Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped

Table 2 Computer-aided detection and diagnosis system can achieve the thresholds of preservation and incorporation of valuable endoscopic innovations for diminutive, non-neoplastic rectosigmoid polyps

Ref.	Target	System	Performance	Year	Country
Aihara et al[35]	All polyps (n = 102)	AFE	Sensitivity: 94.2 % Specificity: 88.9% NPV: 85.2%	2013	Japan
Kuiper et al[36]	< 10 mm polyps (n = 207)	WAVSTAT, WAVSTAT + HRE	Accuracy: 74.4% Accuracy + HRE: 79.2% NPV: 73.5% NPV + HRE: 73.9 %	2015	Netherlands
Rath et al[37]	≤ 5 mm polyps (n = 137)	WAVSTAT4 + LIFS	Accuracy: 84.7% Sensitivity: 81.8% Specificity: 85.2% NPV: 96.1%	2016	Germany
Kominami et al[38]	All polyps (n = 118)	NBI	Concordance: 97.5% Accuracy: 93.2 % Sensitivity: 93% Specificity: 93.3% PPV: 93% NPV: 93.3%	2016	Japan
Mori et al[39]	≤ 5 mm polyps (n = 466)	EC-NBI-CAD EC-MB-CAD	Accuracy: 98.1% NPV EC-NBI-CAD:96.5% NPV EC-MB-CAD: 96.4%	2018	Japan

NBI: Narrow band imaging; CAD: Computer-aided detection and diagnosis; NPV: Negative predictive value.