Artificial intelligence model on images of functional dyspepsia

BACKGROUND Recently, it has been suggested that the duodenum may be the pathological locus of functional dyspepsia (FD). Additionally, an image-based artificial intelligence (AI) model was shown to discriminate colonoscopy images of irritable bowel syndrome from healthy subjects with an area under the curve (AUC) 0.95. AIM To evaluate an AI model to distinguish duodenal images of FD patients from healthy subjects. METHODS Duodenal images were collected from hospital records and labeled as "functional dyspepsia" or non-FD in electronic medical records. Helicobacter pylori (HP) infection status was obtained from the Japan Endoscopy Database. Google Cloud AutoML Vision was used to classify four groups: FD/HP current infection (n = 32), FD/HP uninfected (n = 35), non-FD/HP current infection (n = 39), and non-FD/HP uninfected (n = 33). Patients with organic diseases (e.g. , cancer, ulcer, postoperative abdomen, reflux) and narrow-band or dye-spread images were excluded. Sensitivity, specificity, and AUC were calculated. RESULTS In total, 484 images were randomly selected for FD/HP current infection, FD/HP uninfec