60 results about "Medical model" patented technology

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The application discloses an electronic medical record and a cataloging and classifying method, system and device thereof. The method comprises the following steps: obtaining standardized data by preprocessing multi-format medical record original data through a medical OCR model, a VAE anomaly detection algorithm, a medical knowledge graph word segmentation tool and a Transformer term standardization model; checking semantic rationality, extracting multi-dimensional features, and obtaining a comprehensive feature vector after strengthening and fusing; constructing a transfer learning classification model and training the model, inputting the feature vector to generate cataloging information; and finally, evaluating the classification result through an active learning mechanism, combining artificial labeling data and a causal forest dynamic updating framework, and incrementally learning and optimizing the model performance. The application solves the problems of low multi-format data extraction accuracy, insufficient term standardization and poor model generalization in the prior art.

The invention provides a method and system for improving reliability of medical decision and application. The method comprises the following steps: constructing a data set covering any disease according to medical data; training the medical vertical large model according to the data set to obtain a medical decision set; performing priority ranking on any decision in the medical decision set by constructing a multi-dimensional evaluation system to obtain a primary medical decision ranking result; and dynamically adjusting a weight parameter and a decision rule of any medical decision in the primary medical decision sorting result by taking the feedback suggestions and the actual diagnosis and treatment effects as reward signals to obtain a final medical decision sorting. According to the method, the problems of data fragmentation and illusion faced by the current large medical model and the industrial pain points of insufficient application suitability are effectively solved, and the clinical diagnosis and treatment efficiency and quality are remarkably improved.

This invention discloses a method and system for developing a medical dataset based on a large-scale model. The method includes: acquiring multiple medical data sets to be processed; performing privacy data identification and encryption on each medical data set based on a de-identification algorithm to obtain multiple de-identified medical data sets; labeling each de-identified medical data set with medical information according to multiple preset student models to obtain corresponding labeled data; sampling and verifying all labeled data according to the teacher model corresponding to the student model, and organizing the labeled data into a medical dataset based on the verification results; the medical dataset is a pre-training dataset, instruction fine-tuning dataset, and / or test dataset for large-scale model training. Therefore, this invention can effectively ensure privacy and security during large-scale medical information processing, providing high-quality and standardized data asset support for the robust training of subsequent large-scale medical models.

This application provides a method, apparatus, device, and storage medium for self-correction of a medical visual language model based on a dynamic experience base (DEKB), relating to the field of medical visual language processing technology. The method includes: when the initial diagnostic result of the medical visual language model for a current clinical case does not match the fact label, constructing the current case as a structured experience unit and storing it in a dynamic experience knowledge base; upon receiving a query, retrieving historical experience cases related to the new query from the dynamic experience knowledge base; using the historical experience cases as contextual prompts to guide the medical visual language model in chain-like thinking, generating a corrected reasoning result. By constructing an endogenous dynamic experience knowledge base, designing a deep attribution analysis mechanism, and employing a dual-threshold retrieval algorithm, this application enables DEKB to transform the model's historical errors into structured knowledge that can be used for future reference, significantly improving the robustness and generalization ability of medical model image diagnosis reasoning.

The invention discloses an intelligent monitoring and early warning system for medical adverse events, and relates to the technical field of medical treatment, and the system comprises an ETL module which is used for extracting medical text data from a hospital clinical information system; the medical adverse event prediction module is used for predicting the medical text data and obtaining the medical adverse event category and grade corresponding to the medical text data; the medical adverse event prediction module comprises a medical adverse event model obtained through training; the medical adverse event model is obtained by training a medical model according to historical medical adverse event samples, and the medical adverse event model can output corresponding medical adverse event types and levels according to input medical text data; according to the medical adverse event reporting method and device, the medical adverse event reporting efficiency can be improved.

