Pathological diagnosis report generation system and method applicable to liver cancer and gastrointestinal carcinoma

Abstract

The invention discloses a pathological diagnosis report generation system and method applicable to a liver cancer and a gastrointestinal carcinoma. The system comprises: a pathological report analysis platform, wherein the pathological report analysis platform inputs basic information about a patient, reads a pathological image associated with a pathological number from a pathological image server according to the pathological number, and then stores the pathological number and the associated pathological image in a URL (uniform resource locator) form; the pathological report analysis platform downloads the image according to the URL; meanwhile, the pathological report analysis platform also acquires macro examination information about a pathological diagnosis report from the pathological image server; the pathological report analysis platform also receives a pathological diagnosis analysis result input by a diagnostician, and stores the analysis result into a database server; the pathological report analysis platform also reads corresponding pathological Word template information from a file template server according to a pathological pattern, reads the pathological diagnosis analysis result from the database server, and matches the pathological diagnosis analysis result input by the diagnostician with an index bookmark in a pathological Word template. The diagnostician can be assisted in quickly generating a diagnosis report.

Description

technical field [0001] The invention relates to a pathological diagnosis report generation system and method suitable for liver cancer and digestive tract cancer. Background technique [0002] When a pathological diagnostician makes a pathological diagnosis report, he needs to draw a diagnostic conclusion based on his own experience. The problems that exist in this way are: [0003] (1) Different diagnostic physicians have different experiences, and their diagnostic conclusions are also uneven. With the continuous updating of medical knowledge, the diagnostic terms used by old doctors and new doctors may be inconsistent, and some diagnostic terms may be discontinued, or It will be used by old doctors, or use some unstructured diagnostic information; this will make it inconvenient to normalize the data later and apply big data; [0004] (2) Different diagnosticians choose different diagnostic indicators when making pathological diagnosis conclusions. Some diagnosticians rely...

AI Technical Summary

Problems solved by technology

[0003] (1) Different diagnostic physicians have different experiences, and their diagnostic conclusions are also uneven. With the continuous updating of medical knowledge, the diagnostic terms used by old doctors and new doctors may be inconsistent, and some diagnostic terms may be discontinued, or It will be used by old doctors, or use some unstructured diagnostic information; this will make it inconvenient to normalize the data later and apply big data;

[0004] (2) Different diagnosticians choose different diagnostic indicators when making pathological diagnosis conclusions. Some diagnosticians rely on their own experience to focus on writing three or five diagnostic conclusions, resulting in incomplete diagnostic conclusions and incomplete data. , which will lead to the subsequent physician handing over the work to be unclear about the diagnostic conclusions made by the previous physician, resulting in information faults;

[0005] Some inexperienced diagnostic physicians may draw diagnostic conclusions from medical records, which may be too partial or incomplete due to the incomplete selection of indicators, which will affect the final diagnosis result of the patient;

[0006] (3) The generation speed of existing diagnostic reports is too slow

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