178 results about "Clinical evaluation" patented technology

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

Systems and methods for generating a patient-specific clinical assessment are disclosed. A processor receives patient health data corresponding to a patient. The patient health data is analyzed using a trained large language model (LLM) configured to process the patient health data. Clinical insights are identified based on the analysis of the patient health data using the trained LLM. A set of interactive prompts is generated for a patient interface based on the identified clinical insights. The set of interactive prompts being configured to obtain additional information associated with the patient. A set of patient responses responsive to the generated set of interactive prompts are received. The clinical insights and the set of patient responses are displayed on a clinician interface.

The invention relates to the technical field of medical diagnosis systems, in particular to an early gonitis diagnosis system and method based on multi-modal deep learning, a data collection module is used for collecting and preprocessing multi-modal data including knee joint X-ray image data, MRI (Magnetic Resonance Imaging) image data and clinical evaluation data; the model construction module constructs a deep learning model by combining a convolutional neural network and a Transform architecture, realizes mapping from multi-modal data to a clinical assessment phenotype label by adopting a feature automatic learning module and a potential relationship mining module, and integrates a Grad-CAM interpretability method for understanding a model prediction basis; diagnostic data are continuously collected to be fed back to the data collection module for model optimization, the convolutional network is good at capturing local image features, the Transform architecture is good at modeling a long-range dependency relationship, and the feature extraction and relationship modeling capabilities of the model are greatly improved through combination of the convolutional network and the Transform architecture.

The invention is suitable for the technical field of intelligent medical treatment, and provides an intelligent decision-making system for clinical diagnosis decision analysis, which comprises a data acquisition module, a knowledge graph module, a multi-modal analysis engine, a clinical evaluation module, a clinical treatment scheme decision-making module and a feedback optimization module, the data acquisition module is used for integrating multi-source heterogeneous clinical data; the knowledge graph module is used for dynamically storing a medical entity relationship; the multi-modal analysis engine extracts features through deep learning; the clinical treatment scheme decision module is used for outputting diagnosis suggestions and providing explanations; the feedback optimization module is used for receiving a doctor correction result, automatically iterating a training model and forming closed-loop learning; according to the system, real-time association medical new discovery is supported through a dynamic knowledge graph, and hospital information system data are not effectively integrated; through cooperative work of the rule engine and the AI model, clinical medical quality and efficiency are improved.

The invention provides an Alzheimer disease auxiliary accurate identification method and system based on a multi-omics marker network and an adaptive support vector machine. Accurate identification of Alzheimer's disease and other types of dementia is realized through a marker network based on five modal data and an adaptive support vector machine model. According to the method, multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation are fused, specific markers are analyzed and screened by adopting a weighted gene co-expression network, and cross-modal feature interactive learning is realized through a self-attention mechanism. An online learning mechanism is introduced to enable the model to adapt to new data distribution, and the contribution degree of each marker to diagnosis is output in combination with an interpretability module. Finally, multi-center data synchronization and model optimization are realized by means of a cloud platform, the accuracy, specificity and early diagnosis capability of AD diagnosis and identification and the adaptability of the model are remarkably improved, the limitation of the prior art is overcome, and doctors are assisted to diagnose and treat the Alzheimer's disease.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

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.

The invention belongs to the technical field of rehabilitation training, and particularly relates to an active stroke rehabilitation auxiliary training system which generates a personalized training base line through clinical evaluation data of a patient and guides the patient to cooperatively execute training actions through rehabilitation equipment according to the training base line. Training execution efficiency scores, patient active motion features and equipment auxiliary performance features are analyzed in real time, and rehabilitation equipment auxiliary strength and training task difficulty are dynamically adjusted in the training duration according to the double-feature real-time interaction state and the synchronous training execution efficiency scores; finally, the rehabilitation ability state of the patient is updated according to the multi-dimensional time sequence characteristics of the whole single training process, and a baseline correction instruction of the next training period is output based on the updated rehabilitation ability state, so that the limitation of singly paying attention to patient actions in the past is broken, and the boundary of patient independent effort and equipment assistance in the rehabilitation process is clearly distinguished; and the dynamic adaptability and accuracy of stroke rehabilitation training are effectively improved.

The invention provides a voice-based psychological state recognition method, device and equipment. The method comprises the following steps: acquiring voice data of a user; preprocessing the voice data to obtain target voice data; extracting voice features of the target voice data; inputting the voice features into a pre-trained voice psychological state model; based on the voice psychological state model, the psychological state of the user corresponding to the voice feature is determined, clinical evaluation of professional doctors is not depended on any more, consumed time is short, efficiency is high, and popularization and promotion are facilitated.

