P165 Computer aided diagnosis for dysplasia characterisation in Barrett's esophagus with magnification endoscopy on I-scan imaging. (19th June 2022)

• Record Type: Journal Article
• Authors: Hussein, Mohamed; Lines, David; Puyal, Juana González-Bueno; Bowman, Nicola; Sehgal, Vinay; Toth, Daniel; Everson, Martin; Ahmad, Omer; Kader, Rawen; Esteban, Jose Miguel; Bischopps, Raf; Banks, Matthew; Haefner, Michael; Mountney, Peter; Stoyanov, Danail; Lovat, Laurence; Haidry, Rehan
• Is Part Of: Gut. Volume 71(2022)Supplement 1
• Page Start: A120
• Page End: A121
• Publication Date: 2022-06-19
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2022-BSG.219
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Introduction: We aimed to develop a computer aided detection system that can support the diagnosis of BE dysplasia on magnification endoscopy. Methods: Videos were collected in high-definition magnification white light and virtual chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic/non-dysplastic BE (NDBE) from 4 centres. We trained a neural network with a Resnet101 architecture to classify frames. The network was tested – on high quality still images, all available video frames and on a selected sequence within each video. Results: 57 different patients each with videos of magnification areas of BE (34 dysplasia, 23 NDBE) were included. Performance was evaluated using a leave-one-out cross-validation methodology. 60, 174 (39, 347 dysplasia, 29, 827 NDBE) magnification video frames were used to train the network. The testing set included 49, 726 iscan-3/optical enhancement magnification frames. On 350 high quality images the network achieved a sensitivity of 94%, specificity of 86% and Area under the ROC (AUROC) of 96%. On all 49, 726 frames the network achieved a sensitivity of 92%, specificity of 82% and AUROC of 95%. On a selected sequence of frames per case (Total of 11, 471 frames) we used an exponentially weighted moving average of consecutive frames to diagnose dysplasia. The network achieved a sensitivity of 90%, specificity of 82% and AUROC of 94% ( figure 1 ). The mean assessment speed per frame was 0.0135 seconds (SD, ± 0.006). Conclusion: Our network can characterise BE dysplasia with high accuracy and speed on high-quality magnification images and sequence of video frames moving it towards real time automated diagnosis.