6 results about "Pathology Report" patented technology

The invention relates to a visual template generation method for a medical pathology report, and the method comprises the steps: obtaining a specimen image of a target case and an associated material block information list, carrying out the semantic segmentation model reasoning based on the content of the specimen image, recognizing and extracting a key anatomical structure region with morphological stability and clinical significance, and carrying out the recognition of the key anatomical structure region; according to the method, a group of sparse and reasonably distributed semantic anchor point coordinate sets are generated, the semantic anchor point coordinate sets are correlated with the original size of an image and then are stored in a structured manner, and the spatial topological relation between graphic elements and anchor points is used as a dynamic calibration basis, so that accurate positioning and consistent reproduction of annotations are realized. According to the scheme, the reducibility, the accuracy and the interaction consistency of pathological image annotation in a cross-equipment scene can be improved, and the efficiency and the reliability of pathological auxiliary diagnosis and remote cooperation are improved.

The invention provides a pathology agent system with reasoning ability. The pathology agent system comprises a pathology host agent, a pathology physician agent, a pathology section viewer, a pathology multi-mode large language model and a pathology report agent. The pathology host agent is responsible for coordinating a diagnostic pathology analysis process and strategically planning a pathological section area for detailed examination; the pathology main agent plans and coordinates the plurality of pathologist agents to investigate different pathological section tissue areas at the same time according to the formulated preliminary pathological diagnosis hypothesis and strategy; and each pathologist intelligent agent interacts with the pathological section viewer and the pathological multi-mode large language model, analyzes a specific pathological section area and feeds back a result to the pathologist intelligent agent. According to the invention, different pathological section areas can be studied step by step in a layered manner, and important background information such as tissue morphology and peripheral features can be captured based on analysis of a full pathological section level.

This invention provides a management system for WSI pathology reports based on OFD-H. It includes a presentation layer, a business layer, a data storage layer, and a standard interface for interoperability with third-party medical systems. The presentation layer includes an editor for editing OFD reports and a WSI image reader capable of coordinate backtracking of WSI images. The business layer includes: module one for automatically generating pathology image heatmaps and textual diagnostic suggestions; module two for OFD format container encapsulation; module three for three-level streaming image backtracking; and module four for adding digital signatures and watermarks. The data storage layer is used for hierarchical storage of WSI files, ROI images, and OFD documents to meet the retrieval needs of massive multimodal data. This invention can achieve "intelligent assisted generation" of reports, "two-way interoperability" between text and images, "lightweight encapsulation" of data, and "compliant traceability" of documents.

The invention provides a pet pathology auxiliary diagnosis system based on visual language artificial intelligence. The computer vision AI pathological analysis module analyzes the whole pathological section map, detects a tumor area and identifies key pathological features; the intelligent pathological report generation module automatically generates a pathological diagnosis report based on a large language model in combination with an information retrieval enhancement generation (RAG) technology; the cloud digital section management system provides a remote virtual section reading function, supports pathological specialists to access and manage pathological sections anytime and anywhere, integrates AI auxiliary diagnosis and report generation capabilities, and realizes whole-process information tracking and management. According to the auxiliary diagnosis system, the manual film reading time can be shortened, the pathological resource configuration is optimized, and the overall working efficiency is improved.

The invention relates to a specimen-level pathological report automatic generation method based on artificial intelligence, and the method comprises the steps: obtaining a plurality of digital slide images belonging to the same pathological specimen, inputting the plurality of digital slide images into a pre-trained specimen-level artificial intelligence diagnosis model, receiving a diagnosis analysis result outputted by the specimen-level artificial intelligence diagnosis model, the diagnosis analysis result comprises at least one diagnosis element obtained by comprehensively analyzing the plurality of digital slide images, matching and calling a corresponding structured diagnosis template from a preset diagnosis template library according to the diagnosis analysis result, obtaining an initial diagnosis report draft based on the matched structured diagnosis template and the diagnosis analysis result, and sending the initial diagnosis report draft to a server; and presenting the initial diagnosis report draft and the diagnosis options capable of being adjusted by the user on an interactive interface, and updating the content of the initial diagnosis report draft in response to the interactive operation of the user to obtain a final pathological diagnosis report.

This invention discloses a method and system for intelligent coding review and automated data entry of medical records, relating to the field of data processing technology. The method acquires structured data from the electronic medical record system, including the patient's discharge diagnosis, treatment process, pathology report, medication records, and cost information, through multi-source acquisition. The data is then classified and preprocessed according to medical record type. A pre-trained natural language processing model analyzes the text data in the medical record homepage to understand the doctor's diagnostic descriptions and treatment process. Combined with a constructed coding rule base and knowledge graph, a recommended coding list and anomaly alerts are output through rule mapping and reasoning. A human-machine collaboration mechanism is established to automatically enter the reviewed medical record coding data into the medical record management system. This solves the problems of excessive reliance on manual operation, insufficient coding accuracy, and low efficiency in existing medical record coding review methods, forming a precise medical record coding working mode based on human-machine collaboration.