130 results about "Medical report" patented technology

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 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 discloses a processing method for structural information extraction and privacy protection of a medical report, which relates to the field of medical report processing and can efficiently and accurately extract text information in a table by integrating advanced image preprocessing, dynamic layout analysis, OCR (optical character recognition) technologies and a dual-channel privacy detection model. Establishing a coordinate mapping relation between the image description paragraph and the corresponding image; the generated JSON structure containing the text, table and image-text mapping relation ensures the integrity and accuracy of the data; the problems of data dislocation, context splitting, privacy disclosure and the like when the medical document with complex typesetting is processed in the prior art are solved; besides, an improved Transform framework is constructed on the basis of a Qwen model, a logical reasoning chain is injected, meanwhile, a case reading large model is obtained by integrating clinical diagnosis rules and multi-modal contrast learning strategy training, the model is utilized to generate a high-quality structured abstract, and the abstract generation accuracy is greatly improved.

The invention discloses a cross-modal gating fusion radiology report generation method combined with clinical semantic enhancement, and relates to the technical field of medical report generation. A semantic enhancement decoding unit is used for performing deep fusion on image sequence representation, historical text embedding and clinical semantic representation to obtain final enhancement decoding representation of multi-modal semantic enhancement; and performing report generation on the final enhanced decoding representation output by the semantic enhanced decoding unit to obtain a predicted radiology report. The predicted radiology report generated by the radiology report generation method is closer to a radiology report sample text in the aspects of sentence structure and coherence.

The invention discloses a similarity report prompt cross-modal enhancement medical report automatic generation method, which comprises the following steps: an image feature extraction module extracts multi-scale visual features, and a text feature coding module extracts text features; the medical image in the training stage has a corresponding report; in the testing stage, similar cases are selected for medical images used for testing through a dynamic prompt generation module, text features of text reports corresponding to the similar cases are subjected to weighted fusion and output to obtain dynamic prompt vectors, and the dynamic prompt vectors are used for replacing the corresponding reports; the cross-modal feature fusion module dynamically fuses the image features and the text features, and outputs to obtain a fusion feature vector; the fusion feature vector and an unfinished report generated by autoregression are input into the CMN together to obtain enhanced features; and the report generation module outputs a natural language-based diagnostic report text for the enhanced feature processing. According to the method, a dynamic prompt mechanism and cross-modal attention are adopted, irrelevant noise interference is reduced, and misdiagnosis risks are reduced.

The embodiment of the invention relates to a method and a device for generating a medical report based on evidence-based medicine. The method comprises the following steps: acquiring patient information and an original clinical problem; inputting the patient information, the original clinical question and the first cue word into a target model to generate a structured clinical question; the target model is a medical field large language model; retrieving a medical literature library according to the clinical problem to obtain a plurality of medical literatures; inputting the target medical literature and the second cue word into a target model, so that the target model performs bias risk assessment on the target literature, and a structured initial assessment result of the target literature is generated; inputting the clinical problem, each medical literature, the initial evaluation result of each medical literature and the third cue word into a target model, and enabling the target model to carry out thinking chain gradual reasoning based on a GRADE evaluation process in the third cue word to generate a medical report; the medical report includes medical suggestions for clinical issues, and recommendation intensities and evidence quality levels of the medical suggestions.

A computer-implemented method for creating medical reports, in particular physician's letters, comprises the acquisition of patient information from various sources of electronic health records, with the latter being available in natural-language sampling points. Medical units are determined by computer-aided pattern recognition and classified according to their event type. Event groups that have temporal, thematic or treatment-related relationships are formed. Relationship information is derived and stored in a structured data representation. In addition, these data are enriched with an external medical knowledge base and converted into a format that can be processed by a generative language model in order to generate the continuous text for the medical report.

