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

Table 2 Performance of deep learning models in capsule endoscopy for the assessment of common pathologies in inflammatory bowel disease

Ref.	Model	Key performance metrics
Klang et al[58]	CNN	Ulcers vs normal mucosa: AUC = 0.94-0.99
Barash et al[61]	CNN	Ulcer grade 1 vs 3: AUC = 0.958; ulcer grade 1 vs 2: AUC = 0.565; ulcer grade 2 vs 3: AUC = 0.939
Ribeiro et al[67]	CNN	Ulcers and erosions: AUC = 1.00
Kratter et al[69]	CNN	Ulcer detection (cross-domain model): AUC = 0.921-0.948; ulcer detection (combined model): AUC = 0.984-0.998
Klang et al[59]	CNN	Strictures vs ulcers: AUC = 0.942; strictures vs mild ulcers: AUC = 0.992; strictures vs moderate ulcers: AUC = 0.975; strictures vs severe ulcers: AUC = 0.889
Majtner et al[65]	CNN	SB ulcers: Accuracy = 98.5%; LB ulcers: Accuracy = 98.1%
Ferreira et al[66]	CNN	SB and LB ulcers: AUC = 1.00

Select studies showing the performance of common deep learning models in capsule endoscopy in the evaluation of ulcers, erosions, and strictures. SB: Small bowel; LB: Large bowel; CNN: Convolutional neural networks; AUC: Area under the receiver operating characteristic curve.