Explainable Artificial Intelligence for the Mayo Endoscopic Score in Ulcerative Colitis.

INTRODUCTION The Mayo endoscopic score (MES) is used widely in ulcerative colitis (UC) for severity assessment and therapeutic decision-making. Deep learning (DL) models developed to determine MES currently lack explainability. We aimed to develop explainable models for the MES in patients with UC and examine the human-artificial intelligence interactions with the models.

METHODS This was a retrospective multicenter study conducted across four large tertiary institutions in China. A total of 2,600 white-light images were used for training. Two approaches were adopted: traditional blackbox or explainable AI (XAI). The trained models were evaluated with three external test datasets (#1 Changshu & Jintan hospitals, n = 100; #2 HyperKvasir, n = 100; #3 Yongding hospital, n = 260), and the performance was compared with endoscopists. The primary outcome was the performance of 4-way classification. For explainability, moreover, Grad-CAM was for computer vision, while local interpretation, variable importance, and partial dependence plots were for the classifier within XAI.

RESULTS In the test #1 dataset, a Xception-backboned XAI showed accuracy of 0.910, Matthew's correlation coefficient 0.880 and Cohen's kappa 0.960 [95% CI, 0.940 - 0.990]. The metrics were better than other models, as well as the two endoscopists. With the AI-assistance, the performance of endoscopists were improved (senior's accuracy from 0.890 to 0.930 and junior's accuracy from 0.810 to 0.880). Similar trend was observed in the test #2 and #3 datasets.

CONCLUSION The use of an explainable framework empowers AI models to achieve improved performance with transparency. XAI can also improve endoscopist performance in interpretation of MES in UC.