The invention discloses a medical report interpretation method and a related device, which can be used in the field of artificial intelligence, and the method comprises the steps: firstly, obtaining a medical report; the medical report comprises at least one of text information, image information and table information; then, text information in the medical report is recognized through an optical character recognition technology, and a text recognition result is obtained; identifying the medical report based on the text identification result through a multi-modal large model to obtain a first identification result; the multi-modal large model carries out semantic understanding based on the first recognition result and the target prompt word, and report interpretation content is generated; the report interpretation content comprises a report abstract and personalized suggestions. Therefore, the optical character recognition technology and the multi-mode large model are combined, the defect that medical report solving and interpretation are difficult to accurately conduct depending on single-mode data is overcome, and a rapid, accurate and comprehensive medical report interpretation scheme is provided.

The invention relates to the technical field of medical natural language processing, in particular to a medical report automatic analysis and structured generation method based on a multi-modal vision-language model. The method comprises the following steps: S1, acquiring a target medical image and an initial text report corresponding to the target medical image, and performing feature extraction and preprocessing to obtain a multi-level visual feature sequence and a text feature sequence; s2, obtaining at least one focus-description cluster corresponding to the independent clinical semantic unit according to the multi-level visual feature sequence and the text feature sequence; s3, for each focus-description cluster, the fuzzy medical description in the focus-description cluster is converted into structured attribute information with a deterministic state label; and S4, based on all the focus-description clusters and the corresponding structured attribute information, carrying out content assembly and generation under logic constraints, and outputting a structured medical report with a confidence identifier. According to the invention, deep understanding and high-reliability structured output of the medical report are realized.

The invention relates to the technical field of medical report generation, and discloses a medical image automatic annotation and report generation system which comprises a preprocessing module, a hierarchical feature extraction module, a semantic annotation module, a guide generation module, a diagnosis reasoning module and a man-machine cooperation interface. According to the method, through standardized preprocessing, hierarchical feature extraction and automatic generation of the annotation and the report, the processing efficiency is greatly improved, and the manual load is reduced; secondly, on the basis of hierarchical feature extraction and semantic and visual embedding alignment, feature support and semantic mapping precision of annotation and reporting are enhanced, and result accuracy and cross-scene consistency are improved; meanwhile, multi-modal alignment processing and multi-dimensional information fusion enhance the adaptability of the system to different image types, generate a structured report conforming to the clinical process and improve the clinical applicability; in addition, a man-machine cooperation feedback mechanism forms a parameter optimization closed loop, so that the system dynamically adapts to new scenes and specifications, and continuous improvement of generalization ability is realized.

The invention discloses an automatic medical report generation method and system based on a capsule endoscope, and the method comprises the steps: obtaining a capsule endoscope video, and removing redundant frames of the capsule endoscope video, and obtaining a plurality of representative frames; inputting each representative frame into a preset detection model, determining an initial key frame and a classification result, and performing fine reduction on the initial key frame by using the classification result to obtain a plurality of target key frames; inputting each target key frame into a preset multi-modal model, extracting a corresponding first visual feature, splicing the first visual feature and the text feature to obtain an initial text description, performing iterative prediction on the initial text description until a first iteration termination condition is met, and outputting a target text description; and inputting the target text description into the large language model, predicting the first word segmentation sequence step by step by using an autoregression method until a second iteration termination condition is met, and generating a corresponding structured medical report based on the second word segmentation sequence. The diagnosis report can be automatically generated.

The invention discloses a learnable radiology report generation method based on visual text alignment and fusion of retrieval enhancement. The method comprises the steps of collecting and respectively constructing a model training data set and an auxiliary data set, then constructing a visual text alignment and fusion model based on retrieval enhancement, and inputting the model training data set and the auxiliary data set into the visual text alignment and fusion model based on retrieval enhancement together for training. Constructing an inference model according to the trained visual text alignment and fusion model based on retrieval enhancement; and inputting the to-be-detected medical image and the auxiliary data set into the reasoning model for processing to obtain a radiology report corresponding to the to-be-detected medical image. According to the method, retrieval correlation is enhanced, meanwhile, a fine-grained vision-text alignment and fusion method is adopted to align and fuse features, and the problem that fine-grained region-sentence alignment is difficult due to weak supervision of an image report level in the medical report generation process is solved.

