Ucerative colitis virtual biopsy method and system based on multi-modal image

Through the virtual biopsy method based on multimodal images, deep learning technology is used to construct a virtual biopsy model of ulcerative colitis, which solves the safety and operability problems of traditional endoscopic biopsy, and achieves efficient, accurate and personalized pathological evaluation of ulcerative colitis.

CN120015247APending Publication Date: 2025-05-16THE AFFILIATED HOSPITAL OF QINGDAO UNIV
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Patent Information

Application Number
CN202510106672.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-23
Publication Date
2025-05-16

AI Technical Summary

Technical Problem

The prior art has safety and operability problems in the histological evaluation of ulcerative colitis, and endoscopic biopsy depends on operator experience, and the subjectivity and repeatability of the results are poor.

Method used

Using a virtual biopsy method based on multimodal images, we use deep convolutional neural network and Transformer network structure to construct a virtual biopsy model to achieve automation and accuracy of pathological grading by collecting and processing clinical data of patients with ulcerative colitis, including colonoscopic images and computed tomography images.

Benefits of technology

This method can accurately judge the pathological mobility status of patients with ulcerative colitis, reduce the risk of endoscopic biopsy, improve the reliability and efficiency of evaluation, and help clinicians formulate individualized treatment plans.

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Abstract

The invention belongs to the technical field of medical auxiliary diagnosis, and discloses an ulcerative colitis virtual biopsy method and system based on a multi-modal image, and the method comprises the steps: building an ulcerative colitis medical image database; constructing a prediction data set according to the extracted quantitative features and the pathological grades of the ulcerative colitis patients; based on the prediction data set, constructing and training an ulcerative colitis virtual biopsy model; and acquiring new clinical data of a patient with ulcerative colitis in real time, inputting the acquired new clinical data into the trained ulcerative colitis virtual biopsy model, and outputting a pathological grading result of ulcerative colitis through extraction and prediction of pathological features. According to the method, automatic analysis of image and endoscopic image features of the lesion area can be realized, the pathological activity state of the ulcerative colitis patient can be accurately judged, and association with long-term prognosis of the patient is established, so that help is provided for clinicians to formulate an individualized UC treatment scheme.
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