120 results about "Clinical evaluation" patented technology

The invention discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.

Systems and methods include techniques associated with one or more machine learning systems analyze, compare, and process one or both of model outputs or model inputs. Content verification may include generating content using a first trained machine learning system and then verifying the initial generation using one or more second trained machine learning systems, such as by generating content associated with a prompt, comparing output labels, or comparing output responsive to changing parameters. Additionally, data preparation may include image scaling and batching methods to improve machine learning outputs.

The invention discloses an infant state recognition method and system based on traditional Chinese medicine five-tone monitoring analysis, and the method comprises the steps: synchronously collecting the crying sound, physiological signals and behavior videos of an infant, extracting the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate of the physiological signals, and the facial expression and limb movement features, and carrying out the recognition of the state of the infant through the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate. Performing structured integration by using a multi-modal feature fusion model; in combination with the five-tone theory of traditional Chinese medicine, a corresponding relation between audio features and five-organ states is established, and the robustness of five-tone and five-organ mapping is improved through fuzzy reasoning and a Bayesian mechanism; the system dynamically adjusts the weight coefficient of each mode, adapts to the individual and emotion historical trend, achieves more accurate emotion recognition and physiological evaluation, achieves cross-mode and multi-layer information fusion, improves the accuracy and interpretation ability of infant emotion and five-internal-organ state recognition, and provides a scientific basis for clinical evaluation and health management.

The invention discloses a lumbar postoperative functional recovery evaluation method based on gait analysis, and the method comprises the steps: collecting a multi-modal gait multi-source signal, and building high-quality space-time synchronous multi-modal gait data through standardization processing and abnormity elimination; further extracting multi-point features such as an action sequence, pressure and myoelectricity, and realizing deep association between gait features and clinical evaluation indexes by adopting graph structure fusion and knowledge graph mapping; a graph neural network and an attention mechanism are introduced, the causal relationship between gaits and lumbar function recovery is reasoned, self-supervised dynamic optimization is performed according to individual differences, the objectivity, interpretability and adaptive ability of rehabilitation evaluation can be improved, and the rehabilitation accuracy is improved. And reliable data basis and causal traceability are provided for medical decision and personalized rehabilitation.

The invention discloses a thyroid ultrasound diagnosis report intelligent generation method based on a multi-mode large language model, and relates to a thyroid ultrasound diagnosis report intelligent generation method. The objective of the invention is to solve the problems of lack of term standardization and insufficient complex focus feature analysis in the prior art. According to the method, a full-flow technical system of double-flow coding, cross-modal alignment, dynamic man-machine cooperation and multi-dimensional evaluation is constructed. Multi-scale feature fusion of a thyroid global form and a nodule ROI region is realized through ResNet-50 and ConvNeXt double-flow coding networks, image-text semantic alignment is optimized by adopting a CLIP symmetry loss function, and training resource consumption is reduced in combination with an LoRA parameter fine tuning technology. A dynamic man-machine collaborative closed-loop mechanism is innovatively introduced, model parameters are iteratively optimized through doctor correction data, and a four-dimensional clinical evaluation system comprising ROUGE-L, BLEU-4, CIDEr and expert blind evaluation is established. The invention belongs to the technical field of medical artificial intelligence auxiliary diagnosis.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

The invention discloses a liver cancer metastasis risk prediction method and system based on image-pathomics feature fusion, and relates to the technical field of medical information intelligent processing, and the method comprises the following steps: 1, carrying out the collection and preprocessing of multi-modal data; step 2, image omics feature extraction; step 3, extracting pathological omics features; 4, performing feature screening and fusion modeling; 5, constructing and verifying a risk prediction model; and step 6, outputting and visualizing a result. The method aims at solving the problems that in existing liver cancer metastasis risk prediction, information utilization is single, images and pathological features are not fused, and prediction accuracy is insufficient. At present, clinical assessment mainly depends on pathological grading, AFP and other indexes, and cannot reflect tumor intrinsic heterogeneity, resulting in inaccurate metastasis risk prediction. According to the invention, the image omics-pathomics combined feature fusion prediction model and system are established, and multi-modal data automatic processing, feature optimization and individualized risk assessment are realized.

