9290 results about "Medical practitioner" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention discloses a children MPP (Mycoplasma Pneumoniae) auxiliary diagnosis system based on multi-modal time series data modeling, and aims to improve the auxiliary diagnosis, clinical typing and severe prediction capabilities of mycoplasma pneumonia and reduce the workload of doctors. The MPP dynamic representation method based on multi-modal time sequence modeling is innovatively provided, multi-modal data such as images, electronic medical records and inspection indexes are fused, the time sequence feature embedding technology is adopted, and the evolution trend of the disease course can be effectively captured. Furthermore, deep fusion of different modal data is realized by using a progressive cross-modal semantic alignment strategy and a modal attention gating mechanism, and the problem of insufficient diagnosis accuracy caused by incompatibility of data modals in a traditional method is solved.

The invention relates to the technical field of medical image diagnosis, and discloses a medical image automatic diagnosis method and system based on deep learning. According to the method, multi-modal medical image data of a target object is acquired and standardized, a two-channel convolutional neural network is utilized to extract features, the features are processed through cross-modal feature fusion, adaptive attention weight distribution and other technologies, a cascaded two-way long-short-term memory network is adopted for modeling, abnormity is detected based on a probabilistic graph model, and the target object is identified. And the nidus is segmented by a multi-scale context information enhancement module, and finally a diagnosis suggestion is generated by a diagnosis inference engine driven by a knowledge graph. The system comprises a multi-modal image acquisition interface module, a distributed feature calculation cluster, a visual interaction terminal and a security audit module. According to the method, the accuracy and efficiency of medical image diagnosis can be improved, comprehensive diagnosis reference is provided for doctors, and meanwhile data safety and privacy are guaranteed.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention discloses a medical history recognition method and system based on a large model and an expert strategy, and the method comprises the steps: collecting data in a medical network, and carrying out the cleaning and standardization processing of the data; extracting medical entities and relationships thereof from the text, and optimizing the rule set; the extracted entities and relations are fused into a knowledge graph, a multi-relation graph is constructed, and attributes are defined; storing the knowledge graph in a graph database, and receiving new data through a real-time data stream processing system; basic information and symptom description of a patient are collected, and key information is extracted by using a large language model; an inquiry path is dynamically adjusted, and specific symptoms are clearly and fuzzily answered or deeply discussed by questioning; and generating a structured electronic medical record by using the large language model. Through an intelligent pre-inquiry process, the system can collect detailed medical history information and generate a standardized electronic medical record before a patient officially sees a doctor, so that the information verification and supplement time when a doctor receives the doctor is greatly shortened.

The invention belongs to the technical field of medical robots, and particularly relates to a mammary gland puncture surgical robot navigation system and device based on multi-modal image fusion. The multi-mode image data are registered and fused, high-precision three-dimensional reconstruction of the mammary gland tissue is achieved, more visual and accurate surgical navigation information is provided for doctors, the position of the puncture needle is tracked in real time, space registration is conducted on the puncture needle and the image data, and therefore the precision and safety of the surgery are improved, and the operation efficiency is improved. Meanwhile, puncture force feedback information of a puncture needle in the needle moving process is collected in real time, the implementation effectiveness of a puncture navigation path is evaluated, when it is found that the puncture path has risks, dynamic adjustment is conducted immediately, a new puncture path bypassing an obstacle area and reaching a focus is planned again, and the risk of the puncture path is avoided. And if the safe path cannot be obtained, the system automatically stops needle insertion and gives an acousto-optic early warning prompt, so that the surgical risk caused by manual misoperation in the traditional surgery is avoided.

The invention discloses an auxiliary scheme generation method and system for dysphagia rehabilitation, belongs to the technical field of medical rehabilitation intelligent evaluation, and is used for solving the problem of poor optimization of a swallowing training path. According to the method, myoelectricity, tongue pressure, throat images and acoustic signals in the swallowing process are collected through a multi-channel sensing device, a unified multi-mode swallowing behavior feature vector is constructed, obstacle stage recognition and functional grade judgment are achieved in combination with a pre-training model and historical data, and a structured evaluation result is output; matching training actions with high adaptation degree based on the knowledge graph, fusing muscle group matching degree, edge weight and function level factors to generate a personalized training path, and setting strength, frequency and rhythm parameters; in the training process, the motion and myoelectricity deviation is monitored in real time, a prompt signal is output, and the behavior completion degree is recorded; and the rehabilitation progress map is dynamically updated according to feedback, and if the deviation exceeds the limit, the path is automatically reconstructed and doctor intervention is synchronized, so that the rehabilitation efficiency and safety are improved.