The present invention aims to provide appropriate answers to inquiries regarding medical reports. [Solution] The cloud management server 10 has a general-purpose LLM 16h, a database 15 that manages knowledge graph data 15a generated based on multiple vectorized information obtained by vectorizing text contained in multiple medical information, and an information presentation unit 16g that, when question data related to a medical report is received from a doctor's terminal 30, transmits answer data that responds to the question data in the medical report to the doctor's terminal 30 based on the database 15 and the LLM 16h.

Examples of the present disclosure describe systems and methods for using AI to identify regions of interest (ROI) in medical images. In aspects, medical reports and images may be provided to a second environment. The second environment may use the medical report data / medical images to train a natural language processing (NLP)-based algorithm to identify the location in images of ROI described in the medical report data. The output of the NLP-based algorithm may be stored in an ROI repository in the second environment. After the NLP-based algorithm has been trained, a request to train a user-specific model may be received in a first environment. Data objects for the requested user-specific model may be provided to the second environment, which uses the ROI repository to train the model. The trained model may be provided to the first environment, where the trained user-specific model / algorithm may be tested and stored.

An apparatus for the generation of a medical report is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a user query. The memory instructs the processor to receive a user profile comprising a plurality of medical tests. The memory instructs the processor to generate testing data as a function of the plurality of medical tests using an encoder. The memory instructs the processor to generate textual data that is representative of the testing data using a querying transformer model (Q-former). The memory instructs the processor to generate a medical report as a function of the user query and the textual data using a report large language model (LLM).

There is provided a system, method and device for physiological sensing for automated medical record generation. The method including: receiving a bioacoustics signal from a physiological sensing device, the physiological sensing device receiving and subtracting contemporaneous signals from a primary microphone and a secondary microphone to generate the bioacoustics signal, the primary microphone measuring sound through a membrane that is in contact with a subject, the secondary microphone measuring sound at a separate location than the membrane; using a trained machine learning classifier to determine health-related medical data from the bioacoustics signal, the machine learning classifier trained on a dataset of labeled bioacoustics signals; generating a medical report with a large language machine learning model taking the health-related medical data as input; and outputting the medical report.

The invention belongs to the technical field of image processing, and particularly relates to a medical report generation method based on dual-path multi-level semantic alignment, which comprises the following steps: constructing a dual-path multi-modal feature extraction network, fusing static-dynamic collaborative paths of MedCLIP and a context sensing module and universal visual representation of ViT, and generating a medical report based on multi-level semantic alignment. Specific feature extraction and wide visual semantic understanding in the medical field are coordinated and balanced. Through a CLIP-driven hierarchical cross-modal alignment mechanism, global coarse-grained mapping based on comparative learning and local fine-grained interaction based on bidirectional attention are cooperatively executed, and heterogeneous visual features and text semantics are accurately projected to a unified metric space. The problems that feature representation is deficient and cross-modal alignment granularity is single in the prior art are solved. And multi-level alignment loss and autoregression generation loss are integrated in an optimization target, so that the generated report is ensured to have high clinical fidelity and keep the continuity of language logic at the same time.

Disclosed herein is a medical system (100, 600) comprising a memory (110) storing machine executable instructions (120), recipient-specific metadata (122, 122 '), and a problem database (124). The recipient-specific metadata includes a pointer to at least a portion of the problem database. The medical system also includes a computing system (104). Execution of the machine executable instructions causes the computing system to: receive (200) object metadata (130) describing an object (618); receiving (202) a selection (126) of a primary recipient; selecting (204) one or more questions (128) from a question database using recipient-specific metadata (122 ') of the primary recipient and the object metadata; receiving (206) sender text (132) in response to sequentially presenting the one or more questions using the sender user interface; compiling (208) the sender text into an electronic medical report (134); and sending (210) the electronic medical report to the primary recipient via the network connection.

The invention relates to a computer-implemented method for providing a medical finding to a requesting entity, the method comprising a plurality of steps. A step is directed to obtaining at an evaluating entity, medical image data from a requesting entity, the requesting entity being remote from the evaluating entity. Another step is directed to determining, at the evaluating entity, at least one medical finding based on the obtained medical image data. Another step is directed to generating transmission image data. Another step is directed to embedding at least one of the determined medical findings as text in the transmission image data. Another step is directed to providing the at least one medical finding to the requesting entity by means of transmitting the transmission image data to the requesting entity.

