99 results about "Clinical exam" patented technology

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction are improved.

The invention provides a chronic disease risk assessment and intervention strategy generation system based on data analysis. According to the system, multi-source heterogeneous information including clinical examination, behavior records, environment data and the like is collected, key features are extracted through a data fusion technology, and time and space features of data are enhanced through a space-time weighted tensor decomposition method. And in combination with a causal reasoning technology, the system can accurately evaluate the chronic disease risk of an individual, eliminate confounding factors and provide more reliable risk prediction. In addition, the system dynamically generates a personalized intervention strategy through a reinforcement learning algorithm, adjusts intervention measures according to real-time health data, and ensures accurate chronic disease management. The method has an efficient risk prediction capability and a personalized intervention scheme, and is helpful for improving the accuracy and effect of chronic disease management.

The invention provides a method and a system for evaluating various liver pathologies based on machine learning. The method comprises the following steps: acquiring weight information, clinical examination information and evaluation results of liver tissue samples of a plurality of historical patients; performing data preprocessing based on the acquired information, and constructing a training set and a test set; training a Logistic regression model and a Ridge regression model by using the training set, determining the performance of the model in predicting balloon sample deformation, fatty degeneration, lobular inflammation, significant fibrosis and NASH, and determining final prediction index parameters and a prediction model; and obtaining weight information, clinical examination information and an evaluation result of the liver tissue sample of the patient, extracting related values of prediction index parameters in the evaluation result, and processing the prediction index parameters by the determined trained prediction model aiming at the corresponding prediction index parameters to obtain a final prediction result. According to the method, the simple model is constructed by obtaining the clinical indexes, and all aspects of the liver pathology of the chronic liver disease can be comprehensively evaluated.

The invention relates to a medical image report generation method and device based on multi-modal data. The method comprises the following steps: acquiring original multi-modal data and preprocessing the original multi-modal data, wherein the multi-modal data comprises image data, complaint text data and clinical examination text data; predicting and generating a corresponding medical image report by using the trained multi-modal deep learning model; the prediction process specifically comprises the following steps: based on preprocessed image data, extracting image features by using a trained convolutional neural network model; based on the preprocessed chief complaint text data and clinical examination text data, using the trained artificial neural network model to extract semantic features; fusing the image features and the corresponding semantic features to obtain fused features; and inputting the fusion features into a full connection layer, and mapping to obtain a medical image report. Compared with the prior art, the method has the advantages of improving the reliability of medical image report generation, reducing the manual load and the like.

According to the parameter image generation method and device for shortening the dynamic PET scanning time, the electronic equipment and the storage medium, the 3D image of the time frame of the first 25 minutes of dynamic PET scanning is obtained, feature extraction is conducted on the 3D image, the multi-scale feature map is generated through the network encoder part, the multi-scale feature map is mapped through the network decoder part, and the dynamic PET scanning time is shortened. According to the method, a Ki parameter image for network prediction is generated, the Ki parameter image for network prediction is optimized, the optimized Ki parameter image serves as input of next iteration, the steps are repeated until the set convergence condition is met, and the Ki parameter image meeting the requirement is finally obtained. And the quality of the generated Ki parameter image is beneficial to auxiliary diagnosis, so that the clinical examination efficiency and the comfort of a patient are remarkably improved, motion artifacts which are easy to occur during dynamic PET scanning are reduced as much as possible, and the quality of the generated Ki image and the diagnosis accuracy of a doctor are further ensured.

The invention discloses a model for screening temporal-mandibular joint disc anterior displacement. TMJ ADD risk probabilities are output according to data of a plurality of key features. Wherein the key features comprise the opening degree, the lateral movement of the left side of the lower jaw, the lateral movement of the right side of the lower jaw, the crowding degree of the upper dentition, the Wits value, the FH-NPo angle, the Co-Go, the ALFH / PLFH ratio and the G '-Sn-Pog' distance. After network deployment of the constructed model, interconnection and intercommunication are realized, so that primary hospitals and clinics can also utilize related indexes of a skull lateral position film in orthodontic routine examination and clinical examination results to screen the risk of temporal-mandibular joint disc anterior displacement before orthodontic treatment, the popularization rate of joint screening before orthodontic treatment can be greatly improved, and the screening efficiency of the joint before orthodontic treatment is improved. Therefore, the effect of clinical orthodontic treatment is guaranteed as a medical instrument.