The invention discloses a spasmodic torticollis quantitative analysis and evaluation method and system. The method comprises the following steps: step S1, collecting demographic information of a patient with primary cervical myodynia in an outpatient service and a ward; s2, two clinicians carry out clinical evaluation on the spasmodic torticollis of the patient; s3, recording the video according to the specification; s4, designing features through a face key point recognition algorithm, carrying out feature extraction, recognizing key anatomical points, and constructing auxiliary lines; s5, calculating angles and time through visual identification, and defining various scale indexes and scoring rules; and S6, outputting a score and a quantitative score result, and comparing the score and the score with a clinical expert score for verification. By means of the method, clinical cardiotoxin injection doctors can be assisted in accurately judging motion types, accurately positioning muscles and recommending specific injection doses, and therefore the disease curative effect and the clinical efficiency of the doctors are improved.

The invention discloses a multi-signal fusion upper limb muscular tension increase detection method and device, and relates to the technical field of muscular tension increase detection. The method comprises a data acquisition process, a parameter fitting process, a model training process and a muscular tension increase detection process, passive elbow flexion and elbow extension are carried out at a set speed by driving a forearm with an elbow joint as an axis, mechanical data, kinematics data and myoelectricity data in the movement process are collected, and after the collected various data are preprocessed, the obtained data are subjected to model training and muscular tension increase detection. And training the muscular tension grade prediction model by taking a clinical evaluation result of a doctor on the muscular tension grade as a label, and predicting the muscular tension grade and the contribution degree of each feature to the prediction result through the trained muscular tension grade prediction model. According to the multi-signal fusion upper limb muscular tension increase detection method and device provided by the invention, accurate quantification of muscular tension levels is realized, and meanwhile, the reliability and interpretability of a detection result are enhanced.

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.

A method and device for providing clinical evaluation information by using an image are provided. The method for providing clinical evaluation information by using an image includes the steps of: actively or passively acquiring a photographed image, estimating a 3D posture of a person using only one of depth information and 2D body coordinate information of the person, obtained based on the photographed image, or by integrating the two pieces of information, analyzing a clinical determination criteria indicator according to the person's posture, on the basis of the 3D posture to analyze the person's motion, and generating and providing person's clinical evaluation information based on the clinical determination criteria indicator.

The invention discloses a method for non-contact human myocardial contractility detection by using millimeter waves, and relates to the technical field of myocardial contractility detection. The method for performing non-contact human myocardial contractility detection by using millimeter waves comprises the following steps: S1, constructing a millimeter wave radar array and initializing a space model; s2, adaptive adjustment of the dynamic space model; s3, millimeter wave echo signals are collected and preprocessed; s4, high-dimensional signal feature coding and dimension reduction; s5, myocardial contractility index mapping and clinical evaluation; through millimeter wave radar array construction and dynamic adjustment of a space model, non-contact accurate heart monitoring is realized, invasive operation of a traditional technology is avoided, discomfort of a patient is reduced, tiny motion changes of the heart can be captured with high precision, specific algorithm analysis is combined, the contractility of the cardiac muscle can be accurately evaluated, and the cardiac muscle can be accurately monitored. And real-time feedback is provided, and simple and convenient installation and operation can be realized without depending on a high-level technology of an operator.

The invention relates to a model for predicting an early gastric cancer lymph node metastasis risk and a construction method and application thereof. The input variables of the model comprise gender, tumor position, tumor maximum diameter, pathological differentiation degree, pT staging condition, submitted lymph node number and lymphatic vessel invasion condition, and the output value of the model is lymph node metastasis prediction probability. A model for predicting the lymph node metastasis risk of the early gastric cancer is designed, the lymph node metastasis risk of a patient with the early gastric cancer can be accurately evaluated through the model, a model for predicting the number of the lymph node metastasis is further obtained, a corrected pN stage (pNM) is provided based on the predicted number of the lymph node metastasis, and the establishment of the pNM stage can be used for predicting the lymph node metastasis risk of the patient with the early gastric cancer. The method can be applied to diagnosis of early gastric cancer patients who cannot perform sufficient lymph node cleaning due to various reasons, is helpful for clinicians to evaluate and calculate early gastric cancer patients who do not reach the sufficient number of detected lymph nodes, and provides a new way for accurate clinical evaluation.

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.

The invention relates to the technical field of neural signal processing, in particular to a behavior neural signal optimal weighted integration algorithm applied to language brain diseases, which comprises the following steps of: 1, acquiring a basic activity mode of a brain of a user through electroencephalogram data, and analyzing characteristics of low-frequency oscillation and a functional connection network; 2, in task state electroencephalogram data collection, a user completes a language processing task; step 3, identifying two sets of electroencephalogram indexes closely related to language brain diseases through feature extraction and screening; 4, through an iterative optimization method, different weights are given to different indexes, and the reasonability of weight distribution is verified through a machine learning technology; and 5, generating a comprehensive language brain health score for clinical evaluation. According to the method, multi-dimensional data are integrated into index weight distribution of neural signals, and brain multi-dimensional electrical data and behavioral data are combined, so that a set of efficient language brain function evaluation system is constructed.

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.