The invention discloses a clinical prediction model construction method and system for treating oligometastatic non-small cell lung cancer through radioactive particle implantation, and relates to the technical field of medical model construction, and the method comprises the steps: obtaining patient data, taking survival time as a main endpoint event, employing a Cox regression model to carry out single-factor analysis on factors affecting patient prognosis, and obtaining a single-factor analysis result; determining the relationship between each clinical feature and survival time, and incorporating the important factors influencing the prognosis into multi-factor Cox regression analysis to determine independent risk factors influencing the survival prognosis of the patient; generating a first clinical prediction model according to each independent risk factor nano-construction; cD3 + AC, CD4 + AC, CD8 + AC, combined treatment, complete treatment and the number of metastatic lesions are taken as variables together to be included in construction of a clinical prediction model, and a second clinical prediction model is manufactured and generated. Independent risk factors influencing the prognosis of a radioactive particle implantation treatment few-metastatic NSCLC patient are confirmed, the clinical prediction model is established, the treatment prognosis can be accurately predicted, and the treatment cost is reduced. And screening of dominant benefit people is facilitated.

The invention provides an artificial intelligence diagnosis and treatment analog simulation system and method, and the system comprises a knowledge graph construction module which is used for constructing the knowledge association relation among the medical information, the disease type and the treatment scheme of a patient, generating a medical knowledge graph, and carrying out the iteration; the medical model construction module is used for constructing a large medical model for disease recognition and treatment scheme generation; and the diagnosis and treatment aid decision-making module is used for performing personalized analysis on the medical information of the patient in combination with the knowledge graph construction module and the medical model construction module to generate diagnosis aid information and a treatment aid decision-making scheme. Therefore, by constructing the medical knowledge spectrogram and fusing the large-scale pre-training model ability, the medical information of the patient is automatically analyzed, the auxiliary information of the personalized diagnosis and treatment scheme is generated, and the auxiliary decision making function of the AI doctor role is achieved.

The invention relates to a method for constructing a chronic kidney disease risk prediction model after acute kidney injury, and belongs to the field of medical models. The method comprises the following steps: constructing a clinical complete prediction model by using an XGBoost algorithm, obtaining SHAP values of different factors, carrying out feature importance sorting by using the SHAP values, and identifying key prediction factors through sorting; performing a forward variable selection strategy, and expanding the number of candidate variables to a preset value by adopting a step-by-step forward selection method based on an SHAP value sorting result; cross validation and model optimization are carried out, the optimization objective is to maximize AUC of a training set, and generalization performance of different factor combinations is evaluated; constructing a simplified model, selecting a core prediction factor of which the prediction effect AUC drop range is smaller than a set value, and constructing a core prediction factor model; and carrying out simplified model verification. According to the method, a model can be constructed to accurately identify high-risk people suffering from chronic kidney diseases after acute kidney injury, a reliable basis is provided for clinical intervention, and the clinical application value of the model is finally improved.

Embodiments of the present disclosure disclose a medical model reply content evidence tracing method, device, medium and product. The method comprises: matching the question data with the knowledge text in the knowledge base to obtain a first confidence degree, which is the standard similarity between the question data and the knowledge text; based on the first confidence degree and the first best parameter, filtering out the actual candidate text from the knowledge base, combining the medical model to obtain the actual reply content; after the actual reply content is semantically segmented into multiple actual fine-grained segments, matching the actual fine-grained segments with the knowledge text to obtain a second confidence degree, which is the standard similarity between the actual fine-grained segments and the knowledge text; based on the second confidence degree and the second best parameter, filtering out the tracing evidence of the actual fine-grained segments from the knowledge base, and then the actual reply content tracing result can be displayed. Through RAG retrieval and tracing retrieval, the original source of the reply content can be checked reversely, high-precision tracing is realized, and the explainability is enhanced.

A knowledge-guided fine-tuning and optimization method for a large medical model, and a related apparatus. A method for enhancing the explainability of a large medical model on the basis of "multi-modal data plus expert knowledge" is constructed, with a focus on improving the diagnostic accuracy and explainability of models in the field of diseases. The method comprises: acquiring multi-modal data to be subjected to diagnosis; and inputting said multi-modal data into a knowledge-guided model to obtain a thought-guiding information part in a logic-chain prompt, so as to input the thought-guiding information part into a large medical model to obtain an output that has been normalized by means of chain-of-thought prompting.

System and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular

The invention discloses an image segmentation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed original 3D image which comprises a plurality of 2D slice images; first prompt information is received, second prompt information is generated based on the first prompt information, and the first prompt information indicates a target object in the 2D slice image; and based on the second prompt information, performing segmentation processing on the original 3D image through the 3D medical basic large model to obtain a 3D segmentation result of the target object. According to the image segmentation method, a complete 3D segmentation result can be obtained only by giving a small amount of first prompt information on the 2D slice image, a user does not need to prompt each layer of image, the workload is reduced, a medical basic large model is adopted, the applicability is wider, and end-to-end training does not need to be carried out.