The present invention relates to a system, a device, and a method for providing medical examination information and feedback by utilizing image processing and artificial intelligence technology. The method may comprise the steps of: acquiring a medical image by photographing a part of the body of a subject to be examined by a medical examination device; extracting, from the medical image, medical information including first information related to usability of the medical examination device and second information related to a standardized examination index; identifying whether the medical information satisfies a specified rule; and providing feedback information notifying that the rule fails to be satisfied when at least a part of the medical information fails to satisfy the rule. The present invention can improve usability (or performance capability) of a medical examination device (e.g., endoscope) and / or a quality of a medical examination (e.g., the accuracy and / or efficiency of diagnosis). In addition, the present invention can improve the accuracy and / or completeness of an interpretation.

The invention relates to the technical field of medicine safety monitoring, in particular to an individual case safety report automatic scoring system based on a large model. The system comprises a report receiving module, a deep semantic understanding module, a dynamic graph structure construction module, a graph neural network scoring module and an interpretability output module. Deep semantic analysis is carried out through a large language model pre-trained in the medical field, a multi-dimensional semantic graph with time sequence characteristics is constructed, a graph neural network based on an attention mechanism is utilized to carry out multi-round iterative reasoning to generate scores, and reasoning process interpretation based on a critical path is provided. According to the method, medical report semantics can be deeply understood, the dynamic relation between entities is accurately captured, the scoring accuracy and interpretability are improved, and reliable technical support is provided for drug safety monitoring.

The invention relates to a medical data analysis method and device based on artificial intelligence, equipment and a medium. The method comprises the following steps: acquiring medical data of a user; the medical data comprises blood detection data, medical image index data and basic data, and the medical data is provided with a sampling time label; extracting a change trend of the medical data to obtain a comprehensive feature vector; the comprehensive feature vector at least comprises a blood detection change vector and a medical image index change vector; calculating weighted weights of the blood detection change vector and the medical image index change vector in the comprehensive feature vector, and adjusting the comprehensive feature vector based on the weighted weights to obtain a fused feature vector; and predicting a disease type and a disease risk probability of the user based on the fusion feature vector, and generating a medical report of the user based on the disease type and the risk probability. By adopting the method, the accuracy and timeliness of cardiovascular and cerebrovascular medical data analysis can be improved.

The application discloses a medical report generation method and system based on multi-modal information fusion, which extracts features from the multi-modal data of the patient, fuses the features, and further encodes the disease state information, combines the disease state and disease name related information, generates rich disease embedding, inputs the disease embedding into a Transformer decoder with multi-head self-attention and position encoding to generate a medical report, builds a classification network based on a text encoder, summarizes the generated medical report to obtain an abstract embedding, and classifies the abstract embedding into a corresponding disease state; and guides the generator through loss optimization, and the loss of the generator participates in total loss calculation to ensure the accuracy of the report. The method can accurately describe the symptoms, avoid mistakes and omissions, give reliable diagnostic conclusions, and is highly consistent with clinical facts, and can meet the clinical diagnosis needs.

This invention proposes a medical report generation method and system based on a cross-modal interactive memory network, relating to image processing technology and natural language processing. It addresses the problems of traditional models, such as the high time and effort required to incorporate medical knowledge, the inability to effectively extract anomalous fine-grained textual knowledge, and the lack of accurate loss functions, leading to incomplete and inaccurate reports. The method includes: acquiring a training set consisting of medical images and reports of known healthy and diseased areas; inputting the training set into a cross-modal interactive memory network model for training; and acquiring the medical image for which the report is to be generated and inputting it into the trained cross-modal interactive memory network model to obtain the report generation result. This invention solves the problems of ineffective extraction of anomalous fine-grained textual knowledge, improving the accuracy of the model's report generation.

