrasound images and other patient data, such as medical history and genetic information; • An AI algorithm integrated into the computer system for analyzing the ultrasound images and patient data to develop personalized treatment plans. 2. The system of 1, wherein the AI algorithm analyzes the ultrasound images and patient data based on various parameters, such as the patient's age, medical history, and genetic information. 3. The system of 1, wherein the Al algorithm compares the data to a pre-defined set of criteria to develop personalized treatment plans that take into account any factors that may increase the risk of miscarriage. 4. A method for personalized treatment using Al and ultrasound, comprising: • Capturing ultrasound images of the reproductive system using an ultrasound imaging device; • Collecting other patient data, such as medical history and genetic information; • Transmitting the ultrasound images and patient data to a computer system connected to the ultrasound imaging device; • Analyzing the ultrasound images and patient data using an AI algorithm integrated into the computer system to develop personalized treatment plans that take into account any factors that may increase the risk of miscarriage. 79 9354.06PCTThe method of 4, wherein the AI algorithm analyzes the ultrasound images and patient data based on various parameters, such as the patient's age, medical history, and genetic information. The method of 4, wherein the AI algorithm compares the data to a pre-defined set of criteria to develop personalized treatment plans that take into account any factors that may increase the risk of miscarriage. The method of 4, further comprising implementing the personalized treatment plans to improve the chances of successful pregnancy and reduce the risk of miscarriage. AI-assisted Ultrasound Diagnosis and Treatment for Hypertension Prediction The present invention provides an AI-assisted system for predicting hypertension and developing treatments to change the predicted outcome. The system utilizes ultrasound imaging and a database of ultrasound images and hypertension treatment outcomes to provide accurate and efficient prediction and treatment recommendations. The system is capable of analyzing ultrasound images to detect early signs of hypertension and comparing them to a database of previous images to determine the most effective treatment to change the predicted outcome. Hypertension is a prevalent medical condition that can lead to cardiovascular disease and other serious health problems. Early detection and effective treatment are crucial to preventing hypertension-related complications. Ultrasound imaging has been used to diagnose and monitor hypertension, but traditional diagnostic methods can be time- consuming and often require invasive procedures. AI-assisted prediction and treatment can help improve the accuracy and efficiency of hypertension predic6on and treatment. The AI-assisted ultrasound diagnosis and treatment system includes an ultrasound imaging device, a database of ultrasound images and hypertension treatment outcomes, and an AI algorithm that analyzes ultrasound images to detect early signs of hypertension and recommend the most effective treatment to change the predicted outcome. The AI algorithm is trained on a dataset of ultrasound images and corresponding hypertension treatment outcomes, which are used to develop predictive models for hypertension prediction and treatment. The system operates as follows: a patient is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which compares them to the database of previous images to identify early signs of hypertension. The algorithm 80 9354.06PCTthen recommends the most effective treatment to change the predicted outcome based on the patient's individual characteristics and the characteristics of the hypertension. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound diagnosis and treatment system provides several advantages over traditional hypertension prediction and treatment methods. It is non-invasive and does not require blood or tissue samples, which can be uncomfortable and time-consuming for the patient. It is also more accurate and efficient than traditional methods, allowing for faster and more effective treatment. Additionally, it can help reduce the risk of hypertension-related complications by predicting the outcome and recommending the most effective treatment to change the predicted outcome. The AI-assisted ultrasound diagnosis and treatment system provides a novel and effective method for the prediction and treatment of hypertension. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient prediction and treatment recommendations, in1proving patient outcomes and reducing the risk of hypertension-related complications. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting hypertension and developing treatments to change the predicted outcome, comprising: • an ultrasound imaging device for generating ultrasound images of a patient; • a database of ultrasound images and corresponding hypertension treatment outcomes; • an AI algorithm trained on the database of ultrasound images and hypertension treatment outcomes for identifying early signs of hypertension in the ultrasound images and recommending the most effective treatment to change the predicted outcome of hypertension. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 4. The system of 1, wherein the AI algorithm utilizes deep learning techniques to 81 9354.06PCTanalyze the ultrasound images and recommend hypertension treatments. The system of 1, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when recommending hypertension treatments. Al-assisted Ultrasound Diagnosis and Treatment for Pancreatic Cancer Prediction The present invention provides an AI-assisted system for predicting pancreatic cancer and developing pharmaceuticals to improve the predicted outcome. The system utilizes ultrasound imaging and a database of ultrasound images and pancreatic cancer treatment outcomes to provide accurate and efficient prediction and treatment recommendations. The system is capable of analyzing ultrasound images to detect early signs of pancreatic cancer and comparing them to a database of previous images to determine the most effective pharmaceutical treatment to improve the predicted outcome. Pancreatic cancer is a deadly and difficult to diagnose disease that can be hard to detect in its early stages. Early detection and effective treatment are crucial to improving patient outcomes. Ultrasound imaging has been used to diagnose and monitor pancreatic cancer, but traditional diagnostic methods can be time-consuming and often require invasive procedures. AI-assisted prediction and treatment can help improve the accuracy and efficiency of pancreatic cancer prediction and treatment. The AI-assisted ultrasound diagnosis and treatment system includes an ultrasound imaging device, a database of ultrasound images and pancreatic cancer treatment outcomes, and an AI algorithm that analyzes ultrasound images to detect early signs of pancreatic cancer and recommend the most effective pharmaceutical treatment to improve the predicted outcome. The AI algorithm is trained on a dataset of ultrasound images and corresponding pancreatic cancer treatment outcomes, which are used to develop predictive models for pancreatic cancer prediction and treatment. The system operates as follows: a patient is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which compares them to the database of previous images to identify early signs of pancreatic cancer. The algorithm then recommends the most effective pharmaceutical treatment to improve the predicted outcome based on the patient's individual characteristics and the characteristics of the pancreatic cancer. 82 9354.06PCTThe system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound diagnosis and treatment system provides several advantages over traditional pancreatic cancer prediction and treatment methods. It is non-invasive and does not require blood or tissue samples, which can be uncomfortable and time-consuming for the patient. It is also more accurate and efficient than traditional methods, allowing for faster and more effective treatment. Additionally, it can help improve patient outcomes by predicting the outcome and recommending the most effective pharmaceutical treatment to improve the predicted outcome. The AI-assisted ultrasound diagnosis and treatment system provides a novel and effective method for the prediction and treatment of pancreatic cancer. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient prediction and treatment recommendations, improving patient outcomes and reducing the risk of pancreatic cancer-related complications. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting pancreatic cancer and developing pharmaceuticals to improve the predicted outcome, comprising: • an ultrasound imaging device for generating ultrasound images of a patient; • a database of ultrasound images and corresponding pancreatic cancer treatment outcomes; • an AI algorithm trained on the database of ultrasound images and pancreatic cancer treatment outcomes for identifying early signs of pancreatic cancer in the ultrasound images and recommending the most effective pharmaceutical treatment to improve the predicted outcome of pancreatic cancer. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 4. The system of 1, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and recommend pancreatic cancer treatments. 83 9354.06PCTAI-Assisted Ultrasound Diagnosis and Treatment for Blood Type and Blood Transfusion Compatibility The present invention provides an AI-assisted system for determining blood type and potential blood transfusion issues using ultrasound imaging. The system utilizes ultrasound imaging and a database of ultrasound images and blood transfusion outcomes to provide accurate and efficient diagnosis and treatment recommendations. The system is capable of analyzing ultrasound images to determine blood type and identify potential blood transfusion compatibility issues. Blood transfusions are a common medical treatment that require accurate blood typing and identification of potential blood transfusion compatibility issues to ensure patient safety. Traditional blood typing methods can be time-consuming and may not be readily available in some medical settings. AI-assisted blood typing and identification of potential blood transfusion compatibility issues using ultrasound imaging can help improve the accuracy and efficiency of this critical medical treatment. The AI-assisted ultrasound diagnosis and treatment system includes an ultrasound imaging device, a database of ultrasound images and blood transfusion outcomes, and an AI algorithm that analyzes ultrasound images to determine blood type and identify potential blood transfusion compatibility issues. The AI algorithm is trained on a dataset of ultrasound images and corresponding blood transfusion outcomes, which are used to develop predictive models for blood typing and potential blood transfusion compatibility issues. The system operates as follows: a patient is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which determines the blood type and identifies potential blood transfusion compatibility issues. The algorithm then recommends the most appropriate blood transfusion treatment based on the patient's individual characteristics and the characteristics of the blood transfusion. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound diagnosis and treatment system provides several advantages over traditional blood typing and identification of potential blood transfusion compatibility issues methods. It is non-invasive and does not require blood or tissue 84 9354.06PCTsamples, which can be uncomfortable and time-consuming for the patient. It is also more accurate and efficient than traditional methods, allowing for faster and more effective treatment. Additionally, it can help improve patient outcomes by identifying potential blood transfusion compatibility issues and recommending the most appropriate blood transfusion treatment. The AI-assisted ultrasound diagnosis and treatment system provides a novel and effective method for blood typing and identification of potential blood transfusion compatibility issues. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient diagnosis and treatment recommendations, improving patient outcomes and reducing the risk of blood transfusion-related complications. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for determining blood type and potential blood transfusion compatibility issues using ultrasound imaging, comprising: • an ultrasound imaging device for generating ultrasound images of a patient; • a database of ultrasound images and corresponding blood transfusion outcomes; • an AI algorithm trained on the database of ultrasound images and blood transfusion outcomes for determining blood type and identifying potential blood transfusion compatibility issues in the ultrasound images. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 4. The system of 1, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and determine blood type and potential blood transfusion compatibility issues. 5. The system of 1, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when determining blood type and AI-Assisted Ultrasound Triage System for Emergency Medical Care The present invention provides an AI-assisted system for performing triage of patients in an emergency room or clinic using ultrasound imaging. The system utilizes ultrasound imaging and a database of ultrasound images and emergency medical 85 9354.06PCToutcomes to provide accurate and efficient patient triage and treatment recommendations. The system is capable of analyzing ultrasound images to identify critical and urgent medical conditions and recommending the most appropriate medical interventions. Emergency medical care requires rapid and accurate diagnosis and treatment to improve patient outcomes. Traditional triage methods can be time-consuming and may not provide accurate diagnoses in all cases. AI-assisted triage using ultrasound imaging can help improve the accuracy and efficiency of emergency medical care and lead to improved patient outcomes. The AI-assisted ultrasound triage system includes an ultrasound imaging device, a database of ultrasound images and emergency medical outcomes, and an AI algorithm that analyzes ultrasound images to identify critical and urgent medical conditions and recommend the most appropriate medical interventions. The AI algorithm is trained on a dataset of ultrasound images and corresponding emergency medical outcomes, which are used to develop predictive models for emergency medical care triage and treatment. The system operates as follows: a patient is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which identifies critical and urgent medical conditions and recommends the most appropriate medical interventions based on the patient's individual characteristics and the characteristics of the medical condition. The system can be used in various medical settings, including emergency rooms, urgent care clinics, and other medical facilities. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound triage system provides several advantages over traditional emergency medical care triage methods. It is non-invasive and can rapidly identify critical and urgent medical conditions, allowing for faster and more effective treatment. Additionally, it can help improve patient outcomes by recommending the most appropriate medical interventions based on the patient's individual characteristics and the characteristics of the medical condition. The AI-assisted ultrasound triage system provides a novel and effective method for emergency medical care triage and treatment. By utilizing ultrasound imaging and Al 86 9354.06PCTalgorithms, the system can provide accurate and efficient patient triage and treatment recommendations, improving patient outcomes and reducing the risk of emergency medical complications. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for performing triage of patients in an emergency room or clinic using ultrasound imaging, comprising: • an ultrasound imaging device for generating ultrasound images of a patient; • a database of ultrasound images and corresponding emergency medical outcomes; • an AI algorithm trained on the database of ultrasound images and emergency medical outcomes for identifying critical and urgent medical conditions in the ultrasound images and recommending the most appropriate medical interventions. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 4. The system of 1, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and recommend emergency medical interventions. 5. The system of 1, wherein the Al algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when recommending emergency medical interventions. 6. The system of 1, wherein the AI algorithm is capable of providing a priority level for each recommended medical intervention based on the urgency of the medical condition. 7. The system of 1, wherein the database of ultrasound images and corresponding emergency medical outcomes includes images and outcomes from a variety AI-Assisted Ultrasound Surgical Planning System The present invention provides an Al-assisted system for surgical planning using ultrasound imaging. The system utilizes ultrasound imaging and a database of ultrasound images and surgical outcomes to provide accurate and efficient surgical planning recommendations. The system is capable of analyzing ultrasound images to identify anatomical structures and recommend the most appropriate surgical techniques and procedures. 87 9354.06PCTSurgical planning is a critical component of successful surgeries. Traditional surgical planning methods can be time-consuming and may not provide accurate diagnoses in all cases. AI- assisted surgical planning using ultrasound imaging can help improve the accuracy and efficiency of surgical planning and lead to improved surgical outcomes. The AI-assisted ultrasound surgical planning system includes an ultrasound imaging device, a database of ultrasound images and surgical outcomes, and an AI algorithm that analyzes ultrasound images to identify anatomical structures and recommend the most appropriate surgical techniques and procedures. The AI algorithm is trained on a dataset of ultrasound images and corresponding surgical outcomes, which are used to develop predictive models for surgical planning and treatment. The system operates as follows: a patient is examined using an ultrasound imaging device. The resulting images are analyzed by the Al algorithm, which identifies anatomical structures and recommends the most appropriate surgical techniques and procedures based on the patient's individual characteristics and the characteristics of the surgical condition. The system can be used in various surgical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing surgical equipment and work flows, and can be used by surgical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound surgical planning system provides several advantages over traditional surgical planning methods. It is non-invasive and can rapidly identify anatomical structures, allowing for faster and more effective surgical planning. Additionally, it can help improve surgical outcomes by recommending the most appropriate surgical techniques and procedures based on the patient's individual characteristics and the characteristics of the surgical condition. The AI-assisted ultrasound surgical planning system provides a novel and effective method for surgical planning and treatment. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient surgical planning recommendations, improving surgical outcomes and reducing the risk of surgical complications. The system can be used in various surgical settings and can be integrated into existing surgical equipment and workflows, making it accessible to surgical professionals with various levels of experience and training. 1. A system for surgical planning using ultrasound imaging, comprising: • an ultrasound imaging device for generating ultrasound images of a patient; 88 9354.06PCT• a database of ultrasound images and corresponding surgical outcomes; • an Al algorithm trained on the database of ultrasound images and surgical outcomes for identifying anatomical structures in the ultrasound images and recommending the most appropriate surgical techniques and procedures. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing surgical equipment. 4. The system of 1, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and recommend surgical techniques and procedures. 5. The system of 1, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when recommending surgical techniques and procedures. 6. The system of 1, wherein the Al algorithm is capable of providing a priority level for each recommended surgical technique and procedure based on the urgency of the surgical condition. 7. The system of 1, wherein the database of ultrasound images and corresponding surgical outcomes includes images and outcomes from a variety of surgical conditions and procedures. AI-Assisted Ultrasound Organ Transplant Planning and Monitoring System The present invention provides an Al-assisted system for planning and monitoring organ transplants using ultrasound imaging. The system utilizes ultrasound imaging and a database of ultrasound images and transplant outcomes to provide accurate and efficient transplant planning and monitoring recommendations. The system is capable of analyzing ultrasound images to identify organ structures and predict organ rejection in transplant patients. Organ transplants are a critical medical treatment that require accurate planning and monitoring to improve patient outcomes. Traditional organ transplant planning and monitoring methods can be time-consuming and may not provide accurate diagnoses in all cases. AI-assisted planning and monitoring using ultrasound imaging can help improve the accuracy and efficiency of organ transplants and lead to improved patient outcomes. The Al-assisted ultrasound organ transplant planning and monitoring system includes an ultrasound imaging device, a database of ultrasound images and transplant outcomes, and 89 9354.06PCTan AI algorithm that analyzes ultrasound images to identify organ structures and predict organ rejection in transplant patients. The AI algorithm is trained on a dataset of ultrasound images and corresponding transplant outcomes, which are used to develop predictive models for transplant planning and monitoring. The system operates as follows: a patient receiving an organ transplant is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which identifies organ structures and predicts organ rejection based on the patient's individual characteristics and the characteristics of the transplants. The system can be used in various medical settings, including hospitals and clinics. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound organ transplant planning and monitoring system provides several advantages over traditional organ transplant planning and monitoring methods. It is non- invasive and can rapidly identify organ structures and predict organ rejection, allowing for faster and more effective transplant planning and monitoring. Additionally, it can help improve patient outcomes by predicting potential organ rejection and recommending the most appropriate medical interventions. The AI-assisted ultrasound organ transplant planning and monitoring system provides a novel and effective method for transplant planning and monitoring. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient transplant planning and monitoring recommendations, improving patient outcomes and reducing the risk of transplant-related complications. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for planning and monitoring organ transplants using ultrasound imaging, comprising: • an ultrasound imaging device for generating ultrasound images of a patient receiving an organ transplant; • a database of ultrasound images and corresponding transplant outcomes; • an Al algorithm trained on the database of ultrasound images and transplant outcomes for identifying organ structures and predicting organ rejection in transplant patients. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 90 9354.06PCTThe system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. The system of 1, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and predict organ rejection in transplant patients. The system of 1, wherein the Al algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when predicting organ rejection. The system of I, wherein the AI algorithm is capable of recommending the most appropriate medical interventions based on the predicted organ rejection. The system of 1, wherein the database of ultrasound images and corresponding transplant outcomes includes images and outcomes from a variety of organ transplants and transplant- related complications. AI-Assisted Ultrasound Rehabilitation and Physical Therapy System The present invention provides an AI-assisted system for patient rehabilitation and physical therapy using ultrasound imaging. The system utilizes ultrasound imaging and a database of ultrasound images and rehabilitation outcomes to provide accurate and efficient rehabilitation and physical therapy recommendations. The system is capable of analyzing ultrasound images to identify muscle and joint structures and recommend the most appropriate rehabilitation and physical therapy techniques and exercises. Patient rehabilitation and physical therapy are critical components ofrecovery from injury or surgery. Traditional rehabilitation and physical therapy methods can be time- consuming and may not provide the maximum benefit to patients. AI-assisted rehabilitation and physical therapy using ultrasound imaging can help improve the accuracy and efficiency of rehabilitation and physical therapy and lead to improved patient outcomes. The AI-assisted ultrasound rehabilitation and physical therapy system includes an ultrasound imaging device, a database of ultrasound images and rehabilitation outcomes, and an Al algorithm that analyzes ultrasound images to identify muscle and joint structures and recommend the most appropriate rehabilitation and physical therapy techniques and exercises. The AI algorithm is trained on a dataset of ultrasound images and corresponding rehabilitation outcomes, which are used to develop predictive models for rehabilitation and physical therapy. 91 9354.06PCTThe system operates as follows: a patient undergoing rehabilitation or physical therapy is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which identifies muscle and joint structures and recommends the most appropriate rehabilitation and physical therapy techniques and exercises based on the patient's individual characteristics and the characteristics of the injury or surgery. The system can be used in various medical settings, including hospitals, clinics, and physical therapy centers. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound rehabilitation and physical therapy system provides several advantages over traditional rehabilitation and physical therapy methods. It is non-invasive and can rapidly identify muscle and joint structures, allowing for faster and more effective rehabilitation and physical therapy. Additionally, it can help improve patient outcomes by recommending the most appropriate rehabilitation and physical therapy techniques and exercises based on the patient's individual characteristics and the characteristics of the injury or surgery, reducing the risk of further injury. The AI-assisted ultrasound rehabilitation and physical therapy system provides a novel and effective method for rehabilitation and physical therapy. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient rehabilitation and physical therapy recommendations, improving patient outcomes and reducing the risk of further injury. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for patient rehabilitation and physical therapy using ultrasound imaging, comprising: • an ultrasound imaging device for generating ultrasound images of a patient undergoing rehabilitation or physical therapy; • a database of ultrasound images and corresponding rehabilitation outcomes; • an Al algorithm trained on the database of ultrasound images and rehabilitation outcomes for identifying muscle and joint structures in the ultrasound images and recommending the most appropriate rehabilitation and physical therapy techniques and 92 9354.06PCTexercises. The system of 1, wherein the ultrasound imaging device is a handheld device. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. The system of 1, wherein the AI algorithm utilizes deep learning techniques to analyze the ultrasound images and recommend rehabilitation and physical therapy techniques and exercises. The system of 1, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when recommending rehabilitation and physical therapy techniques and exercises. The system of 1, wherein the Al algorithm is capable of recommending the most appropriate rehabilitation and physical therapy techniques and exercises based on the characteristics of AI-Assisted Ultrasound Diagnostic System The present invention provides an AI-assisted system for diagnostic purposes using ultrasound imaging. The system utilizes ultrasound imaging and a database of ultrasound images and corresponding diagnoses to provide accurate and efficient diagnoses. The system is capable of analyzing ultrasound images to identify abnormal structures and recommend the most appropriate diagnosis based on the patient's individual characteristics. Diagnostic imaging is a critical component of medical treatment. Traditional diagnostic imaging methods can be time-consuming and may not provide accurate diagnoses in all cases. AI- assisted diagnostic imaging using ultrasound can help improve the accuracy and efficiency of diagnosis and lead to improved patient outcomes. The AI-assisted ultrasound diagnostic system includes an ultrasound imaging device, a database of ultrasound images and corresponding diagnoses, and an AI algorithm that analyzes ultrasound images to identify abnormal structures and recommend the most appropriate diagnosis based on the patient's individual characteristics. The AI algorithm is trained on a dataset of ultrasound images and corresponding diagnoses, which are used to develop predictive models for diagnosis. The system operates as follows: a patient is examined using an ultrasound imaging device. The resulting images are analyzed by the Al algorithm, which identifies 93 9354.06PCTabnormal structures and recommends the most appropriate diagnosis based on the patient's individual characteristics. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted ultrasound diagnostic system provides several advantages over traditional diagnostic imaging methods. It is non-invasive and can rapidly identify abnormal structures, allowing for faster and more accurate diagnosis. Additionally, it can help improve patient outcomes by recommending the most appropriate diagnosis based on the patient's individual characteristics, reducing the risk of misdiagnosis and delayed treatment. The AI-assisted ultrasound diagnostic system provides a novel and effective method for diagnosis. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient diagnoses, improving patient outcomes and reducing the risk of misdiagnosis and delayed treatment. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for diagnosis using ultrasound imaging, comprising: • an ultrasound imaging device for generating ultrasound images of a patient; • a database of ultrasound images and corresponding diagnoses; • an AI algorithm trained on the database of ultrasound images and diagnoses for identifying abnormal structures in the ultrasound images and recommending the most appropriate diagnosis based on the patient's individual characteristics. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 4. The system of 1, wherein the Al algorithm utilizes deep learning techniques to analyze the ultrasound images and recommend diagnoses. 5. The system of 1, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when recommending diagnoses. 6. The system of 1, wherein the database of ultrasound images and corresponding diagnoses includes images and diagnoses from a variety of medical conditions. 94 9354.06PCTAI-Assisted Post-Surgery Complication Prediction and Prevention System The present invention provides an AI-assisted system for predicting post-surgery complications and recommending preventative steps using patient data and machine learning algorithms. The system utilizes patient data and surgical outcomes to predict the likelihood of post- surgery complications and recommend preventative steps to minimize the risk of complications. Post-surgery complications are a major concern in medical treatment. Traditional post- surgery care methods can be time-consuming and may not provide the maximum benefit to patients. AI-assisted prediction and prevention of post-surgery complications can help improve patient outcomes and reduce the risk of complications. The Al-assisted post-surgery complication prediction and prevention system includes a patient data collection module, a surgical outcomes database, and an AI algorithm that utilizes machine learning techniques to predict the likelihood of post-surgery complications and recommend preventative steps based on the patient's individual characteristics. The patient data collection module collects patient data such as medical history, surgical procedure details, and vital signs. This information is used by the AI algorithm to generate a risk assessment for post-surgery complications. The surgical outcomes database includes information on surgical procedures, complications, and preventative measures. This information is used by the AI algorithm to recommend preventative steps based on the specific patient's risk assessment. The system operates as follows: after the completion of a surgery, the patient data is collected and used by the AI algorithm to generate a risk assessment for post-surgery complications. The AI algorithm then recommends preventative steps based on the patient's risk assessment and the surgical outcomes database. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted post-surgery complication prediction and prevention system provides several advantages over traditional post-surgery care methods. It utilizes patient data and machine learning algorithms to predict the likelihood of post- surgery complications and recommend preventative steps, improving patient outcomes and reducing the risk of complications. 95 9354.06PCTThe AI-assisted post-surgery complication prediction and prevention system provides a novel and effective method for post-surgery care. By utilizing patient data and machine learning algorithms, the system can predict the likelihood of post-surgery complications and recommend preventative steps improving patient outcomes and reducing the risk of complications. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting post-surgery complications and recommending preventative steps using patient data and machine learning algorithms, comprising: • a patient data collection module for collecting patient data such as medical history, surgical procedure details, and vital signs; • a surgical outcomes database including information on surgical procedures, complications, and preventative measures; • an Al algorithm that utilizes machine learning techniques to generate a risk assessment for post-surgery complications and recommend preventative steps based on the patient's individual characteristics and the surgical outcomes database. 2. The system of 1, wherein the patient data collection module includes ultrasound imaging data. 3. The system of 1, wherein the Al algorithm utilizes deep learning techniques to analyze patient data and generate a risk assessment for post-surgery complications. 4. The system of 1, wherein the Al algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when generating a risk assessment for post-surgery complications. 