By constructing an age-related macular degeneration animal model, experimental results in multiple aspects of clinical examination (FFA and ERG), pathological detection (RPE-choroidal patch IB4 fluorescent staining) and the like prove that the rucotinib can be used for effectively treating age-related macular degeneration, especially wet age-related macular degeneration.

The invention relates to the technical field of medical health, and discloses a health assessment system and method based on multi-dimensional retinal aortic aneurysm monitoring data, and the method comprises the steps: collecting a retinal image of a patient with retinal aortic aneurysm, and extracting the aortic aneurysm features of the patient with retinal aortic aneurysm, setting an eye dynamic monitoring network of the patient with the greater aneurysm of the retina; performing time synchronization processing on the iconography image, the clinical examination data, the biomarker data and the real-time eye data to obtain multi-modal synchronization data; based on the multi-modal synchronous data, determining a risk evaluation index of the patient with the greater retina aneurysm, and constructing a personalized risk analysis model of the patient with the greater retina aneurysm; creating a risk scoring mechanism for the patient with the greater retinal aneurysm, and setting a health feedback tool for the patient with the greater retinal aneurysm. According to the invention, accurate prediction of the retina great aneurysm can be realized.

The invention provides a medical cell detection method. The medical cell detection method comprises the following steps: S1, collecting a cell image for preprocessing; s2, carrying out structured processing and coding processing on the multi-modal data; s3, performing feature extraction on the cell image and the multi-modal data; s4, fusing the cell image and the multi-modal data; s5, inputting the fused feature vector into a segmentation head of the FastSAM; s6, generating a segmentation mask of the image; s7, judging whether the cells are abnormal or not; s8, re-sampling the abnormal cells for cell culture; s9, collecting a cell image in the cell culture equipment in real time, and preprocessing; s10, inputting the cell image into a FastSAM model, and analyzing various indexes of the cell based on the generated segmentation mask; according to the method, cell images, cases, clinical examination results and other multi-modal data are combined, the characteristics of the cells are known more comprehensively, and the detection accuracy is improved.

The invention discloses a clinical automatic diagnosis and treatment platform for temporomandibular joint disorder, and relates to the field of medical instruments. The platform comprises a patient subjective symptom evaluation system, a clinical diagnosis system, an image diagnosis system and a treatment scheme making system. The patient subjective symptom evaluation system is used for automatically obtaining a subjective feeling diagnosis result of the patient according to a subjective feeling questionnaire filled by the patient; the clinical diagnosis system is used for automatically obtaining a clinical diagnosis result of the patient according to the past medical history and clinical examination of the patient; the image diagnosis system is used for automatically obtaining an image diagnosis result of the patient according to the CBCT image and the MRI image of the patient; the treatment scheme making system is used for automatically making a treatment scheme. According to the platform constructed by the invention, subjective evaluation information of a patient, a clinical automatic diagnosis result and an imaging automatic diagnosis result are fused, a treatment scheme is automatically formulated, temporomandibular joint disorder can be found and diagnosed in time, and influences on treatment such as orthodontic treatment, orthognathic treatment, repair and implantation are avoided.

