Background And Aims: Validated scoring methods such as the Mayo Clinic Endoscopic Subscore (MCES) evaluate ulcerative colitis (UC) severity at the worst colon segment, without considering disease extent. We present the Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE) algorithm, which provides a comprehensive and automated evaluation of endoscopic severity and disease extent in UC.

Methods: Ulcerative Colitis Severity Classification and Localized Extent consists of 3 main elements: (1) a quality filter selecting readable images (frames) from colonoscopy videos, (2) a scoring system assigning an MCES to each readable frame, and (3) a camera localization algorithm assigning each frame to a location within the colon. Ulcerative Colitis Severity Classification and Localized Extent was trained and tested using 4326 sigmoidoscopy videos from phase III Etrolizumab clinical trials.

Results: The high agreement between UC-SCALE and central reading at the level of the colon section (πœ…β€…=β€…0.80), and the agreement between central and local reading (πœ…β€…=β€…0.84), suggested a similar inter-rater agreement between UC-SCALE and experienced readers. Furthermore, UC-SCALE correlated with disease activity markers such calprotectin, C-reactive protein and patient-reported outcomes, Physician Global Assessment and Geboes Histologic scores (rs 0.40-0.55, psβ€…<β€…0.0001). Finally, the value of using UC-SCALE was demonstrated by assessing individual endoscopic severity between baseline and induction.

Conclusions: Our fully automated scoring system enables accurate, objective, and localized assessment of endoscopic severity in UC patients. In addition, we provide a topological representation of the score as a marker of disease severity that correlates highly with clinical metrics. Ulcerative Colitis Severity Classification and Localized Extent reproduces central reading and holds promise to enhance disease severity evaluation in both clinical trials and everyday practice.

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http://dx.doi.org/10.1093/ecco-jcc/jjae187DOI Listing

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