Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

Abstract

Background and Aim It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer‐aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection. Methods The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. 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 Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. 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.

Can image‐enhanced endoscopy improve the diagnosis of Kyoto classification of gastritis in the clinical setting?

“…A CAD system based on a DL algorithm had a significantly higher accuracy for BLI‐bright and LCI than for WLI . The diagnostic yield of another CAD based on a conventional support vector machine using LCI were as high as that of the expert endoscopists . CAD has also been studied with Kyoto classification of gastritis for endoscopic diagnosis of H. pylori infection.…”

“…64 The diagnostic yield of another CAD based on a conventional support vector machine using LCI were as high as that of the expert endoscopists. 65 CAD has also been studied with Kyoto classification of gastritis for endoscopic diagnosis of H. pylori infection. The prediction model by a machine learning procedure using the combination of endoscopic features in the Kyoto classification of gastritis demonstrated high diagnostic accuracy of H. pylori infection status the same as that by the experienced endoscopists' assessments.…”

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy

“…Among these, 19 studies were excluded from the final analysis due to the following reasons: narrative review (n=4), incomplete data (n=14), and systematic review or meta-analysis (n=1; the topic of this systematic review was the role of nonmagnified endoscopy for the assessment of H pylori infection) [ 8 ]. The remaining 8 studies [ 9 , 10 , 21 - 26 ] were included in the final analysis. Figure 1 illustrates a flow diagram showing the process used to identify the relevant articles.…”

“…These endoscopic features do not have objective indicators, and there is the potential for interobserver or intraobserver variability in the optical diagnosis of H pylori –infected mucosa [ 10 ]. Although expert endoscopists might reliably identify an H pylori infection with meticulous visual inspection of the mucosa during endoscopic examination, novice endoscopists require substantial time to perform this task efficiently.…”

Linked Color Imaging Demonstrates Characteristic Findings in Semi-Pedunculated Gastric Adenocarcinoma in Helicobacter pylori-Negative Normal Mucosa

“…In LCI imaging of the current lesion, the surrounding mucosa appeared as light orange (white-apricot) in all of the mucosa with fundic glands (Fig. 1B, C) [5][6][7] and no intestinal metaplasia in the antrum was observed although a patchy purple distribution is useful with LCI to diagnose intestinal metaplasia, which spreads from the antrum to the body in H. pylori active or previously infected areas. [1][2][3] These data strongly suggested an area negative for H. pylori infection.…”