This invention provides a medical auxiliary diagnostic system based on a large medical model and speech recognition, including a doctor's workstation deployed on hospital terminal equipment for receiving and displaying diagnostic reports and draft medical records for doctors to review and confirm before submitting to the hospital information system (HIS); an AI doctor client installed on the same terminal device as the doctor's workstation for real-time acquisition of doctor-patient dialogue audio, displaying system-generated follow-up questions and auxiliary prompts, displaying draft medical records and diagnostic reports, and communicating with the AI ​​server and the hospital data interaction server; and a hospital data interaction server, deployed independently in each hospital, for establishing a WebSocket communication channel to achieve real-time data transmission between the AI ​​doctor client and the doctor's workstation within the hospital, forwarding diagnostic reports, draft medical records, and doctor instructions. This system reduces the workload of doctors in data collection, recording, and analysis, and overcomes the shortcomings of existing technologies that rely entirely on manual labor.

The invention provides a medical care and health care data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of old people from a medical institution, a rehabilitation institution, an old-age care institution and a medical care and health care operation platform with data sharing authority; processing the multi-source heterogeneous data of the old people to generate standard fusion data of the old people; according to the standard fusion data of the elderly, the index identifier is generated, and the corresponding source information and the index identifier are sent to the block chain to be stored for use of the medical model, so that full-scene coverage of the input data of the high medical model is realized, and the accuracy of the medical model is improved.

This invention provides a digital fusion management method and system based on a large medical model, relating to the intersection of medical information technology and artificial intelligence. The digital fusion management method specifically includes: acquiring and generating multiple sets of prior information, including: prior information generated based on clinically relevant text, second and third prior information generated based on preliminary diagnostic information, and fourth prior information generated based on historical case knowledge base retrieval; processing examination videos based on the first set of prior information to locate one or more candidate video segments, and verifying the candidate video segments based on the second set of prior information to obtain a final set of retrieved segments; and generating a first conclusion for answering clinical questions and a second conclusion for reviewing preliminary diagnostic information based on the final set of retrieved segments and the multiple sets of prior information.

The application relates to a navigation method, system, computer device, storage medium and computer program product. The method comprises the following steps: obtaining a target medical model, the target medical model being reconstructed based on an initial image scanned by a scanning device; generating operation planning information based on the target medical model; obtaining a real-time image scanned by the scanning device, and registering the real-time image with the target medical model to obtain a first registration relationship; obtaining a first coordinate relationship between a coordinate system corresponding to an execution tool and an image coordinate system of the real-time image; and navigating the execution tool based on the first coordinate relationship, the first registration relationship and the operation planning information. The method can model during operation, does not require preoperative CT, and realizes real-time registration without the need of adding additional markers.

An example operation includes merging diffusion tensor image (DTI) data associated with a scan of a patient body with a three-dimensional medical model to create a merged three-dimensional image in a three-dimensional coordinate space, identifying fibers of selected portions of the merged three-dimensional image associated with a region of interest defined by a virtual area selection performed by a physical instrument, highlighting the fibers as a fiber bundle, and displaying the highlighted fiber bundle as a colorized subset of the merged three-dimensional image.

The embodiment of the present application relates to a kind of medical model federation learning framework data processing system, the system includes: center network and multiple one, two class agency network;Center network includes center server and pedestal model, center server is connected with pedestal model and each first, second agency server respectively;One class agency network includes first agency server, first sample set and first local model, first agency server is connected with first sample set, first local model respectively;Two class agency network includes second agency server, second sample set and second local model, second agency server is connected with second sample set, second local model respectively, still with another one or more first, second agency server interconnection.The system of the present application can expand training sample range, can provide safe transmission guarantee for the model parameter of longitudinal transmission, can provide safe transmission guarantee for shared information when sharing training data in horizontal agency, provide privacy protection for non-shared information.

