PTH-129 Machine Learning Creates A Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett's Oesophagus in Non-Expert Endoscopists. (17th August 2016)

• Record Type: Journal Article
• Authors: Sehgal, V; Rosenfeld, A; Graham, D; Lipman, G; Bisschops, R; Ragunath, K; Banks, M; Haidry, R; Lovat, L
• Is Part Of: Gut. Volume 65(2016)Supplement 1
• Page Start: A283
• Page End: A283
• Publication Date: 2016-08-17
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2016-312388.532
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Introduction: Barrett's Oesophagus (BE) is the pre-cursor to oesophageal adenocarcinoma. Endoscopic surveillance is performed to detect dysplasia in BE as it is likely to be treatable. Machine Learning (ML) is a technology that generates simple rules, known as a Decision Tree (DT). Using a DT generated from Expert Endoscopists (EE), we hypothesised that this could be used to improve dysplasia detection in Non-Expert Endoscopists (NEE). Methods: Endoscopic videos of Non-Dysplastic (ND-BE) and Dysplastic (D-BE) BE were recorded. Areas of interest were biopsied. Videos were shown to 3 EE (blinded) who interpreted mucosal & vascular patterns, presence of nodularity/ulceration & suspected diagnosis. Acetic Acid (ACA) was sometimes used. EE answers were inputted into the WEKA package to identify the most important attributes and generate a DT to predict dysplasia. NEE (GI registrars and medical students) scored these videos online before & after online training using the DT (Fig 1 ). Outcomes were calculated before & after training. Student's t-test was used (p < 0.05). Results: Videos from 40 patients (11 pre/post ACA) were collected (23 ND-BE, 17 D-BE). EE mean accuracy of dysplasia prediction was 96% using the DT. Mean sensitivity/specificty were 93%/99%. Neither vascular pattern nor ACA improved dysplasia detection. Students had a high sensitivity but poor specificity as they 'overcalled' normal areas. GI registrars did the opposite. Training significantly improved sensitivity of dysplasia detection amongst registrars without loss of specificity. (Table 1 ). Specificity rose in students without loss of sensitivity and significant improvement in overall detection. Conclusion: ML can generate a simple algorithm from EE to accurately predict dysplasia. Once taught to NEE, it yields a significantly higher rate of dysplasia detection. This opens the door to standardised training and assessment of competence in those performing endoscopy in BE. Disclosure of Interest: None Declared