The invention discloses a bedridden patient sign measurement and medication assisting system based on multi-sensor fusion, which belongs to the technical field of medical instruments and comprises a multi-sensor fusion measurement module, a self-adaptive calibration compensation module, an intelligent evaluation decision module, a medication assisting calculation module and a closed-loop feedback optimization module. A pressure sensor array, an ultrasonic sensor and an acceleration sensor are used for cooperatively measuring the height and weight of a patient, a deformation compensation model and a body position correction model are adopted for accurate calibration, a BMI index is automatically calculated, a VTE risk score, a falling risk score and a self-care ability score are generated, and the medication dosage is intelligently recommended according to medicine characteristics and patient physique. And continuously optimizing system parameters through an incremental learning algorithm to form a measurement-evaluation-medication-optimization deep coupling closed-loop system, so that the physical sign measurement precision, clinical evaluation efficiency and medication safety of the bedridden patient are remarkably improved, and the nursing labor intensity and the medical error risk are reduced.

The invention discloses a thyroid eye disease activity evaluation method and system based on an eye image, and aims to solve the problems that the existing clinical activity score cannot reflect continuous change of an illness state and is susceptible to subjective influence. The method comprises the following steps: acquiring original image data containing an eye region; performing standardized preprocessing on the image data; extracting multi-scale inflammatory features and structural features from the preprocessed image; carrying out cross-modal fusion on the extracted multi-scale features; calculating an activity quantitative score based on the fused comprehensive features; and outputting a visual report of the quantitative score and the evaluation conclusion. The system comprises an image acquisition module, an image preprocessing module, a feature extraction module, a feature fusion module, an activity evaluation module and a result output module. According to the technical scheme, objective quantitative evaluation of thyroid eye disease activity can be achieved, the consistency and repeatability of evaluation results are improved, remote image acquisition and automatic analysis are supported, and the space-time limitation of traditional clinical evaluation is broken through.

The invention discloses a feature analysis and prediction method for drawing a depression patient based on a convolutional neural network, and belongs to the field of artificial intelligence and mental health. The method comprises the following steps: acquiring drawing image data of a depression patient and a healthy control group, and constructing a standardized data set; preprocessing such as denoising, size normalization and edge enhancement is carried out on the image; constructing a convolutional neural network model comprising four convolutional layers, two pooling layers and a feature fusion layer, and automatically extracting key features such as line definition, spatial layout and detail richness by adopting a variable receptive field mechanism and an attention module; correlation analysis is carried out in combination with scores of a clinical evaluation scale, and the model is optimized through transfer learning and a focus loss function; and generating a risk assessment report. According to the method, objectivity and accuracy of depression screening are remarkably improved, subjective evaluation deviation is reduced, visual decision support is provided for clinicians, and early recognition and intervention of depression are achieved.

A system and method for a clinical education transition coordinator / preceptor system configured for cloud-based clinical assessment, training, and competency management of users. The system may include a shared marketplace or consortium, including resources relevant to specific geographic clinical education markets. The system may include a clinical education transition coordinator / preceptor application configured for training individuals in order to reskill and upskill licensed clinicians as well as for providing clinical site support for clinical students during clinical rotations.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

The application discloses an ophthalmic disease image classification system based on a big data model, which comprises a data set construction module, an image segmentation module and a model training module.The data set construction module is used for collecting eye images of patients with different ophthalmic diseases in an ophthalmic clinic and performing image marking processing to construct training and test data sets.The image segmentation module adopts YOLOv7 to perform image segmentation on the images taken by the smart phone to obtain eye images containing only the part below the eyebrows and above the zygomatic arch.The model training module adopts a five-fold cross-validation method to train the model on the images, and applies data enhancement technology, white balance adjustment and a transfer learning algorithm.The application relates to the technical field of ophthalmic disease image classification.The ophthalmic disease image classification system based on the big data model has high classification accuracy for diseases such as cataracts, keratitis and pterygium in the development stage test set and different clinical evaluation stages.

The utility model discloses an oral biomechanics capsule type detector, which comprises a main body made of an elastic material and provided with a tooth stress area for receiving tooth occlusion and a tongue and lip stress area for receiving tongue pressing and / or lip furling pressure; the capsule type detection structure comprises a plurality of cavities which are formed in the main body and are communicated with one another and a pressure sensor used for measuring the pressure of an integral cavity formed by the plurality of cavities, and the tooth stress area and the tongue and lip stress area are each provided with at least one capsule type detection structure. The measuring device can be used for measuring tooth occlusal force, lip smoothing force and tongue force in the upper jaw jacking direction, has the characteristics of simple structure, safety, sanitation, high comfort level, reliability in measurement, convenience in operation, reusability after cleaning and the like, is beneficial to clinical evaluation and diagnosis, and provides an effective auxiliary means for oral rehabilitation and orthodontic treatment.

