Louis R. Ghanem

Damola Agbelese, Krishna Chaitanya, Pushpak Pati et al.

Reliable uncertainty quantification (UQ) is essential in medical AI. Evidential Deep Learning (EDL) offers a computationally efficient way to quantify model uncertainty alongside predictions, unlike traditional methods such as Monte Carlo (MC) Dropout and Deep Ensembles (DE). However, all these methods often rely on a single expert's annotations as ground truth for model training, overlooking the inter-rater variability in healthcare. To address this issue, we propose MEGAN, a Multi-Expert Gating Network that aggregates uncertainty estimates and predictions from multiple AI experts via EDL models trained with diverse ground truths and modeling strategies. MEGAN's gating network optimally combines predictions and uncertainties from each EDL model, enhancing overall prediction confidence and calibration. We extensively benchmark MEGAN on endoscopy videos for Ulcerative colitis (UC) disease severity estimation, assessed by visual labeling of Mayo Endoscopic Subscore (MES), where inter-rater variability is prevalent. In large-scale prospective UC clinical trial, MEGAN achieved a 3.5% improvement in F1-score and a 30.5% reduction in Expected Calibration Error (ECE) compared to existing methods. Furthermore, MEGAN facilitated uncertainty-guided sample stratification, reducing the annotation burden and potentially increasing efficiency and consistency in UC trials.

Evan Schwab, Gabriela Oana Cula, Kristopher Standish et al.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation of the large intestine. The severity of UC is often represented by the Mayo Endoscopic Subscore (MES) which quantifies mucosal disease activity from endoscopy videos. In clinical trials, an endoscopy video is assigned an MES based upon the most severe disease activity observed in the video. For this reason, severe inflammation spread throughout the colon will receive the same MES as an otherwise healthy colon with severe inflammation restricted to a small, localized segment. Therefore, the extent of disease activity throughout the large intestine, and overall response to treatment, may not be completely captured by the MES. In this work, we aim to automatically estimate UC severity for each frame in an endoscopy video to provide a higher resolution assessment of disease activity throughout the colon. Because annotating severity at the frame-level is expensive, labor-intensive, and highly subjective, we propose a novel weakly supervised, ordinal classification method to estimate frame severity from video MES labels alone. Using clinical trial data, we first achieved 0.92 and 0.90 AUC for predicting mucosal healing and remission of UC, respectively. Then, for severity estimation, we demonstrate that our models achieve substantial Cohen's Kappa agreement with ground truth MES labels, comparable to the inter-rater agreement of expert clinicians. These findings indicate that our framework could serve as a foundation for novel clinical endpoints, based on a more localized scoring system, to better evaluate UC drug efficacy in clinical trials.