Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis

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Abstract
Objective: Previous reports of deep learning-assisted assessment of Mayo endoscopic subscore (MES) in ulcerative colitis have only explored the ability to distinguish disease remission (MES 0/1) from severe disease (MES 2/3) or inactive disease (MES 0) from active disease (MES 1–3). We sought to explore the utility of deep learning models in the automated grading of each individual MES in ulcerative colitis. Methods: In this retrospective study, a total of 777 representative still images of endoscopies from 777 patients with clinically active ulcerative colitis were graded using the MES by two physicians. Each image was assigned an MES of 1, 2, or 3. A 101-layer convolutional neural network model was trained and validated on 90% of the data, while 10% was left for a holdout test set. Model discrimination was assessed by calculating the area under the curve (AUC) of a receiver operating characteristic as well as standard measures of accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results: In the holdout test set, the final model classified MES 3 disease with an AUC of 0.96, MES 2 disease with an AUC of 0.86, and MES 1 disease with an AUC 0.89. Overall accuracy was 77.2%. Across MES 1, 2, and 3, average specificity was 85.7%, average sensitivity was 72.4%, average PPV was 77.7%, and the average NPV was 87.0%. Conclusion: We have demonstrated a deep learning model was able to robustly classify individual grades of endoscopic disease severity among patients with ulcerative colitis.

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