The invention relates to the technical field of medical informatization, and discloses a method for generating a medical record report based on a doctor-patient dialogue, and the method comprises the following steps: multi-modal data collection: collecting the multi-modal data of the doctor-patient dialogue, including voice data, video data and environment data; multi-modal data preprocessing: preprocessing the collected data, including noise reduction and feature extraction of voice signals, key point extraction of video data and time synchronization of the multi-modal data; and semantic analysis: converting the voice signal into a text by using a voice recognition technology, carrying out semantic analysis on the text, and extracting key information. Through multi-modal data acquisition, semantic analysis, emotion modeling and knowledge graph reasoning technologies, the comprehensiveness, logicality and dynamic correction ability of medical record generation are improved, meanwhile, the defects of patient psychological state analysis in the prior art are overcome, information orderliness and doctor decision-making efficiency are remarkably improved through generation of the visual medical record report, and the medical record report generation method is suitable for popularization and application. And powerful support is provided for precise medical treatment.

The invention discloses an image lesion recognition system for assisting a doctor in the digestive system department in sampling inspection. The image lesion recognition system comprises an image acquisition module, a preprocessing module, an AI lesion recognition module, a feature analysis module, a sampling recommendation module, a data storage module, a real-time interaction module, a system control center, a disinfection monitoring module and a power consumption optimization module. The system integrates white light, narrow-band light and fluorescent endoscope images based on a multi-modal large model, accurately identifies focuses such as polyps and ulcers in real time and marks boundaries, quantitatively analyzes the forms, infiltration depths and texture features of the focuses in combination with super-pixel segmentation and three-dimensional reconstruction technologies, and significantly improves the diagnosis objectivity. According to the system, a sampling scheme matched with a clinical guide is dynamically generated by relying on a reinforcement learning strategy and biomechanical simulation, a focus three-dimensional model and a real-time endoscope picture space are subjected to registration through an augmented reality technology, an operation path is guided by means of tactile feedback, and the accuracy and efficiency of doctor operation are greatly optimized.

The systems, devices, and methods presented herein use a blood pump to obtain measurements of cardiac function. The system can quantify the functioning of the native heart by measuring certain parameters / signals such as aortic pressure or motor current, then calculate and display one or more cardiac parameters and heart function parameters, such as left ventricular pressure, left ventricular end diastolic pressure, or cardiac power output. These parameters provide valuable information to a user regarding current cardiac function, as well as positioning and function of the blood pump. In some embodiments, the system can act as a diagnostic and therapeutic tool. Providing cardiac parameters in real-time, along with warnings about adverse effects and recommendations to support cardiac function, such as increasing or decreasing the volumetric flow rate of blood pumped by the device, administering pharmaceutical therapies, and / or repositioning the blood pump allow clinicians to better support and treat cardiovascular disease.

The invention discloses a brain-computer hybrid intelligent closed-loop swallowing rehabilitation system for treating dysphagia after stroke, and relates to the field of artificial intelligence medical instruments. The system is composed of a plurality of key modules: an intelligent swallowing brain-computer interface module for accurately identifying a swallowing intention through multi-modal physiological signal acquisition and processing; the intelligent swallowing functional electrical stimulation module is used for finely regulating and controlling stimulation parameters according to the condition of the patient; the AI auxiliary online integrated module is used for realizing non-invasive brain-controlled swallowing and high-precision AI regulation and control; the closed-loop intelligent feedback regulation and control module is used for real-time monitoring, early warning and self-adaptive optimization of electrical stimulation; the cloud platform remote monitoring and data storage module supports remote diagnosis and treatment of doctors; and the man-machine interaction interface display and printing module optimizes doctor-patient interaction. According to the system, a brain-computer interface and FES are combined, a personalized, safe and efficient rehabilitation scheme is provided for a patient based on data driving and an artificial intelligence algorithm, swallowing function recovery can be accelerated, complications are reduced, and the system has great clinical and social values.

The invention provides a method for establishing a diagnosis and treatment system for digestive system disease multi-modal information. The method comprises the following steps: S1, collecting multi-modal information for labeling and preprocessing; s2, extracting a feature vector and embedding a label into the multi-modal information according to the labeled information; s3, splicing and mapping the feature vector and the tag to a unified dimension to obtain an enhanced feature vector; s4, fusing the enhanced feature vectors to form a multi-modal feature matrix, performing linear mapping and weighted aggregation on the multi-modal feature matrix to obtain global fusion vectors, and collecting to generate a fusion vector sequence; s5, enhancing the time sequence information of the global fusion vector sequence, enhancing the spatial information of the spatial relevance of the specific feature of the part, and performing interactive fusion to obtain a spatio-temporal joint feature; s6, performing classification prediction on the disease stage or the specific pathological type, and outputting a diagnosis result; and S7, performing semantic association on the diagnosis result and the medical knowledge graph, sharing data to an online health intelligent platform, and providing a personalized decision basis for clinicians.