The utility model discloses a simulation model for vascular sheath stripping and ligation suture, and relates to the technical field of medical models. The device comprises a second blood vessel, the outer wall of the second blood vessel is communicated with a first blood vessel, the outer walls of the first blood vessel and the second blood vessel are fixedly wrapped with blood vessel tunica vaginalis, the inner walls, close to each other, of the second blood vessel and the first blood vessel are fixedly connected with the same lymph node, and the second blood vessel and the first blood vessel are in a Y shape and are provided with the blood vessel tunica vaginalis on the surfaces. And the lymph node is connected between the second blood vessel and the first blood vessel, so that the tissue structure simulates a real lung structure. The second blood vessel and the first blood vessel are Y-shaped, the blood vessel tunica vaginalis is arranged on the surfaces of the second blood vessel and the first blood vessel, and the lymph node is fixedly connected between the first blood vessel and the second blood vessel, so that a real lung structure can be well simulated, and the blood vessel tunica vaginalis, the first blood vessel and the second blood vessel are made of biological simulation materials with conductive properties and have real touch feeling; therefore, real skill operation can be carried out.

This invention relates to the fields of medical image processing, deep learning, and large-scale model deployment, and discloses an adaptive method and system for testing large-scale medical models based on adaptive models. It acquires test data streams from multiple clinical centers, received sequentially frame-by-frame, and exhibiting distributional differences. Based on a mean-based teacher architecture, images are enhanced with varying strengths. Predictive uncertainty is calculated using the teacher model output, and structural consistency is calculated based on the differences between the teacher and student model outputs. An entropy-consistency confusion matrix is ​​constructed based on two indicators. A dynamic threshold is calculated based on current and historical data quantiles to classify the reliability state of the images. Directional coefficients are configured according to the state as entropy loss and consistency loss, and a total loss function is constructed and updated online for the large-scale medical model. This invention enables reliable diagnosis of medical images, effectively avoiding error accumulation and model collapse caused by blind optimization, and significantly improving the classification accuracy of large-scale medical models in multi-center distribution offset scenarios.

The application discloses a lesion recognition model training method and device, equipment and a storage medium. The method comprises the following steps: obtaining model training information for guiding model training. According to the model training information, training sample data for model training is obtained. According to a preset lesion recognition model and the training sample data, a training task corresponding to the sample label is executed. When the number of training rounds of the preset lesion recognition model meets a preset round threshold, it is determined that the training of the preset lesion recognition model is completed, and a target lesion recognition model corresponding to the model training information is obtained. The technical scheme can automatically execute the corresponding training task according to the training sample label, enhance the personalized model training of the complex analysis scene, and promote the customized training and development and application of the medical model through the code-free configuration mode.

This application discloses a non-invasive method and system for predicting the ALK rearrangement status of lung adenocarcinoma based on a large-scale medical model. The method includes: acquiring chest medical imaging data and corresponding clinical information, wherein the chest medical imaging data includes lesion regions; inputting the chest medical images into a fine-tuned large-scale automatic segmentation model for lung adenocarcinoma lesions to obtain lesion segmentation mask images, wherein the segmentation model is optimized through a fine-tuning strategy using lung adenocarcinoma-specific cue tokens and a low-rank adapter module; based on the lesion segmentation mask images, cropping standardized lesion region images from the chest medical images; inputting the lesion region images and clinical information into a trained large-scale ALK rearrangement status prediction model for lung adenocarcinoma, and outputting the ALK rearrangement status prediction result for the lesion. The technical solution of this application effectively improves the segmentation accuracy of lung adenocarcinoma lesions and the accuracy and efficiency of ALK rearrangement status prediction.

The application relates to the technical field of medical models, in particular to a manufacturing method and device of an individualized whole-body physical phantom, wherein the method comprises the following steps: obtaining body size parameters and physiological parameters of a target individual; then, based on the body size parameters and the physiological parameters, a digital human body surface element model of the target individual is established; based on a target tissue in the digital human body surface element model, an equivalent index of the target tissue is calculated; then, an equivalent material formula of the target tissue is determined according to the equivalent index; and finally, an additive manufacturing task of the target individual is performed according to the digital human body surface element model and the equivalent material formula, so as to manufacture a whole-body physical phantom of the target individual. Therefore, the problem that in the prior art, the whole-body phantom is usually based on fixed body size and standard posture, only covers limited body type and posture combinations, and is difficult to depict height, weight, age, gender, body fat distribution and external irradiation environment differences, thereby limiting the effectiveness of the phantom in individualized dose optimization and multi-scenario adaptation is solved.

