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

Table 5 Summary of artificial intelligence studies on the detection of polyps and protruding lesions by video capsule endoscopy or small bowel endoscopy[135,138-140,213-221]

Ref.	Endoscopy type	Study design	Training set	Test set	AI model	Performance
Barbosa et al[135]	VCE	Retrospective	104 tumors, 100 normal	700 tumors, 2300 normal	CNN	Se: 94%; Sp: 93%
Li and Meng[213]	VCE	Retrospective	540 tumors, 540 normal	60 tumors, 60 normal	ML (SVM)	Acc: 84%; Se: 82%; Sp: 8%
Constantinescu et al[138]	VCE	Prospective	54 videos	90 images (32 polyps, 58 normal)	ANN	Acc: 98%; Se: 94%; Sp: 91%
Liu et al[214]	VCE	Retrospective	1800 (105 patients, 89 videos)	89 videos (15 tumors, 74 normal)	ML (SVM)	Se: 98%; Sp: 97%
Faghih Dinevari et al[215]	VCE	Retrospective	300 tumors, 300 normal	100 tumors, 100 normal	ML (SVM)	Acc: 94%; Se: 94%; Sp: 93%
Yuan and Meng[216]	VCE	Retrospective	1000 polyps, 3000 normal	200 tumors, 600 normal	DL	Acc: 98%
Ding et al[217]	VCE	Retrospective	158, 235	113, 268, 334	CNN	Se: PPA: 99.9%; PLA: 99.9%; Sp: PPA and PLA: 100%
Saito et al[218]	VCE	Retrospective	30, 584	17507 (7507 protruding lesions)	CNN	Se: 91%; Sp: 80%
Xie et al[219]	VCE	Retrospective	148, 357, 922	146, 956, 145	CNN	Se: 99%
Cardoso et al[139]	Enteroscopy	Retrospective	6340	507 protruding lesions, 1078 normal	CNN	Acc: 97%; Se: 97%; Sp: 97%
Inoue et al[220]	EGD	Retrospective	531	1080	CNN	Se: 95%; Sp: 87%
Ding et al[221]	VCE	Retrospective	280, 426	240 videos	CNN	Acc: 81%; Se: 96%; Sp: 98%
Zhu et al[140]	DBE	Retrospective	8222	3148	CNN	Det model: Se: 92%; Sp: 93%; Class model: Se: 80%-93%; Acc: 86%

AI: Artificial intelligence; VCE: Video capsule endoscopy; EGD: Esophagogastroduodenoscopy; DBE: Double balloon enteroscopy; CNN: Convolutional neural network; ML: Machine learning; SVM: Support vector machine; ANN: Artificial neural network; DL: Deep learning; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; PPA: Per patient analysis; PLA: Per lesion analysis.