The invention discloses an image report generation method, system and device based on multi-modal learning optimization, and the method comprises an image retrieval module which is used for carrying out the image feature extraction of an input medical image, and obtaining similar medical images and reports; the simulation generation module is used for guiding an image report generation model to generate an initial draft report by utilizing the medical image and the report; the personalized diagnosis module is used for carrying out personalized adjustment on the report generation model according to the historical report preference of the doctor or the historical medical data of the patient; the reinforcement learning optimization module is used for dynamically adjusting a text generation mode based on a reinforcement learning report optimization strategy and optimizing an image report generation model in combination with doctor scoring, automatic evaluation and clinical feedback; and the iterative refining module is used for performing text retrieval by using the initial draft report as a query, acquiring text fragments which are semantically similar to the initial draft report, and performing neural symbol reasoning optimization to generate a final report.

The invention discloses a semantic enhancement auxiliary inquiry system and method based on a medical knowledge graph, and the system comprises a patient information receiving module, a theoretical knowledge graph storage library, an evidence-based knowledge graph storage library, a double-track diagnosis path construction module, a medical knowledge conflict judgment module, and a semantic enhancement report generation module. The double-track diagnosis path construction module inquires the received patient information in a theoretical knowledge graph storage library and an evidence-based knowledge graph storage library which are independent from each other in parallel, and a theoretical diagnosis path and an evidence-based diagnosis path are generated respectively; the medical knowledge conflict judgment module dynamically compares the two paths in real-time interaction, and performs priority judgment according to a preset medical judgment rule; finally, the semantic enhancement report generation module presents the two paths and the conflict judgment result to the user at the same time. Transparency and interpretability of the whole interrogation process are assisted, and reliable semantic enhancement decision support is provided for doctors when the doctors face complex medical knowledge conflicts.

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

The invention provides a patient information collection and medical record construction system and method based on multiple rounds of dialogues, and the system comprises an intelligent guide interaction module which receives the natural language input of a patient; the context-aware question and answer engine adopts a dialogue state representation method based on a graph structure to construct entities, relationships and attributes of each round of dialogue into knowledge sub-graphs; the medical record information dynamic builder monitors an updating event of the dialogue state diagram in real time; the abnormal information detection module adopts a mixed conflict detection method combining rules and learning; and the medical knowledge graph support system maps the oral expression of the patient to a standard medical term system in real time through a multi-level semantic matching strategy. According to the invention, the doctor does not need to distract the record in the inquiry process, and can pay more attention to patient observation and clinical thinking. The quality of the medical record first draft automatically generated by the system is high, a doctor only needs to perform a small amount of auditing and supplementing, and the medical record writing time is greatly shortened.

The invention discloses a doctor-patient negotiation method and device fusing LLM and a multi-agent architecture, equipment and a medium, and relates to the technical field of doctor-patient negotiation. The doctor-patient negotiation method comprises the following steps: acquiring doctor-patient background information, and constructing a doctor agent, a patient agent and a coordination agent based on a large language model. The doctor agent generates a preliminary treatment regimen. The patient agent generates feedback to the preliminary treatment regimen. The coordination agent parses the feedback, generating a structured acceptance score and a semantic vector. The dynamic belief is updated based on the semantic vector doctor agent. And performing fuzzy utility evaluation on the current treatment scheme, and judging whether strategy adjustment is triggered or not. And the coordination agent calculates the adjustment amount of each issue based on the acceptance difference value, the patient yielding willingness coefficient and the maximum yielding amplitude, and obtains a new quotation scheme. And triggering a belief calibration and strategy reconstruction process at a preset stage node of multiple rounds of negotiation to obtain a new strategy. And when the negotiation round reaches a preset maximum round number, outputting a final treatment scheme.

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 an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention relates to the technical field of large models, in particular to a TNM (Tumor Necrosis Model) staging prediction method and system in a clinical text of a large language model. Comprising the steps of data acquisition and preprocessing, instruction data acquisition, large language model instruction fine tuning, clinical text preprocessing, medical event extraction, preliminary event processing and screening, cue word engineering optimization, RAG technical framework combination, TNM staging result generation and JSON formatting output. By combining the natural language processing capacity of the large language model and the external knowledge retrieval function of the RAG technical framework, the efficient, accurate and wide-adaptability TNM staging prediction method and system are provided, the automation degree of clinical tumor staging can be effectively improved, a reliable staging basis can be provided for doctors, and the clinical tumor staging prediction efficiency is improved. With the continuous development of medical big data and artificial intelligence technology, the method has a wide application prospect and potential market value.

