Artificial intelligence in the management of inflammatory bowel disease: What’s next?

Table 1 Performance of deep learning models in endoscopic assessment of mucosal healing

Ref.	Input	Model	Key performance metrics
Stidham et al[31]	Images	CNN	MES 0-1 vs MES 2-3: AUC = 0.97
Ozawa et al[32]	Images	CNN	MES 0 vs MES 1-3: AUC = 0.86; MES 0-1 vs MES 2-3: AUC = 0.98
Gottlieb et al[35]	Videos	RNN	EH by eMS: Accuracy = 95.52%; EH by UCEIS: Accuracy = 97.04%
Takenaka et al[34]	Images	DNUC	UCEIS ≤ 1: Accuracy = 90.1%
Higuchi et al[68]	Images1	CNN	MES 0-3: Accuracy = 91.3%-99.4%
Yao et al[33]	Videos	CNN	MES 0-1 vs MES 2-3: Accuracy = 83.7%
Iacucci et al[47]	Videos	CNN	PICaSSO ≤ 3 (VCE): AUC = 0.94; UCEIS ≤ 1: AUC = 0.85

¹Derived from capsule endoscopy.

Select studies showing the performance of common deep learning models in assessing endoscopic healing by Mayo endoscopic score, endoscopic Mayo score, ulcerative colitis endoscopic index of severity, and Paddington international virtual chromoendoscopy score. VCE: Virtual chromoendoscopy; CNN: Convolutional neural network; RNN: Recurrent neural network; DNUC: Deep neural network for the evaluation of ulcerative colitis; MES: Mayo endoscopic score; AUC: Area under the receiver operating characteristic curve; EH: Endoscopic healing; eMS: Endoscopic Mayo score; UCEIS: Ulcerative colitis endoscopic index of severity; PICaSSO: Paddington international virtual chromoendoscopy score.