Table 2.

Endoscopic studies involving artificial intelligence for diagnosis and prediction of colorectal cancer

	AI algorithm/system	Study design	Sensitivity	Specificity	AUC	Accuracy
Ichimasa et al. (2018)	SVM	Prediction of lymph node metastasis post endoscopic resection of T1 colorectal cancer	100%	66%	–	69%
Nakajima et al. (2020)	CNN	Automatic diagnosis system by computer-aided diagnosis (CAD) based on plain endoscopic images	81%	87%	0.888	84%
Lai et al. (2021)	DNN	Improve polyp detection and discrimination by CAD	100%	100%	–	74–95%
Yamada et al. (2019)	CNN	Develop a real-time detection system for colorectal neoplasm	97.3%	99%	0.975	Good and excellent*
Chen et al. (2019)	DNN	Develop a CAD diagnosis system to analyze narrow-bind images	96.3%	78.1%	–	90.1
Repici et al. (2020)	CNN	To assess the safety and efficacy of a computer-aided detection (CADe) system	–	–	–	–
Kudo et al. (2020)	CNN	To determine diagnostic accuracy of EndoBRAIN	96.9%	100%	–	98%
Mori et al. (2018)	SVM	Evaluate the performance of real-time CADe with endocytoscope	91.3–95.2%	65.6–95.9%	–	–
Nguyen et al. (2020)	CNN	To pre-classify the in vivo endoscopic images	19.6–87.4%	42.5–90.6%	–	52.6–68.9%
Deding et al. (2020)	–	To investigate relative sensitivity of colon capsule endoscopies compared with computer tomography colongraphy	2.67#	–	–	–

SVM: support vector machines; CNN: convolutional neural network; DNN: deep neural network; AUC: area under the curve from the receiver operating characteristics; *shown using intersection over the union (IOU); ^#relative sensitivity