Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions

Table 1 Summary of artificial intelligence studies on the detection and characterization of Barrett’s epithelium and associated neoplasia[8,29,30,33,34,37,39-42,45]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
de Groof et al[39]	Real-time Det + Ch of early BE neoplasia	Prospective study	1247 i	297 i	WLI	Hybrid: ResNet-UNet	Acc: 89%; Se: 90%; Sp: 88%
de Groof et al[40]	Real-time Det + Ch of early BE neoplasia	Prospective pilot study	1544 i	144 i	WLI	Hybrid: ResNet-UNet	Acc: 90%; Se 91%; Sp: 89%
Hashimoto et al[41]	Distinguish dysplastic from nondysplastic BE	Prospective pilot study	1374 i	458 i	WLI; NBI	Inception-ResNet-v2	Acc: 95%; Se: 96%; Sp: 94%
Ali et al[33]	BE risk stratification	Prospective pilot study	10000 i	194 v	WLI; NBI	ResNet-50; DeepLabv3 +	Acc: 98%
Hussein et al[45]	Det and delineation of BE dysplasia	Retrospective study	148936 v	276 v	WLI; i-scan	ResNet-101	Se: 91%; Sp: 79%; AUC: 0.93
Abdelrahim et al[37]	Det and localization of BE neoplasia	Prospective multicenter trial	1090171 i + v	471 i; 75 v	WLI	VGG16; SegNet	I: Acc/Se/Sp: 95%; v: Acc: 92%; Se: 95%; Sp: 91%
Tsai et al[30]	Det of BE	Retrospective study	771 i	160 i	NBI	EfficientNetV2B2	Acc: 94%; Se: 94%; Sp: 94%
Xin et al[8]	Det of early BE neoplasia	Retrospective multicenter	14046 i	400 i; 188 v		EfficientNet-Lite1; MobileNetV2; DeepLabv3 +	I: Se: 88%; Sp: 90%; v: Se: 79%; Sp: 94%
Meinikheim et al[34]	AI impact on nonexp BE neoplasia Det	Multicenter RCT	51273 i	96 v	WLI; NBI; Chromo	DeepLabv3; ResNet-50	Se: 70% to 78% w/AI; Sp: 67% to 73% w/AI
Jukema et al[42]	AI vs exp/nonexp in BE neoplasia Det	Prospective	3468 i	161 i; 161 v	NBI	EfficientNet-Lite1	Se: 84% to 96% w/AI; Sp: 90% to 98% w/AI
Jong et al[29]	Ensure AI reliability in BE neoplasia Det	Retrospective real word experience	1102 i; 12011 v	117 i	WLI; NBI	Hybrid: ResNet-50-vision transformer	Se: 85% (high-quality I); 62% (mod); 47% (low)

Det: Detection; Ch: Characterization; BE: Barrett’s epithelium; AI: Artificial intelligence; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; WLI: White light imaging; NBI: Narrow-band imaging; IEE: Image-enhanced endoscopy; Chromo: Chromoendoscopy with acetic acid and indigo carmine; ResNet: Residual network; UNet: U-shaped network; Deeplab: Deep convolutional neural networks for semantic image segmentation; SegNet: Segmentation network; VGG: Visual geometry group; EfficientNet: Efficient convolutional neural network; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; AUC: Area under the curve; mod: Moderate; w: With.