The invention discloses a body composition analysis method based on a medical image, and the method specifically comprises the following steps: S1, training an artificial intelligence segmentation model: obtaining and collecting a human body three-dimensional medical image of clinical scanning, and constructing an original training medical image data set; a doctor manually marks an original image as a mask for artificial intelligence segmentation model training, and the method relates to the technical field of deep learning and image processing. According to the body composition analysis method based on the medical image, the body composition of the whole or single part of the human body can be quantitatively measured completely automatically, quickly and accurately by analyzing the medical image, the body composition evaluation efficiency is effectively improved, and the requirement of serving as a practical tool for clinical evaluation is met. According to the method, the change of the body composition in a period of time can be quantitatively analyzed by using the medical images accumulated in the period of time, the association between the body composition and the disease is explored, and the medical image body composition analysis in large-scale queue research is facilitated.

The invention relates to the field of medical data processing, in particular to a clinical evaluation and prediction method and system for exhaled gas and percutaneous carbon dioxide monitoring, and the method comprises the steps: determining a target skin area for implementing percutaneous carbon dioxide monitoring according to the body surface temperature data of a target object; according to the percutaneous carbon dioxide partial pressure of the target skin area, obtaining percutaneous carbon dioxide release characteristics; acquiring a relative change relationship between the percutaneous partial pressure of carbon dioxide and the exhaled gas partial pressure of carbon dioxide, and estimating energy metabolism characteristics in combination with percutaneous carbon dioxide release characteristics so as to construct a metabolism model matched with a target object and predict a disease risk change trend; evaluating and screening clinical detection data, and generating a disease risk evaluation result. By comparing and analyzing exhaled air and percutaneous carbon dioxide partial pressure data, the real body state can be comprehensively reflected, the potential health risk of a patient can be quickly predicted and evaluated, and the disease risk evaluation credibility can be improved.

The present invention relates to the provision of a scanning system and methods for scanning a discrete location within a body part of a subject. More particularly, the present invention provides a scanning system to scan a suspected injury in a body part of a subject, such as a brain injury. The scanning system provides a diagnostic scanning device that scans the discrete locations within the body part, and a processer configured to carry out calculating the probability of injury in each of the discrete locations; using a statistical map of the probability of injury within the body part; and the diagnostic scanning device scanning each discrete location in order of decreasing probability determined by the processor to obtain results at each discrete location; and displaying the results on a screen as the scanning continues until a clinical assessment of the injury in the body part can be made.

This invention relates to the field of image processing technology and discloses a posture deviation recognition method based on image recognition. The method involves acquiring a posture image at the moment a specified rehabilitation movement is completed, preprocessing and segmenting it to obtain a binary contour of the target posture, representing the contour points as a complex sequence, and performing closure processing and arc length resampling to standardize the number of contour points. Based on this, a DC-free and normalized Fourier descriptor is constructed to obtain the frequency domain features of the posture and the standard posture. The global shape deviation intensity and adaptive judgment threshold are calculated through frequency domain comparison, and the residual signal is reconstructed at the resampling point location to obtain the local deviation distribution. Finally, the posture deviation level and structured results are output according to a grading rule, thereby reducing the dependence on sensors and manual thresholding, realizing the quantitative assessment and presentation of posture deviation, and providing a basis for clinical assessment and training adjustment.

The application discloses a kind of digital lower jaw accurate positioning and customized removable denture preparation integrated method, it is related to stomatology technical field. Including: initial diagnosis data collection;Digital modeling and jaw position adjustment;Design treatment jaw position guide plate;Try on jaw position guide plate, check lower jaw movement, confirm facial form beautiful, prepare silicon rubber final impression, record the actual occlusion relationship of patient;Based on occlusion relationship preparation-final denture, the obtained-final denture is carried out clinical evaluation.The application is for complex oral conditions, such as occlusion disorder, temporomandibular joint disorder patient, by obtaining personalized fitting removable denture, adjusting the relative position relationship of upper and lower jaw bones, relieving abnormal stress of joint, assisting the stability and recovery of temporomandibular joint, realizing the unity of stability and aesthetic effect of stomatognathic system after repair.

