4121results about "Medical reports" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

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.

A wearable system can include an electronic device configured to measure one or more physiological parameters of a patient and a wearable device configured to operably position the electronic device. The electronic device can have at least one light emitter and at least one light detector and be configured to measure at least a pulse oximetry measurement. The wearable device can have a main body with a cavity configured to position the electronic device, a securement portion connected to the main body and configured to secure the main body to the patient, and storage component configured to store patient identification data associated with the patient. When the electronic device and the wearable device are secured to one another, the patient identification data can be transferred from the wearable device to the electronic device.

A system configured to facilitate monitoring a patient when the patient transitions between environments. The system can have an in-room display terminal configured to display indicia of a health of the patient for electronically monitoring the health of the patient within a healthcare environment. The system can receive, via the in-room display terminal, a request to initiate monitoring the patient with the in-room display terminal at the healthcare environment; access historical physiological data associated with the patient and generated by a home monitoring device before the patient enters the healthcare environment; access real-time physiological data associated with the patient and originating from a physiological monitoring device coupled to the patient within the healthcare environment; generate one or more physiological parameters from the real-time physiological data and the historical physiological data; and cause the in-room display terminal to display indicia of the one or more physiological parameters.

A computer implemented method for governing risk actions by an artificial intelligence (AI) browser, by classifying a proposed action by the AI browser based on a large language model (LLM) as safe or risky based on AI weights or based on policy rules; initiating a step up authentication flow for a risk action; presenting an action summary and required capabilities to the user for approval; and enforcing user configured spend or scope limits on the risk action.

The invention relates to a lung focus medical image segmentation method based on graphics and text information and knowledge embedding. The method comprises the following steps: acquiring a lung medical image of a patient and a corresponding clinical diagnosis report; preprocessing the lung medical image to obtain an enhanced image; inputting the lung medical image and the clinical diagnosis report into the medical visual language model to obtain a focus prompt embedding vector; and inputting the lung medical image, the enhanced image and the focus prompt embedding vector into the medical image segmentation model to obtain a lung focus region segmentation image. By adopting the method, the lung focus can be quickly positioned by the segmentation model through the focus prompt embedding vector, the interference of a non-target area is reduced, and the segmentation accuracy and the target concentration are improved.

The invention relates to the field of artificial intelligence, in particular to a medical image computer-aided analysis method based on deep learning, and aims to solve the problems that an existing medical image analysis method is low in high-resolution image processing efficiency, insufficient in tiny focus recognition precision, weak in model generalization ability and insufficient in multi-modal image fusion. According to the method, a lightweight multi-scale feature extraction network is constructed to improve the high-resolution image processing efficiency, a fine-grained lesion recognition module is introduced to improve the detection precision of a tiny lesion, and a self-adaptive regularization strategy is adopted to enhance the model generalization ability. And a multi-modal deep fusion mechanism is designed to make full use of complementary information of different modal images. According to the invention, medical image analysis which is more efficient, more accurate, higher in generalization ability and capable of effectively fusing multi-modal information can be realized, so that clinical application of deep learning in the field of medical images is promoted.

The decision process of a first machine learning (ML) model may be explained based on a second ML model implemented on an apparatus. The apparatus may obtain a prediction about an image made based on the first ML model. The apparatus may further determine visual concepts associated with the image that may have been used by the first ML model to make the prediction, and determine respective contributions of the visual concepts to the prediction made by the first ML model. The apparatus may then generate, based on the second ML model, a textual description that explains the respective contributions of the visual concepts to the prediction made by the first ML model. The second ML model may determine respective image features associated with the visual concepts, map the determined image features to corresponding text features, and generate the textual description based at least on the text features.

The invention discloses a multi-modal X-ray image diagnosis report generation method based on reinforcement learning optimization. The method comprises the following steps: acquiring a multi-source public data set and a private data set in a specific range; performing format standardization processing on the X-ray image, performing normalization processing on text information, and constructing a structured public training data pair and a private enhanced data pair; training an end-to-end multi-modal neural network by using the public training data pair in a supervised fine tuning mode; carrying out reinforcement learning on the first-stage end-to-end multi-mode neural network by adopting a strategy gradient algorithm optimization mode of GRPO and using candidate reports and weighted rewards to obtain a second-stage end-to-end multi-mode neural network; and generating a diagnosis report by using the two-stage end-to-end multi-modal neural network. The method is suitable for personalized application scenes of X-ray image diagnosis, can quickly adapt to specific clinical requirements on the basis of limited professional data, and has good practical value and popularization prospect.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

There is provided a method and a system for generating a 3D parametric mesh of an anatomical structure of a patient for storing multi-domain data therein. A plurality of anatomical segments having been obtained from segmentation of a set of images of a patient are received. A 3D mesh comprising a plurality of concentric 3D mesh layers is received, where each concentric 3D mesh layer includes a same predetermined number of nodes. A set of nodes in the 3D mesh corresponding to a respective anatomical segment is determined to obtain a respective correspondence rule therebetween. The set of nodes is encoded with a set of features from the respective anatomical segment by using the correspondence rule to obtain a 3D parametric mesh, each node of the set of nodes in the 3D parametric mesh being associated with a respective plurality of feature channels comprises the set of features.