The invention discloses a genetic disease pathogenic site sorting and diagnosis auxiliary method and system based on multi-modal artificial intelligence. The method comprises the following steps: firstly, collecting genetic disease data to construct a heterogeneous knowledge graph; the method comprises the following steps: acquiring patient data, and executing an analysis process: generating multi-modal feature representation of candidate pathogenic sites from four dimensions of variation features based on rules, a tissue specificity mechanism, a protein three-dimensional structure and real-time literature evidence; carrying out fusion sorting on the features by using a sorting model, and generating an interpretable report and a clinical examination suggestion based on the uncertainty of a sorting result; and after the doctor executes examination according to the suggestion and feeds back newly added data, the analysis process is repeated until a preset iteration target is achieved. According to the method, the limitation of a static analysis model is broken through, multi-dimensional evidence fusion and clinical workflow embedding are realized, and the interpretation accuracy and diagnosis efficiency of the pathogenic site of the genetic disease are remarkably improved.

The invention discloses a system for evaluating coronary artery calcification points through photon CT non-electrocardiogram gated lung CT plain scanning, and relates to the technical field of coronary artery calcification point evaluation. Through the CT data acquisition module, the coronary artery calcification recognition module, the integral calculation evaluation module and the risk evaluation generation module, X-ray photons are directly counted, electronic noise is eliminated, the signal-to-noise ratio and the contrast noise ratio are improved, in conventional chest low-dose CT, electrocardio gating is not needed, and the cost is reduced. Automatic processing from low-dose lung CT flat scanning data acquisition to structured report generation is achieved, manual data transmission and intervention among all links are not needed, coronary artery calcification evaluation is fused into a conventional lung CT examination process, the extra burden caused by the fact that a patient needs to independently conduct coronary artery CT scanning is avoided, and the examination efficiency is improved; errors possibly caused by manual operation are reduced, consistency and reliability of evaluation results are ensured, and clinical examination and diagnosis processes are optimized.

Systems and methods for ophthalmic disease detection and risk assessment that combines digitally-augmented examination instruments with neural networks trained to analyze examination imagery across diverse patient populations and variable image qualities, enabling automated analysis and classification of eye conditions during routine clinical examinations.

1. Name of the product in this design: Wired oral endoscope. 2. Purpose of this design: For dental clinical examination, especially suitable for detailed observation and image acquisition of oral mucosal lesions. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

1. Name of the design product: Pupillary distance and pupil height measuring lamp. 2. Use of the design product: Used for ophthalmic optometry clinical examination to measure the pupillary distance and pupil height of patients. 3. Design key points of the design product: Lies in the shape. 4. Picture or photo that best shows the design key points: Perspective view.

The medical photodynamic multispectral electronic endoscope relates to the technical field of endoscopes and comprises an eyepiece seat and an objective lens, one end of the eyepiece seat is connected with a light transmitting bundle, the objective lens is mounted at one end, far away from the eyepiece seat, of the light transmitting bundle, a mounting cavity is formed in the eyepiece seat, one side of the mounting cavity is provided with an opening, and the opening is communicated with the eyepiece seat. A mounting cavity is formed in the eyepiece base, a mounting shell is clamped in the mounting cavity, through holes are formed in the two ends of the mounting shell, an eyepiece body is clamped in the mounting shell, sliding grooves are formed in the inner walls of the two ends of the side, away from the opening, of the eyepiece base, sliding rods are fixedly arranged in the sliding grooves, and the rod walls of the sliding rods are movably sleeved with sliding blocks. According to the utility model, the eyepiece quick-changing structure is arranged on the eyepiece seat body, so that a doctor can quickly change different eyepieces in a short time without complicated dismounting and mounting operations, the examination time is greatly saved, and the clinical examination efficiency is improved.

The present invention provides a method and system for evaluating various liver pathologies based on machine learning, which obtains the weight information, clinical examination information, and evaluation results of liver tissue samples of multiple historical patients; based on the obtained information, performs data preprocessing to construct a training set and a test set; uses the training set to train Logistic regression and Ridge regression models to determine the performance of the models in predicting ballooning degeneration, steatosis, lobular inflammation, significant fibrosis, and NASH, and determines the final prediction index parameters and prediction models; obtains the weight information, clinical examination information, and evaluation results of liver tissue samples of a patient, extracts the numerical values related to the prediction index parameters therefrom, and processes them with the determined trained prediction models for the corresponding prediction index parameters to obtain the final prediction results. The present invention constructs a simple model by obtaining clinical indicators and can comprehensively evaluate all aspects of the liver pathology of chronic liver diseases.

