Potential of Automatic Diagnosis System with Linked Color Imaging for Diagnosis of Helicobacter Pylori Infection.

OBJECTIVES: It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as a computer-aided diagnosis. In this study, we propose a multistage diagnosis algorithm for HP infection.

METHODS: The aims of this study are to (1) to construct an interpretable automatic diagnostic system using a support vector machine (SVM) for HP infection, and (2) to compare the diagnosis capability of our AI system with that of endoscopists. The presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. The trained classifiers automatically diagnosed HP positive and negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post-eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis.

RESULTS: The accuracy, sensitivity, specificity, PPV, and NPV of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. The accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system.

CONCLUSIONS: The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post-eradication patients. By learning more images and considering a diagnosis algorithm for post-eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians. This article is protected by copyright. All rights reserved.