ArticleBMC gastroenterology2026
Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.
Article in BMC gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundReal-time endoscopic diagnosis of Helicobacter pylori infection remains challenging and often requires biopsy-based testing, delaying treatment decisions. Although deep learning (DL) approaches have shown promise, most prior studies have relied on retrospective datasets or image-enhanced modalities, limiting their applicability to routine white-light endoscopy. We aimed to develop and prospectively validate a research-stage DL model for H. pylori detection using only standard white-light endoscopic images.
methodsIn this single-center prospective study, consecutive adults undergoing diagnostic gastroscopy were enrolled. Six standardized gastric images per patient were targeted under standard white-light endoscopic imaging. Histopathology from antral and corpus biopsies served as the reference standard. An EfficientNet-B0-based DL model was developed to classify H. pylori infection at the patient level by aggregating image-level predictions. Model performance was assessed using five-fold cross-validation within the development cohort, followed by evaluation in an independent temporally separated validation cohort (70% development / 30% temporal validation).
resultsA total of 172 patients (1,000 images) were included; 94 patients (54.7%) were H. pylori-positive. In five-fold cross-validation, the model achieved a patient-level AUC of 0.901 (95% CI: 0.863-0.936), with 85.1% sensitivity and 81.4% specificity. In the independent temporal validation cohort (n = 52; prevalence 48.1%), the AUC was 0.889 (95% CI: 0.793-0.960), with 84.0% sensitivity and 85.2% specificity. Aggregating predictions across multiple gastric views improved discrimination compared with single-image inference.
conclusionIn this prospective study, a deep learning model using routine white-light endoscopic images demonstrated reasonable patient-level discrimination for H. pylori detection, including in an independent temporally separated validation cohort. At present, the model should be viewed as a research and decision-support tool rather than a standalone diagnostic system. Multicenter external validation and prospective video-based studies are warranted before routine clinical deployment.
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