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 provides a small sample tympanic membrane image recognition method based on meta-prompt and knowledge driving, and the method comprises the steps: inputting a small sample training set into an initial multi-mode pre-training model, obtaining a prediction category, comparing the prediction category with a real category, and screening misclassification samples in combination with confidence to construct a meta-task set; and inputting the meta-task set into the primary diagnosis model, and outputting a primary diagnosis report. And then, optimizing the medical description text sample based on the preliminary diagnosis report by utilizing a knowledge refining model, and replacing the original text sample, so as to obtain an updated sample. And finally, iteratively training the initial multi-modal pre-training model by using the updated sample until a termination condition is met. According to the method, a closed-loop optimization system composed of a primary diagnosis model and a knowledge refining model is constructed. Under the condition of small samples, the system dynamically optimizes the visual-semantic understanding ability of the model by using error samples generated by the model, and the accuracy of small sample tympanic membrane image recognition is effectively improved.

The invention relates to the technical field of physiological parameter monitoring of intelligent wearable equipment, in particular to an intelligent ring health monitoring method based on multi-sensor cooperation and related equipment. The method comprises the following steps: acquiring motion, optical and temperature sensing data, determining a scene in combination with a multi-dimensional rule and a user preset log, executing differential data acquisition, and generating a health assessment result matched with the scene after signal noise reduction, feature extraction and fusion analysis. According to the invention, monitoring accuracy, low energy consumption and individuation can be considered, and the effectiveness of health monitoring is improved.

The invention relates to the technical field of pneumoconiosis image detection, and discloses a pneumoconiosis-oriented chest X-ray image processing method based on an AI small shadow detection rate. According to the method, a basic mask is generated through an initial segmentation network, and then an accurate lung contour is obtained through correction of a hierarchical optimization unit. The multi-scale feature extraction module analyzes gray features in the contour and constructs an initial small shadow probability distribution map. A morphological structure analyzer identifies discrete shadow regions and marks suspicious shadow clusters, and a dynamic confidence feedback mechanism evaluates its credibility to generate an optimized feature map. A shadow cluster topological incidence matrix is established by the spatial relation modeling network and matched with a pneumoconiosis pathological feature library to screen a target shadow area. The three-dimensional reconstruction engine generates a small shadow volume density thermodynamic diagram, the hierarchical fusion module integrates the thermodynamic diagram and original image space coordinates, an enhanced distribution map is output, and finally structured diagnosis report data is generated.

A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and / or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention discloses an image diagnosis report generation method and device and a storage medium. The method comprises the following steps: determining the matching degree on three different levels, namely, the spatial matching degree between a diagnosis text and a candidate template on an anatomical part level, the semantic matching degree between the diagnosis text and the candidate template and related to pathological information, and the clinical association degree between the diagnosis text and the candidate template; according to the method, the target template matched with the diagnosis text is searched based on the diagnosis text input by the user, and the search is further optimized through the time decay factor, the clinical priority and the conflict degree among the regions of interest, so that the accuracy of searching the required image diagnosis template for the user is improved, and the user experience is improved. Therefore, the technical problems that in the prior art, a traditional keyword matching algorithm is poor in keyword matching effect in a medical scene, and the accuracy of searching out a correct template is reduced are solved.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention discloses a pathological feature recognition and negative elimination method based on microscopic imaging. The method comprises the following steps: S1, collecting a pathological section image and digitally generating original microscopic image data; s2, preprocessing the original microscopic image; s3, constructing a pathological image recognition network fusing converter coding and a gating dynamic receptive field mechanism, and outputting pathological feature vectors; s4, performing context modeling through an attention guidance and category perception decoder, and outputting an image classification result; s5, constructing a discriminant boundary separation model based on positive and negative sample embedding, and performing negative exclusion judgment; s6, performing confidence coefficient weighted evaluation in combination with the uncertainty and the boundary distance, setting a dynamic threshold value, and screening out low-credibility samples; and S7, coding the classification result and the negative label into structured data, and sending the structured data to a diagnosis auxiliary system. According to the method, multi-scale modeling and a negative screening mechanism are fused, and intelligent recognition and credible diagnosis output of the pathological image are realized.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention provides a digital information processing method for a hospital radiology department, which comprises the following steps: S1, multi-modal image collaborative acquisition and standardization: synchronously acquiring anatomical structure images, functional images and metabolic parameter data of a patient through radiology department imaging equipment, and converting the anatomical structure images, the functional images and the metabolic parameter data into space-time aligned three-dimensional digital matrixes; s2, image quality optimization processing: performing nonlinear contrast enhancement and noise suppression on the original image to improve the signal-to-noise ratio of a target area; s3, dynamic self-adaptive registration: according to the biomechanical characteristics of the organ, fusing the rigid transformation model and the elastic deformation model, and according to the digital information processing method for the hospital radiology department, based on the dynamic registration matrix of the biomechanical model, improving the multi-modal image fusion precision; a deep learning segmentation algorithm fused with morphological constraints improves the focus boundary recognition accuracy; a texture mapping three-dimensional reconstruction technology is mixed, and an anatomical structure and metabolism information are presented at the same time; the invention discloses a structured report automatic generation system based on an attention mechanism.

