Table 2.

Summary of convolutional neural network studies.

STUDY	ALGORITHM TYPE	STUDY DESIGN	IMAGE TYPE	TRAINING SET	TESTING SET	PROCESSING TIME	OUTCOMES
BILLAH 2017[22]	CNN	Ex-vivo	Still	14,000 combined training/testing	14,000 combined training/testing	Not reported	99% sensitivity, 99% specificity
YU 2017[36]	CNN	Ex-vivo	Video	ASU-Mayo 20 videos	ASU-Mayo 18 videos	1.23 s per frame	71% sensitivity, 88% PPV
MISAWA 2018[52]	CNN	Ex-vivo	Video	411 video clips	135 video clips	Not reported	94% per-polyp sensitivity, 90% per-frame sensitivity with 60% false positive rate
PARK AND SARGENT 2016[53]	CNN	Ex-vivo	Still	562 images	Same set as training	Not reported	86% sensitivity, 85% specificity
POGORELOV 2018[54]	CNN	Ex-vivo	Still	Multiple sets from 1350 to 11954 frames	Multiple sets from 1350 to 11954 frames	Not reported	75% sensitivity, 94% specificity
ZHANG 2017[23]	CNN	Ex-vivo	Still	Pretrained on ImageNet 1.2 mil and Places2015 2.5mil; trained on2262	150 random + 30 NBI images	Not reported	98% sensitivity, 99% PPV, 1.00 AUROC
URBAN 2018[24]	CNN	Ex-vivo	Video	Pretrained on ImageNet 1.2 mil, trained on multiple sets: 8641 images, 1330 images, 9 videos, 53588 images from videos, 11 “challenging” videos	Multiple combinations of training datasets	10 ms per frame (real-time)	90% sensitivity with 0.5% false positive rate
KLARE 2019[25]	CNN	In-vivo cohort study	Live colonoscopy	Not available	55 live colonoscopies	50 ms latency	Per-polyp sensitivity 75%, ADR 29% (compared to endoscopist 31%)
WANG 2019[26]	CNN	Randomized controlled trial	Live colonoscopy	5545 images	1058 patients - 536 standard colonoscopy, 522 with CAD system	25 fps with 77 ms latency	ADR increased in CAD group from 20% to 29%, polyps per procedure 0.97 vs. 0.51

Abbreviations: CNN, convolutional neural network; NBI, narrow-band imaging; PPV, positive predictive value; AUROC, Area Under the Receiver Operating Characteristic; ADR, adenoma detection rate; CAD, computer-aided detection