The invention discloses a hierarchical chronic disease management and referral system based on artificial intelligence, and belongs to the technical field of intelligent medical treatment, and the system comprises the steps: setting upstream and downstream referral paths composed of diagnosis and treatment institutions, binding responsible doctors corresponding to chronic diseases for the diagnosis and treatment institutions of each level, and forming a referral relation mapping table; predicting a chronic disease dynamic risk based on the multi-source and multi-modal data of the target crowd, classifying the target crowd, generating a chronic disease management suggestion for each type of crowd, and pushing the chronic disease management suggestion to a chronic disease doctor for contract signing management; generating referral suggestions based on the chronic disease dynamic risk changes of each type of people and pushing the referral suggestions to chronic disease doctors, and the chronic disease doctors determining the referral suggestions, setting referral information based on upstream and downstream referral paths and a referral relation mapping table and synchronously notifying target diagnosis and treatment institutions and patients; performance excitation is performed on whether a chronic disease doctor completes contract signing of a chronic disease patient, implementation of graded referral treatment and medical cost saving, so that the medical service precision can be improved, the diagnosis and treatment efficiency is improved, and medical cost throttling is promoted.

The invention relates to the field of medical treatment, and discloses a cardiac surgery risk assessment system based on artificial intelligence. The system fuses electrocardiogram, image data and physiological data of wearable equipment, and data quality is ensured through a data preprocessing module; carrying out data dimensionality reduction and nonlinear mapping by using manifold learning and the method, and capturing a relationship between complex physiological signals; the sensor weight is optimized by adopting the maximum entropy principle, and the accuracy of the evaluation result is improved; performing space-time alignment of the multi-modal data based on the Lie group and Lie algebra theories; modeling the fused data by using a machine learning algorithm to generate a risk assessment result; the real-time feedback module dynamically adjusts the evaluation model and system parameters, and the output module displays an evaluation result through a visual interface to assist doctors in accurate decision making. The system improves the accuracy and real-time performance of assessment, and provides scientific and comprehensive risk assessment support for cardiac surgery.

The invention discloses a medical image report generation method based on multiple agents. The method comprises the following steps: step 1, constructing an image segmentation agent, a cue word retrieval agent, a report generation agent and an image database; 2, inputting the medical image into an image segmentation agent, and generating a focus label through the image segmentation agent; 3, inputting the focus label into a cue word retrieval intelligent agent, and obtaining an example report related to the focus label by the cue word retrieval intelligent agent; and 4, taking the focus label and the example report as input of a report generation agent, outputting a preliminary image report by the report generation agent, and then introducing a terminology condition constraint and reinforcement mechanism to correct the preliminary image report to generate a final image report. According to the invention, the workload of doctors can be reduced while the image analysis report which is accurate and meets the medical standard is generated.

The invention discloses an image report generation method, system and device based on multi-granularity knowledge fusion, and the method comprises an image report pre-generation unit which is used for receiving a medical image as input, and processing the medical image through an image report generation model to obtain an initial image report text; the knowledge retrieval unit is used for retrieving related medical entities and definition knowledge thereof from a public medical knowledge base, a local self-built medical knowledge base and a real-time data source based on the initial image report text to obtain multi-source medical knowledge; the knowledge fusion unit is used for fusing the multi-source medical knowledge to obtain a fused knowledge text; and the final report generation unit is used for combining the medical image, the initial image report text and the fused knowledge text, and regenerating an image report through an image report generation model to obtain a final image report. According to the method, diagnosis errors can be remarkably reduced, the working efficiency of doctors is improved, and meanwhile reports easier to understand are provided for patients.

The invention relates to the technical field of large language models, in particular to a medical question and answer data generation method and system, a terminal and a medium, and the method comprises the steps: constructing a patient agent and a doctor agent, and setting a dialogue strategy between the patient agent and the doctor agent; real medical records are obtained from the medical database, and knowledge bases of the patient intelligent agent and the doctor intelligent agent are constructed based on the real medical records; and controlling the patient agent and the doctor agent to simulate doctor-patient dialogues based on the dialogue strategy and respective knowledge bases, and generating original question and answer data. Two agents are set, real doctor-patient interaction is simulated, and the authenticity and practicability of the generated original question and answer data are improved. The doctor-patient dialogue is performed based on the real medical record, it is ensured that the generated original question and answer data meets medical specifications and professional requirements, and the accuracy is high. A large amount of original question-answer data can be automatically and quickly generated, the training efficiency is improved, and the training cost is reduced.

