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Machine learning-based prediction of Geboes score and histologic improvement and remission thresholds in ulcerative colitis

Study Background

Histology is emerging as a potential therapeutic endpoint for ulcerative colitis (UC) driven by associations between histologic response and long-term outcomes1. However, existing scoring systems are subjective and consequently have variable inter- and intra-reader variability2. Furthermore, manual histologic assessment is semi-quantitative and limited in the ability to capture spatial relationships.

Here we report the first machine learning (ML)-based prediction of the Geboes score, and GS-derived thresholds of histologic improvement and remission3, directly from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained mucosal biopsies. Together, PathAI models for characterization of the UC histology (IBD-Explore) and the PathAI algorithm for Geboes scoring (AI-measurement of histological improvement in UC (AIM-HI UC); for research use only) have the potential to identify clinically relevant histologic features, enable robust scoring, and ultimately advance precision medicine for patients with IBD.

1. Gonzalez-Partida, I. et al. Eur J Gastroenterol Hepatol. 2021; 33:e796-e802.
2. Romkens, TH. et al. J Crohns Colitis. 2018; 12:425-431.
3. Li, K. et al. Gastroenterol. 2020; 159:P2052-P2064.
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Carlos Gaitán1, Zahil Shanis1, Kathleen Sucipto1, John Shamshoian1, Jin Li1, George Hu1, Harshith Padigela1, Andrew Walker1, Harsha Pokkalla1, Darren Fahy1, Geetika Singh1, Chinmay Surve2, Natoria Wade2, Archit Khosla1, Mary Lin1, Michael Montalto1, Andrew Beck1, Jimish Mehta1, Ilan Wapinski1, Michael Drage1, Fedaa Najdawi1, Christina K.B. Jayson

1PathAI, Boston, Massachusetts
2PathAI Diagnostics, Memphis, Tennessee