BACKGROUND AND AIMS: The endoscopic evaluation of narrow-band imaging (NBI)-zoom imagery in Barrett's esophagus (BE) is associated with suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CAD) systems may assist endoscopists in the characterization of Barrett's mucosa. Our aim was to demonstrate feasibility of a deep-learning CAD system for tissue characterization of NBI-zoom imagery in BE.
METHODS: The CAD system was first trained using 494,364 endoscopic images of general endoscopic imagery. Next, 690 neoplastic BE and 557 nondysplastic (ND)BE white-light endoscopy overview images were used for refinement training. Subsequently, a third dataset of 112 neoplastic and 71 NDBE NBI-zoom images with histological correlation was used for training and internal validation. Finally, the CAD system was further trained and validated with a fourth, histologically confirmed, dataset of 59 neoplastic and 98 NDBE NBI-zoom videos. Performance was evaluated using fourfold cross-validation. Primary outcome was the diagnostic performance of the CAD system for classification of neoplasia in NBI-zoom videos.
RESULTS: The CAD system demonstrated an accuracy, sensitivity and specificity for detection of BE neoplasia using NBI-zoom images of 84%, 88%, and 78%, respectively. In total 30,021 individual video frames were analyzed by the CAD system. Accuracy, sensitivity and specificity of the video-based CAD system were 83% (95% CI, 78%-89%), 85% (95% CI, 76%-94%) and 83% (95% CI, 76%-90%), respectively. Mean assessment speed was 38 frames per second.
CONCLUSION: We have demonstrated promising diagnostic accuracy of predicting the presence/absence of Barrett's neoplasia on histologically confirmed unaltered NBI-zoom videos with fast corresponding assessment time.