Artificial intelligence in gastrointestinal endoscopy: The future is almost here

Table 1 Summary of clinical studies involving computer-aided detection and computer-aided diagnosis in real time (during live colonoscopy or video recording)

Reference	Year	Type of CAD	Endoscopic Modality/ Input	Processing Modality	Study Design	Sensitivity	Specificity	Accuracy	Latency	Notes
Wang et al[25]	2015	CADe	White-Light Endoscopy	Polyp-Edge Detection Algorithm and Shot Extraction	Retrospective	-	-	97.7%1	0.02 s	36 false-positives per video
Fernández-Esparrach et al[38]	2016	CADe	White-Light Endoscopy	WM-DOVA	Retrospective	70.4%2	72.4%2	-	-	Accuracy and latency reported for this study
Tajbakhsh et al[37]	2016	CADe	White-Light Endoscopy	Hybrid Context-Shape Extractor, Edge Mapping	Retrospective	88.0%2 for CVC-ColonDB	-	-	0.3 s	0.1 False positives per frame
						48.0% for ASU-Mayo
Wang et al[40]	2017	CADe	White-Light Endoscopy	Deep learning, built on SegNet Architecture	Retrospective	91.6%2	96.3%2	100.0%1	0.04 s
Misawa et al[41]	2018	CADe	White-Light Endoscopy	Deep learning, built on a DCNN	Retrospective	90.0%2	63.3%2	76.5%1	-
Kominami et al[54]	2016	CADx	Magnifying NBI	Bag of features representation, SVM output	Prospective	93.0%3	93.3%3	93.2%4	-	97.5% concordance between automatic diagnosis and endoscopic diagnosis
Komeda et al[75]	2017	CADx	A mix of White-Light Endoscopy, NBI and Chromoendoscopy	Deep learning, built on a CNN	Retrospective	-	-	75.1%5
Byrne et al[59]	2017	CADx	White-Light Endoscopy and NBI	Deep learning, built on a DCNN	Retrospective	98.0%36	83.0% 36	94.0%4	0.05 s	For 19 polyps the system was unable to reach a credibility score threshold of ≥ 50%
Mori et al[58]	2017	CADx	Endocytoscopy and NBI	Texture analysis, automatic vessel extraction, SVM output	Prospective	97.0%3	67.0%3	83.0%4

¹Tracking accuracy or detection rate, defined as number of polyps detected by software/total number of polyps present in videos;

²Sensitivity and specificity for the detection of polyps;

³Sensitivity and specificity for the diagnosis of neoplastic versus non-neoplastic lesions;

⁴Accuracy defined as differentiation of adenomas from non-neoplastic lesions;

⁵Accuracy of a 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN;

⁶Sensitivity and specificity in this case are calculated based on histology of 106/125 polyps in the video test set. For the remaining 19 polyps the system was unable to reach a credibility score threshold of ≥ 50%; CADx: Computer-aided diagnosis; CADe: Computer-aided detection; SVM: Support vector machine; WM-DOVA: Window median depth of valleys accumulation; NBI: Narrow band imaging; CNN: Convolution neural network; DCNN: Deep convolution neural network.