Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study
BMC Gastroenterology, cilt.26, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1186/s12876-026-05026-7
- Dergi Adı: BMC Gastroenterology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: <italic>Helicobacter pylori</italic>, Artificial intelligence, Deep learning, White-light endoscopy, Optical diagnosis, Gastritis, Endoscopy
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Background: Real-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. Methods: In 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). Results: A 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. Conclusion: In 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.