The invention provides a method for generating a PDF (Portable Document Format) file by a microcontroller of medical equipment and refrigeration equipment. The method comprises the following steps of: defining a PDF file core structure body meeting medical data record and refrigeration equipment operation log archiving requirements; defining a PDF object structure for organizing medical instrument monitoring data, patient information or refrigeration equipment temperature curves and alarm logs; defining enumeration types related to medical report typesetting and refrigeration state visualization requirements; a bottom-layer file operation interface is mapped to an FATFS file system through macro definition, and it is ensured that medical reports and refrigeration logs can be reliably stored in an external medium; defining an upper-layer interface function generated for PDF files of operation records of medical instruments and refrigeration equipment; an FATFS file system is transplanted on the microcontroller, initialization and read-write operation functions of an external flash or a USB flash disk are achieved in a diskio.c file, and support is provided for a medical instrument to locally store a PDF diagnostic report or refrigeration equipment to store a PDF-format temperature historical record.

Creating a medical report, indicative of a longitudinal examination includes receiving an input longitudinal report data structure having longitudinal data elements, which are associated with annotation elements of a medical annotation database that is indicative of relationships between medical concepts. A set of reference report data structures is received, which has reference data elements associated with annotation elements of the medical annotation database. For each longitudinal data elements, it is determined whether the longitudinal data element can be linked with a reference data element of at least one reference report data structure from the set of reference report data structures. The determining is made based on the associated annotation elements and based on the medical annotation database. An interface element indicative of a pointer from the input longitudinal report data structure to the reference report data structure having the linked reference data element, is outputted responsive to the determining.

Described herein are method, system, and apparatus for correlating medical images with a medical report. An apparatus may obtain a plurality of medical images of a patient and a medical report for the patient, wherein the plurality of medical images may be associated with respective sequence labels and respective view labels, and wherein the medical report may include multiple diagnoses. The apparatus may determine and fuse respective image-type embeddings and image-content embeddings for the plurality of medical images into a combined image feature representation. The apparatus may further encode the features of the medical report into a text feature representation and calculate a similarity score based on the image feature representation and the text feature representation to indicate a correlation between the medical images and the medical report.

The invention relates to an ultrasonic imaging medical report generation method and device and computer equipment. The method comprises the following steps: acquiring an inspection record description standard audio file of ultrasonic image inspection, and performing identification processing on the inspection record description standard audio file to obtain an inspection record text; obtaining ultrasonic examination doctor's advice information of ultrasonic image examination, and performing ultrasonic image medical report template matching processing according to the ultrasonic examination doctor's advice information to obtain a target ultrasonic image medical report template; and obtaining a target cue word, and inputting the examination record text, the target ultrasonic image medical report template and the target cue word into the large language model to obtain an ultrasonic image medical report of the ultrasonic image examination. By adopting the method, the efficiency and the accuracy can be improved.

The invention discloses a medical image and medical report interaction method and system. The method comprises the following steps: displaying a medical image on a medical image display interface; displaying the medical report on a report display interface; in response to the operation of the user on the medical image, generating operation information containing the measurement data and the layer position data; the operation information is transmitted to a report display interface through a communication module; and in response to the operation of the user on the report display interface, establishing a link between the operation information and the medical image. A user can copy measurement data to a specified position of a medical report, and an operable mark linked to a corresponding layer position of a medical image is automatically generated. And triggering the mark to control the medical image display interface to jump to the position of the corresponding layer. The invention further provides a corresponding interaction system which comprises a report processing module, a medical image processing module and a communication module. According to the invention, efficient interaction between the medical image and the medical report is realized, and the medical diagnosis efficiency and accuracy are improved.

Embodiments of the present specification relate to a method and device for generating a medical report based on evidence-based medicine, the method comprising: obtaining patient information and an original clinical question; inputting the patient information, the original clinical question and a first prompt word into a target model to generate a structured clinical question; the target model being a large language model in the medical field; retrieving a medical literature database according to the clinical question to obtain a plurality of medical literatures; inputting the target medical literature and a second prompt word into the target model to enable the target model to perform bias risk assessment on the target literature to generate a structured initial assessment result of the target literature; inputting the clinical question, each medical literature and its initial assessment result and a third prompt word into the target model to enable the target model to perform step-by-step reasoning based on a GRADE evaluation process in the third prompt word to generate a medical report; the medical report including a medical recommendation for the clinical question, a recommended strength of the medical recommendation and an evidence quality level.