5. The system of 1, wherein the AI algorithm is capable of recommending preventative steps based on the patient's risk assessment and the surgical outcomes database. AI-Assisted Post-Surgery Activity Resumption System The present invention provides an AI-assisted system for determining when a patient can safely resume specific activities post-surgery using ultrasound imaging. The system utilizes ultrasound imaging and a database of ultrasound images and corresponding activity resumption timelines to provide accurate and efficient recommendations for activity resumption. Post-surgery activity resumption is a critical component of recovery from surgery. Traditional activity resumption methods can be time-consuming and may not provide the 96 9354.06PCTmaximum benefit to patients. Al-assisted activity resumption using ultrasound can help improve the accuracy and efficiency of activity resumption and lead to improved patient outcomes. The AI-assisted post-surgery activity resumption system includes an ultrasound imaging device, a database of ultrasound images and corresponding activity resumption timelines, and an Al algorithm that analyzes ultrasound images to determine when a patient can safely resume specific activities post-surgery based on the patient's individual characteristics and the characteristics of the surgery. The AI algorithm is trained on a dataset of ultrasound images and corresponding activity resumption timelines, which are used to develop predictive models for activity resumption. The system operates as follows: a patient undergoing post-surgery activity resumption is examined using an ultrasound imaging device. The resulting images are analyzed by the AI algorithm, which determines when the patient can safely resume specific activities based on the patient's individual characteristics and the characteristics of the surgery. The system can be used in various medical settings, including hospitals, clinics, and physical therapy centers. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted post-surgery activity resumption system provides several advantages over traditional activity resumption methods. It is non-invasive and can rapidly identify when a patient can safely resume specific activities, allowing for faster and more effective recovery. Additionally, it can help improve patient outcomes by recommending the most appropriate activity resumption timeline based on the patient's individual characteristics and the characteristics of the surgery, reducing the risk of further injury. The AI-assisted post-surgery activity resumption system provides a novel and effective method for activity resumption. By utilizing ultrasound imaging and AI algorithms, the system can provide accurate and efficient recommendations for activity resumption, improving patient outcomes and reducing the risk of further injury. The system can be used in various medical settings and can be integrated into existing medical equipment 97 9354.06PCTand workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for determining when a patient can safely resume specific activities post- surgery using ultrasound imaging, comprising: • an ultrasound imaging device for generating ultrasound images of a patient undergoing post-surgery activity resumption; • a database of ultrasound images and corresponding activity resumption timelines; • an AI algorithm trained on the database of ultrasound images and corresponding activity resumption timelines for determining when a patient can safely resume specific activities based on the patient's individual characteristics and the characteristics of the surgery. 2. The system of 1, wherein the ultrasound imaging device is a handheld device. 3. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 4. The system of 1, wherein the Al algorithm utilizes deep learning techniques to analyze the ultrasound images and determine when a patient can safely resume specific activities. 5. The system of 1, wherein the AI algorithm is capable of considering the patient's individual characteristics, such as age, sex, and medical history, when determining when a patient can safely resume specific activities. 6. The system of 1, wherein the AI algorithm is capable of recommending the most appropriate activity resumption timeline based on the patient's individual characteristics and the characteristics of AI-Assisted Detection of Foreign Bodies in Patients using Ultrasound Imaging The present invention provides an Al-assisted system for detecting foreign bodies in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of foreign bodies, particularly after surgery. The system can be used to improve patient safety and reduce the risk of complications associated with foreign body presence. The presence of foreign bodies in patients is a major concern, particularly after surgery. Traditional methods of detecting foreign bodies can be time-consuming and may not provide the maximum benefit to patients. Al-assisted detection of foreign bodies using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. 98 9354.06PCTThe AI-assisted detection of foreign bodies in patients using ultrasound imaging system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to detect foreign bodies. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can identify foreign bodies in the images and provide a notification to medical personnel. The system operates as follows: after surgery or in cases where the presence of foreign bodies is suspected, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to detect the presence of foreign bodies. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted detection of foreign bodies in patients using ultrasound imaging system provides several advantages over traditional detection methods. It is non- invasive and can rapidly identify foreign bodies, allowing for faster and more accurate detection. Additionally, it can help improve patient outcomes by reducing the risk of complications associated with foreign body presence. The AI-assisted detection of foreign bodies in patients using ultrasound imaging system provides a novel and effective method for foreign body detection. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of foreign bodies, improving patient safety and reducing the risk of complications associated with foreign body presence. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for detecting foreign bodies in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to detect the presence of foreign bodies. 2. The system of l, wherein the machine learning algorithms utilize deep 99 9354.06PCTlearning techniques to analyze the ultrasound images. The system of 1, wherein the machine learning algorithms are capable of identifying different types of foreign bodies, including but not limited to surgical instruments and foreign objects. The system of 1, wherein the ultrasound imaging device is a handheld device. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. The system of 1, wherein the machine learning algorithms provide a notification to medical personnel upon detecting the presence of foreign bodies. AI-Assisted Blood Clot Identification and Prediction System using Ultrasound Imaging The present invention provides an Al-assisted system for identifying blood clots in patients using ultrasound imaging and predicting the formation of blood clots. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of blood clots, reducing the risk of complications associated with blood clots. Blood clots are a major concern in medical treatment. Traditional methods of detecting blood clots can be time-consuming and may not provide the maximum benefit to patients. AI-assisted detection of blood clots using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted blood clot identification and prediction system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to identify blood clots and predict the formation of blood clots. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can identify blood clots in the images and provide a notification to medical personnel. The algorithms can also predict the formation of blood clots based on the patient's individual characteristics and medical history. The system operates as follows: the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to detect the presence of blood clots and predict the formation of blood clots. 100 9354.06PCTThe system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted blood clot identification and prediction system provides several advantages over traditional detection methods. It is non-invasive and can rapidly identify blood clots, allowing for faster and more accurate detection. Additionally, it can help improve patient outcomes by predicting the formation of blood clots based on the patient's individual characteristics and medical history, reducing the risk of complications associated with blood clot formation. The AI-assisted blood clot identification and prediction system provides a novel and effective method for blood clot detection and prediction. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of blood clots and predict the formation of blood clots, improving patient outcomes and reducing the risk of complications associated with blood clots. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for identifying blood clots in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to identify the presence of blood clots and predict the formation of blood clots based on the patient's individual characteristics and medical history. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying the location and size of blood clots. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 6. The system of 1, wherein the machine learning algorithms provide a notification to medical personnel upon detecting the presence of blood clots. 101 9354.06PCTAI-Assisted Anesthesia Complication Prediction System using IDtrasound Imaging The present invention provides an AI-assisted system for predicting anesthesia complications using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate prediction of anesthesia complications, reducing the risk of complications associated with anesthesia use. Anesthesia complications are a major concern in medical treatment. Traditional methods of predicting anesthesia complications can be time-consuming and may not provide the maximum benefit to patients. AI-assisted prediction of anesthesia complications using ultrasound imaging can help improve the accuracy and efficiency of prediction and lead to improved patient outcomes. The AI-assisted anesthesia complication prediction system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict anesthesia complications. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict anesthesia complications before use, during use, and after use based on the patient's individual characteristics and medical history. The system operates as follows: the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to predict anesthesia complications before use, during use, and after use. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted anesthesia complication prediction system provides several advantages over traditional prediction methods. It is non-invasive and can rapidly predict anesthesia complications, allowing for faster and more accurate prediction. Additionally, it can help improve patient outcomes by predicting anesthesia complications based on the patient's individual characteristics and medical history, reducing the risk of complications associated with anesthesia use. The AI-assisted anesthesia complication prediction system provides a novel and effective method for predicting anesthesia complications. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate prediction of anesthesia complications, improving patient outcomes and reducing the 102 9354.06PCTrisk of complications associated with anesthesia use. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting anesthesia complications using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict anesthesia complications before use, during use, and after use based on the patient's individual characteristics and medical history. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of predicting different types of anesthesia complications, including but not limited to respiratory depression and hypotension. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 6. The system of 1, wherein the machine learning algorithms provide a notification to medical personnel upon predicting anesthesia complications. AI-Assisted Internal Bleeding and Stroke Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for detecting internal bleeding and stroke in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of internal bleeding and stroke, reducing the risk of complications associated with these conditions. Internal bleeding and stroke are major concerns in medical treatment. Traditional methods of detecting these conditions can be time-consuming and may not provide the maximum benefit to patients. AI-assisted detection of internal bleeding and stroke using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. 103 9354.06PCTThe AI-assisted internal bleeding and stroke detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to detect internal bleeding and stroke. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can identify signs of internal bleeding and stroke in the images and provide a notification to medical personnel. The system operates as follows: the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to detect signs of internal bleeding and stroke. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted internal bleeding and stroke detection system provides several advantages over traditional detection methods. It is non-invasive and can rapidly detect internal bleeding and stroke, allowing for faster and more accurate detection. Additionally, it can help improve patient outcomes by reducing the risk of complications associated with these conditions. The AI-assisted internal bleeding and stroke detection system provides a novel and effective method for detecting internal bleeding and stroke. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of these conditions, improving patient outcomes and reducing the risk of complications associated with internal bleeding and stroke. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for detecting internal bleeding and stroke in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to detect signs of internal bleeding and stroke. 2. The system of 1, wherein the machine learning algorithms utilize deep 104 9354.06PCTlearning techniques to analyze the ultrasound images. The system of 1, wherein the machine learning algorithms are capable of identifying the location and size of internal bleeding and stroke. The system of 1, wherein the ultrasound imaging device is a handheld device. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. The system of 1, wherein the machine learning algorithms provide a notification to medical personnel upon detecting signs of internal bleeding and stroke. AI-Assisted Nerve Damage Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for detecting, quantifying, and localizing nerve damage in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of nerve damage, reducing the risk of complications associated with these conditions. Nerve damage is a major concern in medical treatment. Traditional methods of detecting nerve damage can be time-consuming and may not provide the maximum benefit to patients. Al- assisted detection of nerve damage using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The Al-assisted nerve damage detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to detect, quantify, and localize nerve damage. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can identify signs of nerve damage in the images and provide a notification to medical personnel. The algorithms can also quantify and localize the nerve damage, providing a more detailed understanding of the extent of the damage. The system operates as follows: the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to detect, quantify, and localize nerve damage. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. 105 9354.06PCTAdvantages: The Al-assisted nerve damage detection system provides several advantages over traditional detection methods. It is non-invasive and can rapidly detect nerve damage, allowing for faster and more accurate detection. Additionally, it can help improve patient outcomes by providing a more detailed understanding of the extent of the damage, which can guide treatment decisions and improve recovery. The AI-assisted nerve damage detection system provides a novel and effective method for detecting, quantifying, and localizing nerve damage. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of nerve damage, improving patient outcomes and reducing the risk of complications associated with nerve damage. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for detecting, quantifying, and localizing nerve damage in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to detect signs of nerve damage and quantify and localize the nerve damage. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying the location and size of nerve damage. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 6. The system of 1, wherein the machine learning algorithms provide a notification to medical personnel upon detecting signs of nerve damage and provide a quantification and localization report of the damage. AI-Assisted Dehiscence Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for predicting, identifying, and localizing dehiscence in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate 106 9354.06PCTdetection of dehiscence, reducing the risk of complications associated with this condition. Dehiscence is a major concern in medical treatment, particularly in the context of surgical procedures. Traditional methods of detecting dehiscence can be time-consuming and may not provide the maximum benefit to patients. AI-assisted detection of dehiscence using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted dehiscence detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict, identify, and localize dehiscence. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of dehiscence before surgery, identify signs of dehiscence in the images, and provide a notification to medical personnel. The algorithms can also localize the dehiscence, providing a more detailed understanding of the extent of the condition. The system operates as follows: before surgery, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to predict the likelihood of dehiscence. After surgery, the patient is again examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify signs of dehiscence and localize the condition. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted dehiscence detection system provides several advantages over traditional detection methods. It can predict the likelihood of dehiscence before surgery, allowing for preemptive measures to be taken to reduce the risk of complications. Additionally, it can help improve patient outcomes by providing a more detailed understanding of the extent of the condition, which can guide treatment decisions and improve recovery. The AI-assisted dehiscence detection system provides a novel and effective method for predicting, identifying, and localizing dehiscence. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of 107 9354.06PCTdehiscence, improving patient outcomes and reducing the risk of complications associated with this condition. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting, identifying, and localizing dehiscence in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood of dehiscence before surgery, identify signs of dehiscence after surgery, and localize the dehiscence. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying the location and size of dehiscence. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. AI-Assisted Hematoma Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for predicting, identifying, and localizing a hematoma in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of a hematoma, reducing the risk of complications associated with this condition. A hematoma is a common condition in medical treatment, particularly in the context of trauma or surgery. Traditional methods of detecting a hematoma can be time- consuming and may not provide the maximum benefit to patients. AI-assisted detection of a hematoma using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted hematoma detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict, identify, and localize a hematoma. 108 9354.06PCTThe ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of a hematoma before symptoms appear, identify signs of a hematoma in the images, and provide a notification to medical personnel. The algorithms can also localize the hematoma, providing a more detailed understanding of the extent of the condition. The system operates as follows: before symptoms appear, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to predict the likelihood of a hematoma. After symptoms appear, the patient is again examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify signs of a hematoma and localize the condition. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted hematoma detection system provides several advantages over traditional detection methods. It can predict the likelihood of a hematoma before symptoms appear, a11owing for preemptive measures to be taken to reduce the risk of complications. Additionally, it can help improve patient outcomes by providing a more detailed understanding of the extent of the condition, which can guide treatment decisions and improve recovery. The AI-assisted hematoma detection system provides a novel and effective method for predicting, identifying, and localizing a hematoma. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of a hematoma, improving patient outcomes and reducing the risk of complications associated with this condition. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting, identifying, and localizing a hematoma in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood 109 9354.06PCTof a hematoma before symptoms appear, identify signs of a hematoma after symptoms appear, and localize the hematoma. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. The system of 1, wherein the machine learning algorithms are capable of identifying the location and size of a hematoma. The system of 1, wherein the ultrasound imaging device is a handheld device. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. The machine learning algorithms provide a notification to medical personnel upon detecting signs of a hematoma and provide a quant AI-Assisted Anemia Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for predicting and identifying anemia in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of anemia, reducing the risk of complications associated with this condition. Anemia is a common condition in medical treatment, particularly in the context of chronic illnesses. Traditional methods of detecting anemia can be time-consuming and may not provide the maximum benefit to patients. AI-assisted detection of anemia using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. Description: The AI-assisted anemia detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict and identify anemia. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of anemia before symptoms appear, identify signs of anemia in the images, and provide a notification to medical personnel. The system operates as follows: before symptoms appear, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to predict the likelihood of anemia. After symptoms appear, the patient is again examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify signs of anemia. 110 9354.06PCTThe system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted anemia detection system provides several advantages over traditional detection methods. It can predict the likelihood of anemia before symptoms appear, allowing for preemptive measures to be taken to reduce the risk of complications. Additionally, it can help improve patient outcomes by providing a more detailed understanding of the extent of the condition, which can guide treatment decisions and improve recovery. The Al-assisted anemia detection system provides a novel and effective method for predicting and identifying anemia. By utilizing ultrasound imaging and machine learnjng algorithms, the system can provide efficient and accurate detection of anemia, improving patient outcomes and reducing the risk of complications associated with this condition. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting and identifying anemia in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood of anemia before symptoms appear, identify signs of anemia after symptoms appear, and provide a notification to medical personnel. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying indicators of anemia, such as reduced hemoglobin levels or smaller blood vessels. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 6. The machine learning algorithms provide a notification to medical personnel upon detecting signs of anemia. 111 9354.06PCTAI-Assisted Renal Failure Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for predicting and identifying renal failure in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of renal failure, reducing the risk of complications associated with this condition. Renal failure is a major concern in medical treatment, particularly in the context of chronic kidney disease. Traditional methods of detecting renal failure can be time- consuming and may not provide the maximum benefit to patients. Al-assisted detection of renal failure using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted renal failure detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict and identify renal failure. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of renal failure before symptoms appear, identify signs of renal failure in the images, and provide a notification to medical personnel. The system operates as follows: before symptoms appear, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to predict the likelihood of renal failure. After symptoms appear, the patient is again examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify signs of renal failure. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and work flows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted renal failure detection system provides several advantages over traditional detection methods. It can predict the likelihood of renal failure before symptoms appear, allowing for preemptive measures to be taken to reduce the risk of complications. Additionally it can help improve patient outcomes by providing a more detailed understanding of the extent of the condition, which can guide treatment decisions and improve recovery. The AI-assisted renal failure detection system provides a novel and effective method for predicting and identifying renal failure. By utilizing ultrasound imaging and machine 112 9354.06PCTlearning algorithms, the system can provide efficient and accurate detection of renal failure, improving patient outcomes and reducing the risk of complications associated with this condition. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting and identifying renal failure in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood of renal failure before symptoms appear, identify signs of renal failure after symptoms appear, and provide a notification to medical personnel. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying indicators of renal failure, such as changes in renal architecture or blood flow. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. 6. The machine learning algorithms provide a notification to medical personnel upon detecting signs of renal failure. AI-Assisted Septicemia Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for predicting, identifying, and localizing the cause of septicemia in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of septicemia, reducing the risk of complications associated with this condition. Septicemia is a life-threatening condition caused by bacterial infections. Early detection and treatment of septicemia are critical for improving patient outcomes. Traditional methods of detecting septicemia can be time-consuming and may not provide the maximum benefit to patients. AI- assisted detection of septicemia using ultrasound 113 9354.06PCTimaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted septicemia detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict, identify, and localize the cause of septicemia. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of septicemia before symptoms appear, identify signs of septicemia in the images, and provide a notification to medical personnel. The algorithms can also localize the cause of septicemia, such as identifying the location of a bacterial infection. The system operates as follows: before symptoms appear, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to predict the likelihood of septicemia. After symptoms appear, the patient is again examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify signs of septicemia and localize the cause of the condition. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted septicemia detection system provides several advantages over traditional detection methods. It can predict the likelihood of septicemia before symptoms appear, allowing for preemptive measures to be taken to reduce the risk of complications. Additionally, it can help improve patient outcomes by providing a more detailed understanding of the cause and extent of the condition, which can guide treatment decisions and improve recovery. The Al-assisted septicemia detection system provides a novel and effective method for predicting, identifying, and localizing the cause of septicemia. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of septicemia, improving patient outcomes and reducing the risk of complications associated with this condition. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. A system for predicting, identifying, and localizing the cause of septicemia in patients 114 9354.06PCTusing ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood of septicemia before symptoms appear, identify signs of septicemia after symptoms appear, and localize the cause of the condition. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying the location and extent of bacterial infections. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital AI-Assisted Obstruction Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for predicting, identifying, and localizing obstructions in the body using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of obstructions, reducing the risk of complications associated with this condition. Obstructions in the body can lead to a wide range of medical conditions, from mild discomfort to life-threatening emergencies. Traditional methods of detecting obstructions can be time- consuming and may not provide the maximum benefit to patients. AI- assisted detection of obstructions using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted obstruction detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict, identify, and localize obstructions in the body. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of obstructions before symptoms appear, identify signs of obstructions in the images, and provide a notification to medical personnel. The algorithms can also localize the site of the obstruction, allowing for more targeted treatment. The system operates as follows: before symptoms appear, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine 115 9354.06PCTlearning algorithms to predict the likelihood of obstructions. After symptoms appear, the patient is again examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify signs of obstructions and localize the site of the obstruction. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted obstruction detection system provides several advantages over traditional detection methods. It can predict the likelihood of obstructions before symptoms appear, allowing for preemptive measures to be taken to reduce the risk of complications. Additionally, it can help improve patient outcomes by providing a more detailed understanding of the site and extent of the obstruction, which can guide treatment decisions and improve recovery. The AI-assisted obstruction detection system provides a novel and effective method for predicting, identifying, and localizing obstructions in the body. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of obstructions, improving patient outcomes and reducing the risk of complications associated with this condition. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting, identifying, and localizing obstructions in the body using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood of obstructions before symptoms appear, identify signs of obstructions after symptoms appear, and localize the site of the obstruction. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying the site and extent of obstructions. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital 116 9354.06PCTor clinic's existing medical equipment. AI-Assisted Hernia Detection System using Ultrasound Imaging The present invention provides an AI-assisted system for predicting, identifying, and localizing hernias in patients using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of hernias, reducing the risk of complications associated with this condition. Hernias can be a painful and uncomfortable condition that requires medical attention. Traditional methods of detecting hernias can be time-consuming and may not provide the maximum benefit to patients. Al-assisted detection of hernias using ultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted hernia detection system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict, identify, and localize hernias in patients. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of hernias before symptoms appear, identify signs of hernias in the images, and provide a notification to medical personnel. The algorithms can also localize the site of the hernia, allowing for more targeted treatment. The system operates as follows: before symptoms appear, the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to predict the likelihood of hernias. After symptoms appear, the patient is again examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify signs of hernias and localize the site of the hernia. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted hernia detection system provides several advantages over traditional detection methods. It can predict the likelihood of hernias before symptoms appear, allowing for preemptive measures to be taken to reduce the risk of complications. Additionally, it can help improve patient outcomes by providing a more detailed 117 9354.06PCTunderstanding of the site and extent of the hernia, which can guide treatment decisions and improve recovery. The AI-assisted hernia detection system provides a novel and effective method for predicting, identifying, and localizing hernias in patients. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of hernias, improving patient outcomes and reducing the risk of complications associated with this condition. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for predicting, identifying, and localizing hernias in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood of hernias before symptoms appear, identify signs of hernias after symptoms appear, and localize the site of the hernia. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of identifying the site and extent of hernias. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. AI-Assisted Identification of Pathogens using Ultrasound Imaging The present invention provides an Al-assisted system for identifying the specific type of pathogen, including bacteria, fungus, or virus, using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of pathogens, reducing the risk of misdiagnosis and complications associated with incorrect treatment. Identifying the specific type of pathogen is crucial for effective treatment of infectious diseases. Traditional methods of identifying pathogens can be time-consuming and may not provide the maximum benefit to patients. Al-assisted identification of pathogens using 118 9354.06PCTultrasound imaging can help improve the accuracy and efficiency of detection and lead to improved patient outcomes. The AI-assisted pathogen identification system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to identify the specific type of pathogen. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can identify the specific type of pathogen present in the images, allowing for more targeted treatment. The system operates as follows: the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to identify the specific type of pathogen present. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted pathogen identification system provides several advantages over traditional identification methods. It can accurately identify the specific type of pathogen present, allowing for more targeted and effective treatment. Additionally, it can help reduce the risk of misdiagnosis and incorrect treatment, leading to improved patient outcomes and reduced healthcare costs. The AI-assisted pathogen identification system provides a novel and effective method for identifying the specific type of pathogen present in patients. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate identification of pathogens, improving patient outcomes and reducing the risk of complications associated with incorrect treatment. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for identifying the specific type of pathogen, including bacteria, fungus, or virus, present in patients using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to identify the specific 119 9354.06PCTtype of pathogen present. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. The system of 1, wherein the machine learning algorithms are capable of identifying the specific type of pathogen present. The system of 1, wherein the ultrasound imaging device is a handheld device. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. AI-Assisted Failure Prediction System for Clinical Trials using Ultrasound Imaging The present invention provides an AI-assisted system for predicting future failure in clinical trials using ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate detection of potential failure, reducing the risk of wasting time, money, and resources on clinical trials that are unlikely to succeed. Clinical trials are an important part of the drug development process, but they can be time-consuming and costly. Identifying potential failures early in the process can help reduce costs and improve efficiency. AI-assisted prediction of future failure in clinical trials using ultrasound imaging can help improve the accuracy and efficiency of this process. The AI-assisted failure prediction system for clinical trials includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to predict future failure in clinical trials. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can predict the likelihood of future failure before symptoms appear, allowing for preemptive measures to be taken to reduce the risk of failure. Additionally, the algorithms can provide a notification to medical personnel when potential failure is detected. The system operates as follows: patients in the clinical trial are examined using the ultrasound imaging device at regular intervals. The resulting images are analyzed by the machine learning algorithms to predict the likelihood of future failure. If potential failure is detected, medical personnel can take preemptive measures to reduce the risk of failure. 120 9354.06PCTThe system can be used by researchers and medical professionals involved in clinical trials to improve the accuracy and efficiency of the drug development process. It can help reduce costs and improve patient outcomes by identifying potential failures early in the process. Advantages: The AI-assisted failure prediction system for clinical trials provides several advantages over traditional detection methods. It can predict the likelihood of future failure before symptoms appear, allowing for preemptive measures to be taken to reduce the risk of failure. Additionally, it can help reduce costs and improve efficiency by identifying potential failures early in the process. The Al-assisted failure prediction system for clinical trials provides a novel and effective method for predicting future failure in clinical trials using ultrasound imaging. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate detection of potential failure, improving the drug development process and reducing costs associated with wasted time and resources. The system can be used by researchers and medical professionals involved in clinical trials to improve patient outcomes and reduce healthcare costs. 1. A system for predicting future failure in clinical trials using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to predict the likelihood of future failure before symptoms appear. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of predicting future failure in clinical trials. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. AI-Assisted Medication Regimen Optimization System using Ultrasound Imaging The present invention provides an AI-assisted system for improving medication regimens using ultrasound imaging. The system utilizes ultrasound imaging and 121 9354.06PCTmachine learning algorithms to provide efficient and accurate optimization of medication regimens, improving patient outcomes and reducing healthcare costs. Optimizing medication regimens can be a challenging task, requiring a careful balance between efficacy and potential side effects. Traditional methods of medication optimization can be time-consuming and may not provide the maximum benefit to patients. AI-assisted optimization of medication regimens using ultrasound imaging can help improve the accuracy and efficiency of optimization and lead to improved patient outcomes. The AI-assisted medication regimen optimization system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to optimize medication regimens. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can provide a detailed understanding of the patient's anatomy and physiology, allowing for more targeted medication regimens. The system operates as follows: the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to provide a detailed understanding of the patient's anatomy and physiology. Based on this analysis, the algorithms can optimize the patient's medication regimen to improve efficacy and reduce potential side effects. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted medication regimen optimization system provides several advantages over traditional medication optimization methods. It can provide a detailed understanding of the patient's anatomy and physiology, allowing for more targeted medication regimens. Additionally, it can help reduce the risk of potential side effects and improve patient outcomes, leading to reduced healthcare costs. The AI-assisted medication regimen optimization system provides a novel and effective method for optimizing medication regimens using ultrasound imaging. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate optimization of medication regimens, improving patient outcomes and reducing healthcare costs. The system can be used in various medical settings and can 122 9354.06PCTbe integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for optimizing medication regimens using ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to provide a detailed understanding of the patient's anatomy and physiology and optimize the patient's medication regimen. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of optimizing medication regimens to improve efficacy and reduce potential side effects. 4. The system of 1, wherein the ultrasound imaging device is a handheld device. 5. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. AI-Assisted Lab Test Recommendation System using Ultrasound Imaging The present invention provides an Al-assisted system for recommending lab tests based on ultrasound imaging. The system utilizes ultrasound imaging and machine learning algorithms to provide efficient and accurate recommendations for lab tests, improving patient outcomes and reducing healthcare costs. Ordering the right lab tests is a critical component of diagnosing and treating medical conditions. Traditional methods of ordering lab tests can be time-consuming and may not provide the maximum benefit to patients. AI-assisted lab test recommendation based on ultrasound imaging can help improve the accuracy and efficiency of ordering lab tests and lead to in1proved patient outcomes. The AI-assisted lab test recommendation system includes an ultrasound imaging device, a database of ultrasound images, and machine learning algorithms that analyze the images to recommend lab tests. The ultrasound imaging device generates ultrasound images of the patient's body, which are then analyzed by the machine learning algorithms. The algorithms can 123 9354.06PCTprovide a detailed understanding of the patient's anatomy and physiology, allowing for more targeted lab test recommendations. The system operates as follows: the patient is examined using the ultrasound imaging device. The resulting images are analyzed by the machine learning algorithms to provide a detailed Understanding of the patient's anatomy and physiology. Based on this analysis, the algorithms can recommend the lab tests that would be most useful for diagnosing and treating the patient's condition. The system can be used in various medical settings, including hospitals, clinics, and doctor's offices. It can be integrated into existing medical equipment and workflows, and can be used by medical professionals with various levels of experience and training. Advantages: The AI-assisted lab test recommendation system provides several advantages over traditional lab test ordering methods.1t can provide a detailed understanding of the patient's anatomy and physiology, allowing for more targeted lab test recommendations. Additionally, it can help reduce the risk of unnecessary lab tests and improve patient outcomes, leading to reduced healthcare costs. The AI-assisted lab test recommendation system provides a novel and effective method for recommending lab tests based on ultrasound imaging. By utilizing ultrasound imaging and machine learning algorithms, the system can provide efficient and accurate recommendations for lab tests, improving patient outcomes and reducing healthcare costs. The system can be used in various medical settings and can be integrated into existing medical equipment and workflows, making it accessible to medical professionals with various levels of experience and training. 1. A system for recommending lab tests based on ultrasound imaging and machine learning algorithms, comprising: • an ultrasound imaging device for generating ultrasound images of a patient's body; • a database of ultrasound images; • machine learning algorithms that analyze the ultrasound images to provide a detailed understanding of the patient's anatomy and physiology and recommend the lab tests that would be most useful for diagnosing and treating the patient's condition. 2. The system of 1, wherein the machine learning algorithms utilize deep learning techniques to analyze the ultrasound images. 3. The system of 1, wherein the machine learning algorithms are capable of recommending the lab tests that would be most useful for diagnosing and treating the 124 9354.06PCTpatient's condition. The system of 1, wherein the ultrasound imaging device is a handheld device. The system of 1, wherein the ultrasound imaging device is integrated into a hospital or clinic's existing medical equipment. AI-Assisted Frequency Optimization System for ultrasound Imaging The present invention provides an AI-assisted system for optimizing frequency in ultrasound imaging for optimum retrieval of health attributes. The system utilizes raw ultrasound information and machine learning algorithms to provide efficient and accurate frequency optimization, improving patient outcomes and reducing healthcare costs. Frequency optimization is an important aspect of ultrasound imaging, allowing for the retrieval of high-quality health attri...

