Artificial intelligence in colonoscopy

Table 2 White light endoscopy

Ref.	Study design	Algorithm type	Dataset	Results
Komeda et al[23]	Diagnostic model development	CAD-neural network combination to assist WL endoscopy	1200 training images then tested on 10 new images	Cross-validation accuracy: 0.751
Zheng et al[24]	Diagnostic model development	WL endoscopy using YOLO (CNN)	196 WL images from an independent public database	Accuracy: 79.3%
				Sensitivity: 68.3%
Wang et al[25]	Prospective crossover study	Traditional WL endoscopy vs CAD colonoscopy	369 patients from a single hospital in China	Adenoma miss rate of 13.9% in the CAD group vs 40% in the traditional group, P < 0.0001
Yang et al[26]	Diagnostic model development	Validation of a deep learning model called “ResNet-152” to classify colorectal lesions	3828 WL colonoscopy images from 1339 patients	Mean model accuracy: 79.2% for advanced CRC, early CRC/HGD, TA, and non-neoplastic
				AUC: 0.818

CAD: Computer-aided diagnosis; WL: White light; CNN: Convoluted neural network.