This invention discloses a multimodal respiratory disease-related data acquisition system and method. The data acquisition system includes a disposable oral-nasal breathing interface, an oral-nasal breath sound acquisition module, a bidirectional flow / differential pressure sensing module, a temperature and humidity auxiliary acquisition module, a chest and back multi-channel auscultation module, a channel auscultation front end, a trimodal respiratory data acquisition box, a mobile terminal annotation module, a clinical label association module, and a local storage module. The data acquisition method is as follows: Step 1, prepare the oral-nasal breathing interface and acoustic coupling patch; Step 2, start the trimodal respiratory data acquisition box; Step 3, the patient performs quiet breathing; Step 4, the patient performs a maximal inhalation followed by a forceful exhalation; Step 5, associate the clinical examination results with the acquired data; Step 6, output risk scores, abnormality prompts, or classification prompts. Beneficial effects: Reduces wind noise interference, allows acoustic signals and lung function data to be acquired at the same time reference, and reduces frictional noise and poor contact.

The application provides a kind of based on deep learning's absorbed dose prediction method, device, equipment and medium, it is related to medical image processing technical field, comprising: obtaining the treatment of patient's pre-test multi-modal data;According to clinical examination data and the mask map of the target tumor region and normal key organ region of pre-set drug, space coding is carried out, and the biomarker feature map of the patient to be tested is constructed;According to the pre-treatment medical image and biomarker feature map, a preset first deep learning model is used for prediction, to generate the simulated treatment initial medical image of the patient to be tested;According to the simulated treatment initial medical image, a preset second deep learning model is used for prediction, to generate the post-treatment predicted dosimetry parameter map of the patient to be tested.The application can improve the accuracy of nuclide absorbed dose prediction evaluation.

An intelligent homing type lens box comprises: a box body, which is internally provided with a chamber, and any side of which is provided with a lens recovery port; the try-on lens box is arranged on any side of the box body, and a plurality of lens grooves for accommodating lenses are formed in the try-on lens box; the feeding track is arranged in the cavity, and the feeding track is provided with an inlet end communicated with the lens recovery opening and a detection end used for positioning a to-be-returned lens; the detection mechanism is used for detecting parameters of the to-be-homed lens and is arranged corresponding to the detection end; and the sorting mechanism is used for transferring the to-be-homed lenses into the lens grooves matched with the parameters of the to-be-homed lenses in the try-on lens box based on the detection information acquired by the detection mechanism. According to the utility model, full-automatic identification and homing of the try-on lenses can be realized, manual searching for storage positions and degree comparison are not needed, and the sorting time is remarkably saved. The workflow automation degree of an optometry consulting room can be remarkably improved, non-diagnosis and treatment labor is reduced, and doctors can put more energy into clinical examination and analysis.

A multifunctional diagnostic system includes a reusable speculum with a modular head and a portable digital twin station. The speculum includes a set of heated blades configured to comfortably open the vaginal canal, a numbing spray nozzle to anesthetize the cervix, and an integrated light source to illuminate the internal anatomy for clinical examination. The same speculum head supports a vaginal sweep using embedded imaging components and sensors to capture interior anatomical data. The captured data is wirelessly transmitted to a companion digital twin system that includes a processor, memory, and smart display. The system implements multi-spectral imaging configured to generate a digital twin of the patient's anatomy, which may be used for assessment, diagnosis, treatment planning, education, and analysis with artificial intelligence and extended reality platforms. A portable housing station serves as both a charging dock and sanitization unit.