The invention provides a wound surface area automatic calculation method, device and system and a storage medium. The wound surface area automatic calculation method comprises the following steps: S1, collecting a digital image containing a scald wound surface through a camera device, shooting the image in a non-real-time manner, and uploading the image to an attached standard ruler; and S2, during real-time measurement, measuring the distance between the wound surface and the camera, calculating the radius of a picture shot by the camera device by using a trigonometric function, and further calculating the shooting area corresponding to the shot picture. And calculating the number of pixel points in a unit area according to the pixels of the shot picture. During non-real-time shooting, the number of pixels corresponding to the unit length of the standard ruler is measured, and the total area of the image is calculated according to the proportional relation between the actual size of the unit length and the number of the pixels. The wound surface area automatic calculation method, device and system and the storage medium provided by the invention have the advantages that the operation is simple and convenient, the result is objective, accurate measurement of the scald area is realized through digital image analysis, and the clinical evaluation accuracy can be remarkably improved.

The invention discloses a non-stationary pupil signal blind source separation method based on a Gamma unit impulse response model. Compared with the prior art, the method has the advantages that the ecological effectiveness of clinical evaluation is improved; high-precision verification with zero hardware cost is realized; and the problem of time sequence asynchronization of physiological signals is solved.

The application discloses a lung inflammation lesion quantitative evaluation artificial intelligence model based on CT images and relates to the field of artificial intelligence models.The model comprises CT image preprocessing, self-supervised lesion segmentation and quantification, multi-modal joint pre-training coding and multi-task diagnosis and prognosis prediction module composition which are sequentially and communicatively connected, and realizes feature interaction through a unified feature embedding space.The model realizes self-supervised lesion segmentation and multi-dimensional quantification by adopting a three-dimensional generative reconstruction network, completes image-text feature fine-grained alignment through cross-modal contrast learning of an adversarial enhancement, and fuses multi-dimensional features.The model can complete multiple tasks such as ARDS diagnosis, non-invasive estimation of P / F ratio, severity grading and prognosis prediction in parallel, reduces dependence on artificial labeling, improves cross-center generalization capability and diagnosis precision, and provides a standardized intelligent tool for clinical evaluation of severe lung inflammation.

The invention provides a multi-parameter flow cytometry detection method and system for ferroptosis phenotype quantification, and relates to the technical field of cell biological detection and analysis. By setting a ferroptosis induction control group, a ferroptosis inhibition control group and a group to be detected, lipid peroxidation signals, free iron signals and membrane integrity signals are synchronously collected in the same detection process, and original multi-parameter data are obtained. And performing spectrum unmixing and batch correction by utilizing artificial intelligence, and screening a target event set based on a preset event quality control condition. On the basis, the ferroptosis probability of the event to be detected is calculated, the ferroptosis phenotype index is determined, standardization and quantification of ferroptosis phenotypes are achieved, and the method is suitable for high-throughput screening and clinical evaluation.

The invention provides an intestinal tract evaluation method, system and device under an endoscope and a storage medium. The method comprises the following steps: acquiring a colon endoscope image of a patient; performing UCEIS scoring on the vascular texture, bleeding and ulcer erosion; performing colorectum segmentation identification and constructing an ordered frame sequence scoring matrix; the UCEIS highest score of each colorectal segment is calculated; constructing a colorectum segmentation scoring matrix, wherein the matrix comprises the UCEIS highest score in each colorectum segmentation part; and determining a Monte-UCEIS endoscope AI scoring result, wherein the Monte-UCEIS endoscope AI scoring result comprises a lesion accumulation range, a UCEIS highest score in the lesion accumulation range, a sum of highest scores and a comprehensive score calculated according to the sum of highest scores and Monte scores. According to the method, the multi-dimensional lesion trend of the intestinal tract can be analyzed, and more comprehensive clinical evaluation is provided.

The application discloses a diabetes patient biological age calculation method based on glycosylation markers and a multi-task deep learning framework, and belongs to the technical field of bioinformatics and medical data processing technology, and the method comprises the following steps: obtaining glycomics feature data and clinical examination data of diabetes patients; preprocessing the data; obtaining a feature set by adopting a multi-method screening and fusion strategy; constructing a deep learning model comprising a multi-modal feature fusion module and a multi-task learning module for training, wherein the multi-task learning module comprises a main task of aging clock regression and at least one auxiliary task; and outputting biological age prediction values and age acceleration evaluation results of individuals by using the trained model. The method realizes deep fusion of multi-modal biological data and exclusive modeling for the diabetes population, can accurately quantify the biological aging process and the deviation degree relative to the calendar age, and provides an objective quantitative tool for clinical evaluation.