Claims

CLAIMS What is claimed is:

1. A system for making a comprehensive medical determination, the system comprising: A digital storage storing a plurality of medical images or corresponding raw data of a plurality of anatomical structures of a patient; analysis logic comprising a trained neural network in communication with the digital storage and configured to provide a prediction or an insight related to a medical condition, based on the stored medical images or raw data; a user interface configured to provide the prediction or insight to a healthcare professional; and a microprocessor configured to execute at least the analysis logic.

2. The system of claim 1, wherein the analysis logic includes first logic configured to analyze the stored medical images or raw data to predict a current state of a medical condition.

3. The system of claim 1, wherein the analysis logic includes second logic configured to predict a future state, progression, or risk of a medical condition.

4. The system of claim 1, wherein the analysis logic is configured to employ a regression algorithm to provide the prediction as a range or confidence interval.

5. The system of claim 1, wherein the analysis logic is configured to employ a classification algorithm to provide the prediction as one of a plurality of categories or severities.

6. The system of claim 1, wherein the analysis logic is further configured to provide the prediction or insight based on a combination of the medical images or raw data and additional clinical data.

7. The system of claim 1, further comprising an image acquisition device configured to acquire the medical images or raw data of the plurality of anatomical structures of the patient for storing in the digital storage.

8. The system of claim 7, further comprising feedback logic configured to guide a user of the image acquisition device in optimizing the acquisition of the medical images or raw data.

9. A method for training a neural network to make comprehensive medical predictions, the method comprising: receiving a dataset of medical images, or raw data from which the medical images can be generated, of a plurality of anatomical structures of a plurality of patients, 153 9354.06PCTthe images or raw data labeled with ground truth diagnoses of a plurality of medical conditions; partitioning the dataset into a training set, a validation set, and a test set; training a neural network using the training set to predict the plurality of medical conditions; fine-tuning the neural network using the validation set to optimize its performance and generalization; and evaluating the final performance of the trained neural network using the independent test set, by comparing the predictions to the ground truth labels.

10. The method of claim 9, wherein the medical images or raw data comprise one or more imaging modalities including ultrasound, radiography, MRI, CT, PET, SPECT, mammography, and optical imaging.

11. The method of claim 9, further comprising pre-processing the medical images or raw data before training the neural network, including steps of normalization, resizing, cropping, or augmentation.

12. The method of claim 9, further comprising employing transfer learning to leverage pre- trained neural networks and adapt them for the specific task of comprehensive medical condition prediction.

13. A method for making a comprehensive medical determination using a trained neural network, the method comprising: acquiring one or more medical images, or raw data from which the medical images can be generated, of a plurality of anatomical structures of a patient using an image acquisition device; pre-processing the acquired medical images or raw data to optimize them for input into a trained neural network; providing the pre-processed medical images or raw data to the trained neural network, which has been optimized to predict a plurality of medical conditions; receiving from the trained neural network a prediction or insight related to the presence, absence, severity, or risk of a medical condition for the patient; and presenting the prediction or insight to a healthcare professional via a user interface to support clinical decision-making. 154 9354.06PCT14. The method of claim 13, wherein the trained neural network employs a combination of regression and classification algorithms to provide both continuous and categorical predictions.

15. The method of claim 13, wherein the prediction or insight are accompanied by explanations or visualizations highlighting the key anatomical features or abnormalities that contributed to the prediction or insight.

16. The method of claim 13, wherein the trained neural network is continuously updated and refined using new medical images or raw data and ground truth data.

17. The method of claim 13, wherein the comprehensive medical determination comprises determining usage of a drug by the patient.

18. The method of claim 13, wherein the comprehensive medical determination comprises determining that the patient is diabetic or pre-diabetic.

19. The method of claim 13, wherein the comprehensive medical determination comprises determining that the patient has inflammatory bowel disease.

20. The method of claim 13, wherein the comprehensive medical determination comprises determining a medication dosage for the patient.

21. The method of claim 13, wherein the comprehensive medical determination comprises determining an administration route for a medication to be given to the patient.

22. The method of claim 13, wherein the comprehensive medical determination comprises determining the patient has an allergy.

23. The method of claim 13, wherein the comprehensive medical determination comprises determining a likelihood that the patient will develop arthritis.

24. The method of claim 13, wherein the comprehensive medical determination comprises determining a pain level and pain location in the patient.

25. The method of claim 13, wherein the comprehensive medical determination comprises determining a symptom and a location thereof in the patient.

26. The method of claim 13, wherein the comprehensive medical determination comprises determining a symptom in the patient and a drug for the treatment thereof.

27. The method of claim 13, wherein the comprehensive medical determination comprises determining an optimum drug administration for the patient in a clinical trial.

28. The method of claim 13, wherein the comprehensive medical determination comprises determining a symptom onset and a severity of the symptom in the patient. 155 9354.06PCT29. The method of claim 13, wherein the comprehensive medical determination comprises determining a diagnosis of an infection in the patient, and a treatment therefor.

30. The method of claim 13, wherein the comprehensive medical determination comprises determining an embryo selection for the patient.

31. The method of claim 13, wherein the comprehensive medical determination comprises determining a personalized ovarian stimulation procedure for the patient.

32. The method of claim 13, wherein the comprehensive medical determination comprises determining a degree of follicle development in the patient.

33. The method of claim 13, wherein the comprehensive medical determination comprises determining a level of fertility of the patient.

34. The method of claim 13, wherein the comprehensive medical determination comprises determining whether the patient is a candidate for In Vitro Fertilization.

35. The method of claim 13, wherein the comprehensive medical determination comprises a real-time monitoring of an for In Vitro Fertilization procedure in the patient.

36. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a likelihood of a successful In Vitro Fertilization procedure in the patient.

37. The method of claim 13, wherein the comprehensive medical determination comprises a determination of endometrial receptivity in the patient.

38. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a level of a hormone in the patient.

39. The method of claim 13, wherein the comprehensive medical determination comprises a determination of personalized treatment regimen in the patient.

40. The method of claim 13, wherein the comprehensive medical determination comprises a determination of hypertension in the patient and a treatment therefor.

41. The method of claim 13, wherein the comprehensive medical determination comprises a determination of blood type in the patient.

42. The method of claim 13, wherein the comprehensive medical determination comprises a determination of blood transfusion compatibility in the patient.

42. The method of claim 13, wherein the comprehensive medical determination comprises an emergency triage of the patient.

43. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a surgical plan for the patient. 156 9354.06PCT44. The method of claim 13, wherein the comprehensive medical determination comprises a determination of an organ transplant plan for the patient.

45. The method of claim 13, wherein the comprehensive medical determination comprises a determination of an organ transplant level of acceptance by the patient.

46. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a rehabilitation and physical therapy plan for the patient.

47. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a likelihood of a surgical complication for the patient.

48. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a surgical complication prevention method for the patient.

49. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a timeline for post-surgical resumption of activities by the patient.

50. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a foreign body in the patient.

51. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a blood clot in the patient.

52. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a likelihood of a blood clot developing in the patient.

53. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a likelihood of an anesthesia complication for the patient.

54. The method of claim 13, wherein the comprehensive medical determination comprises a determination of internal bleeding or a stroke in the patient.

55. The method of claim 13, wherein the comprehensive medical determination comprises a determination of nerve damage in the patient.

56. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a dehiscence in the patient.

57. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a hematoma in the patient.

58. The method of claim 13, wherein the comprehensive medical determination comprises a determination of anemia in the patient.

59. The method of claim 13, wherein the comprehensive medical determination comprises a determination of renal failure in the patient. 157 9354.06PCT60. The method of claim 13, wherein the comprehensive medical determination comprises a determination of septicemia in the patient.

61. The method of claim 13, wherein the comprehensive medical determination comprises a determination of an obstruction in the patient.

62. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a hernia in the patient.

63. The method of claim 13, wherein the comprehensive medical determination comprises an identification of a pathogen within the patient.

64. The method of claim 13, wherein the comprehensive medical determination comprises a determination of an optimal medication regimen for the patient.

65. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a lab test recommendation for the patient.

66. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a likelihood of pre-eclampsia for induction timing in the patient.

67. The method of claim 13, wherein the comprehensive medical determination comprises a identification of a medication effect in the patient.

68. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a virtual biopsy for the patient.

69. The method of claim 13, wherein the comprehensive medical determination comprises a determination of an optimal medication regimen for the patient.

70. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a medication to treat an infection in the patient.

71. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a thyroid treatment for the patient.

72. The method of claim 13, wherein the comprehensive medical determination comprises a determination of a prostate treatment for the patient.

73. The method of claim 13, wherein the comprehensive medical determination comprises a quantitative prediction of a psychiatric disorder in the patient.

74. A method for calculating lab values using a trained neural network, the method comprising: acquiring one or more medical images, or raw data from which the medical images can be generated, of a plurality of anatomical structures of a patient using an image acquisition device; 158 9354.06PCTpre-processing the acquired medical images or raw data to optimize them for input into a trained neural network; providing the pre-processed medical images or raw data to the trained neural network, which has been optimized to calculate lab values; receiving from the trained neural network a lab value; and presenting the value via a user interface. 159 9354.06PCT

Images