Study Background
Microscopic inflammation has been shown to be an important indicator of disease activity in ulcerative colitis (UC) 1. However, manual histologic scoring is semi-quantitative and subject to interobserver variation, and AI-based solutions often lack interpretability 2.
Here we report two distinct quantitative approaches to predict disease activity scores and histological remission using AI-powered digital pathology. Both the random forest classifier (RFC) and graph neural network (GNN) further provide explainability and biological insight by identifying histological features informing model predictions.
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
Kathleen Sucipto 1, Archit Khosla 1, Michael G. Drage 1, Yilan Wang 1, Darren Fahy 1, Mary Lin 1, Murray B. Resnick 1, Michael Montalto 1, Andrew Beck 1, Ilan Wapinski1, Stephanie Hennek 1, Christina K.B. Jayson 1, & Fedaa Najdawi 1
1 PathAI, Boston, Massachusetts