The invention provides an automatic registration method, a surgical navigation system and a surgical robot system. The method comprises the following steps: acquiring numbers of m registration mark points in a medical image model; obtaining n registration mark points under a world coordinate system; respectively selecting k registration mark points from the model coordinate system and the world coordinate system for pairing, and calculating a transformation matrix and a registration mark point error under each pairing relationship; and according to the registration mark point error under each pairing relationship, determining a correct point pair pairing relationship. The registration mark points as many as possible in the model coordinate system and the world coordinate system are automatically paired, the correct point pair pairing relation is automatically determined by calculating and comparing the errors of the registration mark points, the mark points do not need to be searched and collected in one-to-one correspondence with the registration mark points in the medical model, and the workload of doctors is relieved.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to a Trajectory Alignment Engine that generates within a unified three-dimensional (3D) coordinate space: (i) a 3D virtual medical model positioned according to a model pose, (ii) a virtual trajectory extending from a target point associated with a selected portion of the 3D virtual medical model and (iii) a dynamic navigation guide virtual object. The Trajectory Alignment Engine detects one or more changes in an instrument pose and / or an instrument position of a physical instrument in the 3D coordinate space. The Trajectory Alignment modifies the display of the dynamic navigation guide virtual object in the AR display at least in part on one or more of: the instrument pose changes and the instrument position changes.

The present application relates to the technical fields of medical model, especially to a newborn emergency simulation training device and simulation system, comprising a head assembly; a body assembly arranged at the end of the head assembly, comprising a body shell, a compression simulation piece and a ventilation monitoring piece; the compression simulation piece comprises a rib plate, a compression chest plate and a transfer air cylinder, the transfer air cylinder is provided with an air bag; the head assembly comprises an oral airway silica gel piece; the air bag is arranged between the rib plate and the compression chest plate, the present application can monitor the force and frequency of compression in real time through the compression simulation piece, and the monitoring data is quantitatively displayed, and the transfer air cylinder can close the air inlet to warn the students when the compression and air supply frequency is wrong, and can also simulate the changes of pulse and skin color for real-time feedback, thereby improving the training effect, the authenticity and the immersion of training.

The invention provides a medical inference method based on large model collaboration and evolutionary optimization, and aims to improve the inference capability and interpretability of complex medical problems. According to the method, a general large language model and a medical special model are combined, and conversational reasoning is realized through dynamic interaction. The method comprises the specific steps that a general model carries out preliminary analysis on medical problems, needed knowledge is recognized, a request is sent to a medical model, and the medical model provides corresponding medical knowledge; after the universal model obtains knowledge, the reasoning direction is dynamically adjusted, and reasoning continues until a final answer is obtained. In order to ensure the accuracy of knowledge requests, a simplified evolutionary algorithm is introduced to carry out cross optimization of reasoning steps, and effectiveness evaluation is carried out. Through cooperative work of the large language model and optimization of the evolutionary algorithm, the accuracy and interpretability of medical reasoning are remarkably improved, the method is suitable for an intelligent medical system, and the solving efficiency of complex medical problems is improved.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Interaction Engine. According to various embodiments, Interaction Engine generates within a unified three-dimensional (3D) coordinate space, a virtual 3D medical model positioned according to a model pose. The Interaction Engine receives video data from a plurality of video sources. The Interaction Engine renders a first Augmented Reality (AR) display that includes concurrent display of the virtual 3D medical model and visualization of at least a portion of video data from a first video source. The Interaction Engine renders a second Augmented Reality (AR) display that includes concurrent display of the virtual 3D medical model and visualization of at least a portion of video data from a second video source.

The application provides a medical model evaluation method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a general evaluation set; converting tasks in the general evaluation set through a data adaptation layer based on an interface protocol of a target model, and inputting the tasks into the target model to obtain first output data; performing pre-security verification on the first output data; evaluating the target model that passes the pre-security verification based on the first output data to obtain capability distribution data of the target model; determining a special evaluation set from a preset task library based on the capability distribution data; converting tasks in the special evaluation set through the data adaptation layer, and inputting the tasks into the target model to obtain second output data; and evaluating the target model based on the first output data and the second output data. The application can improve the accuracy and comprehensiveness of medical model evaluation.