Helicobacter pylori stomach video full-automatic intelligent analysis system and marking method thereof

The fully automated intelligent video analysis system for Helicobacter pylori gastric infection utilizes a multi-instance learning mechanism to train a deep learning model, enabling fully automated and real-time diagnosis of Helicobacter pylori infection. This solves the problems of high data annotation costs and low automation in existing technologies, thereby improving diagnostic efficiency.

CN114359131BActive Publication Date: 2025-12-30ZHEJIANG UNIV
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Patent Information

Application Number
CN202111338011.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2021-11-12
Publication Date
2025-12-30
Estimated Expiration
2041-11-12

AI Technical Summary

Technical Problem

Existing Helicobacter pylori identification technologies suffer from high data annotation costs, low levels of automated reasoning, and poor robustness to video data, increasing the workload of physicians.

Method used

The fully automated intelligent video analysis system for Helicobacter pylori infection in the stomach is adopted, which includes modules for image acquisition, preprocessing, effective frame screening, anatomical location localization, and deep learning. The deep learning model is trained using a multi-instance learning mechanism to achieve real-time automated diagnosis of Helicobacter pylori infection and reduce manual operation by physicians.

Benefits of technology

It enables fully automated, real-time diagnosis of Helicobacter pylori infection, reducing the workload of physicians, improving diagnostic efficiency, and reducing judgment errors caused by the intensity of their work.

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Abstract

The present application belongs to the field of medical data mining, and particularly relates to a kind of helicobacter pylori stomach video full-automatic intelligent analysis system and marking method thereof, including image acquisition module, image preprocessing module, effective frame module, anatomical position positioning module, deep learning module and marking display module, the image acquisition module is used to obtain video stream, the image preprocessing module is used to pre-process the image of video stream, the effective frame module is used to classify and screen the image of video stream, the anatomical position positioning module is used to identify the specific position of endoscope currently located in stomach, the deep learning module is used to predict the helicobacter pylori infection probability of different anatomical position pictures.The present application does not need doctor to step on pedal to input specific picture to identify helicobacter pylori infection in test process, the whole process does not need manual intervention, and full automation and high intelligence can be achieved.
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