Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video)
BACKGROUND AIMS We assessed the preliminary diagnostic accuracy of a recently developed computer aided detection (CAD) system for detection of Barrett's neoplasia during live endoscopic procedures. METHODS The CAD system was tested during endoscopic procedures of 10 patients with nondysplastic Barrett's esophagus (NDBE) and 10 patients with confirmed Barrett's neoplasia. White light endoscopy images were obtained at every 2 centimeter level of the Barrett's segment, and immediately analyzed by the CAD system, providing instant feedback to the endoscopist. At every level, 3 images were evaluated by the CAD system. Outcome measures were as follows: (1) diagnostic performance of the CAD system per level and per patient, defined as accuracy, sensitivity, and specificity. Ground truth was established by expert assessment and corresponding histopathology; and (2) concordance of 3 sequential CAD predictions per level. RESULTS Accuracy, sensitivity, and specificity of the CAD system in a per level analyses were 90% 91% and 89% respectively. In total, 9 out of 10 neoplastic patients were correctly diagnosed. The single lesion not detected by CAD, showed NDBE in the endoscopic resection specimen. In only 1 NDBE patient, the CAD system produced false positive predictions. In 75% of all levels, the CAD system produced 3 concordant predictions. CONCLUSION This is one of the first studies to evaluate a CAD system for Barrett's neoplasia during live endoscopic procedures. The system detected neoplasia with high accuracy, with only a small number of false positive predictions, and with a high concordance rate between separate predictions. The CAD system is thereby ready for testing in larger, multicenter trials.
Gastrointestinal endoscopy