The invention relates to the technical field of medical image processing, and particularly discloses a lung focus recognition method and system based on image deep learning, and the method comprises the steps: carrying out the enhancement and registration of an input lung image, and constructing a marking data set; constructing a convolutional neural network model with a multi-scale receptive field, introducing an attention mechanism to focus the model on a lesion area, coarsely and finely identifying the lesion position and estimating the size of the lesion position; further purifying a focus area and removing artifacts and noise by applying morphological filtering and connected domain analysis; and carrying out uncertainty evaluation on the identification result to obtain a focus identification result. According to the method, local details and global structure information in the lung image can be comprehensively captured, the model can be more accurately focused on the lesion area in combination with an attention mechanism, and the lesion recognition accuracy is improved. According to the invention, confidence information of an identification result is provided for doctors. And for a low-confidence result, measures such as manual judgment or re-collected data identification can be taken, so that the diagnosis reliability is improved.

The invention provides a disease screening system based on a large model. The disease screening system is used for solving the technical problem that an existing disease screening system is intelligently used for single special disease screening. The system comprises a scheduling model and a plurality of AI auxiliary diagnosis models, the scheduling model is connected with the plurality of AI auxiliary diagnosis models, and the scheduling model is connected with a big data disease library. According to the method, a high-level scheduling model is utilized, multiple single AI auxiliary diagnosis models are managed in a centralized mode, automatic calling of a multi-disease AI system is achieved, automatic structured report generation is achieved through a large language model, historical medical history is combined, progress is predicted, treatment suggestions and reference cases are automatically given, an existing clinician film reading workflow is fitted, and the efficiency is improved. The whole process of actual diagnosis decision making of doctors is greatly fitted, and the working efficiency of the doctors can be improved to the maximum extent.

The invention discloses an insulin infusion decision-making system and method combining reinforcement learning and metabolism simulation, and belongs to the field of clinical decision-making assistance. Comprising an acquisition processing module, a metabolism simulation module, a dynamic updating module, a path simulation module, a strategy control module, a monitoring constraint module, a decision analysis module, a correction processing module, a training optimization module and an injection control module. The method can accurately reflect the metabolic characteristics of the patient, makes the insulin decision more targeted, realizes automatic adjustment of the model, keeps real-time perception and response to the state of the patient, avoids the risk caused by model aging or error accumulation, enhances the long-term control ability, improves the control efficiency while guaranteeing the blood glucose stability, and reduces the cost. The fault-tolerant capability of the system is improved, the interpretability and safety guarantee of clinical decisions are improved, and the credibility of doctors is enhanced.

The present invention relates to the technical field of medical data processing. Disclosed are an electroencephalogram-based cognitive decline risk assessment method and system. In the present invention, a plurality of electroencephalogram representations in electroencephalogram data of a target group and behavioral data of the target group are used to perform training to obtain a first cognitive decline risk assessment model, a second cognitive decline risk assessment model and a third cognitive decline risk assessment model, wherein the first cognitive decline risk assessment model mainly includes spectral information, information included in the second cognitive decline risk assessment model is mainly connection information of a brain network, and the third cognitive decline risk assessment model mainly includes neural spatio-temporal dynamics information. Thus, doctors can be assisted in performing efficient and accurate cognitive decline risk assessment on subjects to be assessed, and patients can be classified on the basis of the cognitive decline risk types of the patients, thereby helping the doctors to quickly formulate rational diagnosis and treatment strategies and improving the working efficiency and quality.

The invention provides an image segmentation labeling method and system based on machine learning, and relates to the technical field of computer vision, and the method comprises the steps: dynamically selecting three detection points in a key region of a segmentation boundary based on the boundary form of an initial segmentation result, and constructing a dynamic triangular mesh unit; calculating a geometric characteristic value according to the vertex coordinates of the dynamic triangular mesh unit, wherein the geometric characteristic value comprises a proportional relation between the area and the side length of the triangular mesh unit; comparing the geometric characteristic value with a preset threshold range, and if the geometric characteristic value exceeds the threshold range, generating a boundary coordinate correction parameter; and adjusting boundary coordinates of the lung region segmentation result according to the correction parameter, and generating a corrected lung region segmentation result. Through end-to-end operation from image acquisition to lesion marking, the workload of doctors is reduced, and based on the segmentation result, the lesion marking unit can accurately position the lesion position.