Details

Autor(en) / Beteiligte

• Gutierrez Becker, B
• Fraessle, S
• Yao, H
• Lüscher, J
• Girycki, R
• Machura, B
• Gośliński, J
• Czornik, J
• Pitura, M
• Arús-Pous, J
• Fisher, E
• Bojic, D
• Richmond, D
• Bigorgne, A
• Prunotto, M

Titel

P098 The Endoscopic Severity Score Map (ESSM): An Artificial Intelligence scoring system providing accurate, objective and localised measurements of endoscopic disease severity in ulcerative colitis

Ist Teil von

• Journal of Crohn's and colitis, 2024-01, Vol.18 (Supplement_1), p.i377-i378

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• Abstract Background Commonly used scoring schemes as the Mayo Endoscopic Subscore (MES) account for disease severity only at specific (i.e., the worst) segments and do not capture disease extent. However, for an accurate assessment of disease severity in patients with ulcerative colitis (UC), the measure of the precise extent of disease activity is necessary. Alternative systems that include disease extent have been proposed (Balint 2018), but their implementation is prohibited by the time and cost constraints of comprehensively scoring each location along the entire colon. Here, we present the Endoscopic Severity Score Map (ESSM), a scoring system based on Artificial Intelligence, capable of providing an assessment of disease severity and extent in UC in a fully automated manner. Methods The ESSM consists of 3 main elements (Fig. 1): 1) a quality algorithm which selects readable frames from a colonoscopy video, 2) a scoring system which assigns an MES to each readable frame (Gutierrez Becker 2020) and 3) a camera localisation algorithm that assigns each frame to an anatomical location within the colon (Yao 2022). The ESSM was trained and tested using 4,306 sigmoidoscopy videos from phase III Etrolizumab clinical trials (Hickory NCT02100696, Laurel NCT02165215, Hibiscus I NCT02163759, Hibiscus II NCT02171429 and Gardenia NCT02136069). Results We evaluate the performance of the ESSM by first assessing the agreement of scoring as compared to centrally read MES. The agreement between central reading and the ESSM at the colon section level was high (quadratic-weighted kappa k=0.81; Tab. 1). This was comparable to the agreement between central and local reading (k=0.84), suggesting that the ESSM shows levels of inter-rater variability comparable to experienced readers. Finally, we found correlations between the average ESSM at all anatomical locations and other disease activity markers to be moderate to high: faecal calprotectin rs=0.24, CRP rs=0.29, stool frequency rs=0.49, rectal bleeding rs=0.43 and physician global assessment rs=0.47 (Tab. 1). Conclusion Here, we introduced the ESSM, a fully-automated AI-based scoring system that enables accurate, objective and localised assessment of disease severity in UC. In brief, we show that the ESSM compares well with central reading (at the colon section level) and has clinical relevance when compared to other markers of disease activity. This tentatively suggests that the ESSM has the potential to augment the current way of assessing disease severity, both in clinical trials and everyday clinical practice.