The invention is suitable for the technical field of image transmission, and provides an image stream transmission method and device and a computer program product. The method comprises the following steps: in response to a device binding operation of a user, starting a scanning function of a wearable display terminal to scan a device identification code of a medical device, and obtaining a network address and communication port information of the medical device; based on the network address and the communication port information, initiating a remote connection establishment request carrying the equipment authorization customer dependence information to the medical equipment so as to establish a remote procedure call connection with the medical equipment; based on the remote procedure call connection, sending a first media connection establishment parameter to the medical equipment, and receiving a returned second media connection establishment parameter; establishing point-to-point media transmission connection with the medical equipment according to the first media connection establishment parameter and the second media connection parameter; and based on the point-to-point media transmission connection, receiving and displaying the image stream pushed by the medical equipment, so as to improve the image access efficiency in the clinical examination process.

1. Name of the designed product: surgical microscope. 2. Use of the designed product: for clinical examination and surgery. 3. Design points of the designed product: in shape. 4. Picture or photo that best shows the design points: front view.

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 probiotic strain formula scheme screening method. The method comprises the following steps: establishing a disease model database according to a clinical examination result, performing standardization processing and data layering on the disease model database to obtain a layered database, scoring and grading diseases in the layered database, and establishing a disease model grading data matrix of patients; establishing a strain curative effect matrix according to the strain database and published clinical research data; and associating the disease model grading data matrix with the strain curative effect matrix, screening a relationship with high association strength, constructing a disease-microbial treatment response model, and obtaining different types and different levels of diseases and corresponding recommended bacterial systems through the disease-microbial treatment response model. And grading the synergistic effect of the disease and the recommended strain, filtering the score through a threshold value to screen out a scheme of a strain formula, and outputting the scheme of the strain formula through a user interface.

The invention relates to the technical field of artificial intelligence, in particular to a patient information analysis method based on multi-modal fusion, and the method comprises the steps: obtaining a target liver CT image of a patient, marking the target radiology characteristics of the target liver CT image and the target clinical examination information of the corresponding patient, coding the target liver CT image, and obtaining the image coding characteristics; the method comprises the following steps: marking and coding a target radiology feature to obtain a radiology coding feature, coding clinical examination information to obtain an examination information coding feature, fusing an image coding feature, the radiology coding feature and the examination information coding feature to obtain a cross-modal fusion feature, and using a classification head module in a trained deep learning model to carry out classification on the cross-modal fusion feature. And performing cirrhosis outcome classification on the cross-modal fusion features to obtain a cirrhosis outcome classification result. The fusion features of the liver information of the patient are subjected to outcome classification in an automatic fusion mode, so that the classification result of the liver cirrhosis outcome is predicted more accurately.

The present application relates to a kind of POVIC drug efficacy prediction method, the prediction method includes: obtaining prediction object, lung function index FEF 50％ And exhaled nitric oxide (FeNO), three data are processed by regression, obtain P value, when P value is less than or equal to preset value, predict the drug effective for treating POVIC as: compound methoxyphenamine and ICS / LABA joint;When P value is greater than preset value, predict the drug effective for treating POVIC as: compound methoxyphenamine is used alone.The present application also provides corresponding prediction device, computer readable storage medium and its electronic equipment.The two clinical examination indexes used can be obtained by hospital lung function result report, without carrying out complex additional examination, greatly reduce the examination times and examination cost of patient.

The objective of the present disclosure is to provide a technique for determining an order of application of a patch in consideration of severity of risk of patient harm (severity of harm × occurrence frequency) when vulnerability of software installed in a clinical examination device is exploited. A security patch management system according to the present disclosure determines an order in which a security patch is applied to software installed in a clinical examination device according to a magnitude of an influence in the case of occurrence of a risk and the probability of occurrence of the risk, and when the orders determined for two or more pieces of the software are the same, re-determines the order so that the software having greater influence in the case of occurrence of the risk is given an earlier order (refer to FIG. 10).

Triggering information that defines conditions for presenting a virtual clinical exam may be downloaded to a user device. The user device may collect sensor data and use the sensor data to evaluate the conditions. When certain conditions are fulfilled, the user device may present the virtual clinical exam at the user device.