Chierici

et al., 2022

[40]

Italy

14,226 images

Endoscopy images

Residual Network (ResNet)

The combination model resnet 34-50-101, resnet 34-50-152 exhibited more prominent recognition capabilities in N-P, UC-CD, and UC-N samples compared to any individual resnet model.

Ensemble learning methods effectively improve the performance of the combination model.

Eyal

et al., 2020

[41]

Israel

27,892 images

CE images

Deep Learning Network

The network achieved an average accuracy of 93.5% in classifying narrow and non-narrow cases. It also demonstrated good discrimination between narrow and normal mucosa, narrow and all ulcers, as well as narrow and different grades of ulcers.

The DL network was validated to effectively identify intestinal strictures, normal mucosa, and different grades of ulcers in Crohn’s disease patients’ CE images.

Barash

et al., 2021

[42]

Israel

17,640 images

CE images

CNN

The accuracy for classifying grade 1 and grade 3 ulcers was 0.91 (95% confidence interval, 0.867 - 0.954). For grade 2 and grade 3, it was 0.78 (95% confidence interval, 0.716 - 0.844), and for grade 1 and grade 2, it was 0.624 (95% confidence interval, 0.547 - 0.701).

The CNN achieved a high accuracy in detecting severe CD ulcers but performed poorly in mild CD ulcers.