The invention discloses a medical report generation method, a model training method, equipment and a medium, and the model training method comprises the steps: constructing a medical report generation model framework which comprises a global semantic collaborative multi-modal enhancement module, a visual encoder, a text encoder, a medical insight analyzer and an LLM decoder; wherein the global semantic collaborative multi-modal enhancement module respectively enhances a medical image and a medical report by utilizing a selected image enhancement strategy and a text enhancement strategy, and the medical insight analyzer comprises a fine-grained structure learning device and a global context guide learning device which are connected in sequence so as to enhance the cross-modal alignment capability; and performing intelligent collaborative optimization by taking a strategy set formed by an image enhancement strategy and a text enhancement strategy and architecture configuration parameters of the medical insight analyzer as optimization targets to obtain an optimal medical report generation model. The medical report generation performance can be effectively improved.

The invention provides a multi-modal large language model for generating a hepatocellular carcinoma key pathological diagnosis report, a framework main body is a visual coding module, and a multi-modal feature alignment module, a multi-head low-rank attention mechanism, an enhanced medical MoE mechanism and a structured output decoding layer are also introduced. The visual coding module is constructed on the basis of a Swin Transform architecture, visual pre-training is completed on hepatocellular carcinoma MRI data, and after a task specific classification head is stripped, a trunk feature extraction network is reserved to serve as an image modal representation encoder. The multi-modal feature alignment module guides the model to learn a cross-modal semantic mapping relation between a hepatocellular carcinoma MRI image and a key pathological diagnosis report language, image modal input is a visual feature sequence, and text modal output is a structured description text; and the structured output decoding layer generates six types of liver cancer focus attributes. According to the method, the pre-operative multi-parameter and multi-stage enhanced MRI image is utilized, and the open-source large model is finely adjusted to generate a matched liver cancer postoperative pathology report.

The invention discloses an X-ray chest radiography report automatic generation method, system and device based on factual description enhancement and a medium. The method comprises the following steps: firstly, constructing a medical entity extraction method based on a RadGraph model, and carrying out identification and structured extraction on clinical keywords to obtain factual description consisting of key medical entities; secondly, establishing a comparative learning method guided by factual description, enhancing semantic consistency between the image and the text from global and local levels, and extracting visual features with diagnostic value; establishing a historical similar case retrieval strategy independent of disease tags, and calculating visual semantic similarity to realize automatic retrieval of historical cases; and finally, proposing an evidence-driven chest radiography report generation method, constructing a cross-modal fusion network, and generating a chest radiography report with clinical accuracy and consistency. The system, the equipment and the medium automatically generate an X-ray chest radiography report based on factual description enhancement based on the method; according to the method, efficient and stable automatic retrieval is realized, the clinical accuracy of the generated chest radiograph report and the reliability of evaluation are improved, and the universality and robustness of the model are remarkably improved.

The invention relates to the field of brain tumor analysis, and discloses a brain tumor image analysis system based on artificial intelligence, comprising: a spatial alignment unit for acquiring original image data of the brain of a subject; performing spatial alignment on the original image data according to a cross-modal registration algorithm to obtain standardized image data; the feature extraction unit is used for performing tumor region initial segmentation on the standardized image data according to a three-dimensional convolutional neural network so as to obtain a coarse segmentation probability graph; and extracting three-dimensional geometric feature parameters of the tumor candidate region according to the coarse segmentation probability graph. According to the method, the original image data is spatially aligned through the cross-modal registration algorithm, and the spatial consistency between different image sources is ensured, so that the image data under different modals can be accurately compared and analyzed, and an accurate spatial reference is provided for subsequent tumor region identification and processing.

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.