The invention provides a contrast-agent-free virtual enhanced magnetic resonance imaging device and a corresponding method, the contrast-agent-free virtual enhanced magnetic resonance imaging device is based on a VCE-MRI model of federated learning (FL), the VCE-MRI model is trained by using large-scale highly heterogeneous multi-center data for data of nasopharyngeal carcinoma (NPC) patients, and the data privacy of the patients is protected while high generalization of the model is ensured. The invention further provides a data preprocessing device and a corresponding method for contrast-agent-free virtual enhanced magnetic resonance imaging so as to obtain a training data set and / or a test data set suitable for the FL model from patient data from different medical institutions, and the training data set and / or the test data set are / is used for model training and / or local verification of the FL model. A clinical evaluation result shows that the VCE-MRI model developed by the invention has high generalization and high clinical use value.

A clinical evaluation system and method are disclosed that facilitate the use of one or more conduction deviation features or parameters determined from biophysical signals such as cardiac or biopotentials signals. Conduction derivation features or parameters may include VD conduction derivation features or parameters and / or VD conduction derivation Poincaré features or parameters. The conduction derivation features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, a medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and / or severity and / or localization of diseases or conditions or in the treatment of said diseases or conditions.

The application discloses a fundus recognition hyperspectral imaging chip design method, a fundus imaging device and equipment, and relates to the field of intelligent medical treatment. The method comprises the following steps: collecting a hyperspectral image sample of a clinical hyperspectral leopard-shaped fundus in a set wave band range, and performing wave band splitting to obtain a single-channel spectral image; performing image scoring on the single-channel spectral image to form a training sample, so as to train a neural network model; randomly combining a plurality of single-channel spectral images corresponding to each patient to obtain a plurality of single-channel spectral image combinations; scoring each single-channel spectral image combination based on the trained neural network model, and screening a set number of single-channel spectral image combinations with high scores; constructing a corresponding hyperspectral imaging chip for each single-channel spectral image combination, and performing clinical evaluation to obtain an optimal hyperspectral imaging chip. The application can effectively improve the recognition efficiency of the leopard-shaped fundus in clinical treatment.

This invention relates to the field of biomedical technology, specifically to the use of the N-terminal fragment of insulin-like growth factor binding protein-4 (IGF-4) in the clinical assessment of heart failure and coronary artery disease. The N-terminal fragment of IGF-4 is obtained by catalytic hydrolysis of IGF-4 by pregnancy-associated plasma protein A. Clinical assessment of heart failure includes diagnosing or assisting in the diagnosis of heart failure, or assessing or assisting in the prognostic risk of heart failure. Clinical assessment of coronary artery disease includes assessing or assisting in the prognostic risk of coronary artery disease. By detecting the content of the N-terminal fragment of IGF-4 in a sample, heart failure can be diagnosed or assisted in the diagnosis, and the prognosis of heart failure and coronary artery disease can be assessed or assisted in, demonstrating good clinical assessment efficacy.

The invention relates to a bromhidrosis postoperative wound infection risk prediction method and a bromhidrosis postoperative wound infection risk prediction system, aims to solve the problems that an existing clinical evaluation method depends on subjective experience and lacks a standardized tool, and performs quantitative prediction on the individual infection risk of a patient by utilizing a column graph model constructed based on multi-factor analysis. The method comprises the following steps: systematically collecting clinical index data of a patient, wherein the clinical index data comprises general data and operation-related indexes; inputting the data into a pre-constructed column graph model, and obtaining a comprehensive risk score by matching a corresponding score for each index and performing accumulation; and finally, the risk level is divided into a low-risk class and a high-risk class according to a preset threshold value, and an intuitive basis is provided for clinical decision making. According to the method, the operation process is standardized, the result output is visual, the method can be effectively integrated into the clinical working process, and a practical auxiliary tool is provided for medical staff to identify high-risk patients and implement targeted prevention and nursing measures, so that the quality and efficiency of postoperative management are expected to be improved.