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Conventional white-light endoscopy has high interobserver variability for the diagnosis of gastric precancerous conditions. Here we present a deep-learning (DL) approach for the diagnosis of atrophic gastritis developed and trained using real-world endoscopic images from the proximal stomach. The model achieved an accuracy of 93% (area under the curve (AUC): 0.98; F-score 0.93) in an independent data set, outperforming expert endoscopists. DL may overcome conventional appraisal of white-light endoscopy and support human decision making. The algorithm is available free of charge via a web-based interface (https://www.ccb.uni-saarland.de/atrophy).
In more detail
Introduction
Chronic inflammation of the gastric mucosa induces a cascade of precancerous conditions (chronic atrophic gastritis, intestinal metaplasia) and lesions (dysplasia) that may result in the development of intestinal-type gastric cancer.1 Infection with Helicobacter pylori and autoimmune gastritis are the most relevant factors initiating these mechanisms. Conventional white-light endoscopy has moderate sensitivity and specificity, as well as a high interobserver variability, and is therefore not sufficient to reliably diagnose gastric atrophy or intestinal metaplasia.2 3 Thus, especially in Western countries, histology-based diagnosis of precancerous conditions using standardised biopsy protocols is favoured. Advanced endoscopic techniques (eg, virtual or conventional chromoendoscopy, magnification endoscopy, confocal laser endomicroscopy) are often hindered by technical availability and costs.
DL has demonstrated potential in medical imaging, including GI endoscopy.4 In this field, DL has been used to diagnose focal pathologies (in particular colorectal polyps and oesophageal adenocarcinoma), and only occasionally for diseases diffusely affecting the GI mucosa (eg, H. pylori-associated gastritis).4–7 Here, for the first time, we present a DL approach that overcomes the limitations of white-light endoscopy in diagnosing atrophic gastritis.
Patients and methods
For a first data set, we identified 200 real-world images from patients with and without histology-proven atrophic gastritis (100 each) from subjects undergoing routine oesophagogastroduodenoscopy between 2008 and 2018 (data set DS1). Endoscopies …
Footnotes
Contributors PG: programming of the deep-learning algorithm, image analysis, statistics, manuscript preparation. AK: revision and editing of the manuscript, supervision of the artificial intelligence part, idea for the study. TF: Programming of the web-based software tool. FL: revision and editing of the manuscript; supervision of the clinical part, idea for the study. MC: manuscript preparation, patient identification and coordination of image evaluation by endoscopists.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval The study was approved by the ethics committee of Ärztekammer des Saarlandes (Saarbrücken, Germany; #36/19).
Provenance and peer review Not commissioned; externally peer reviewed.