Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

Table 2 Summary of the non-controlled studies involving computer-aided detection for colonoscopy

Ref.	Year	Study design	System	Image modality	Number of patients/colonoscopies used for training/test datasets (total)	Number of colonoscopy/polyp images/videos used for training/test datasets	Diagnostic properties
Park and Sargent[81]	2016	Retrospective	CADe based on DCNN using a conditional random field model	Still images	35 (colonoscopy videos)	562/562 (colonoscopy still images)	Sensitivity = 86%; specificity = 85%; AUC = 0.8585
Fernández-Esparrach et al[73]	2016	Retrospective	CADe based on energy map	Still images	NA/24 colonoscopy videos containing 31 different polyps	NA/Experiment A: 612 polyp images from all 24 videos. Experiment B: 47886 frames from the 24 videos	Experiment A: accuracy = small vs all polyps = 77.5%, 95%CI = 71.5%–82.6% vs 66.2%, 95%CI = 61.4%–70.7%; P < 0.01. Experiment B: The AUC = high quality frames vs all Frames = 0.79, 95%CI = 0.70–0.87 vs 0.75, 95%CI = 0.66–0.83
Yu et al[82]	2017	Retrospective	CADe based on three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully CNN (3D-FCN)	Videos	20/18 (colonoscopy videos)	3799 frames with polyps in total	Sensitivity = 71%; PPV = 88%; precision = 88.1%
Billah et al[83]	2017	Retrospective	CADe based on CNN and color wavelet features using a linear support vector machine	Still images	100 (colonoscopy videos for combined training and test datasets)	14000 still images (combined for training and test datasets)	Accuracy = 98.65%; sensitivity = 98.79%; specificity = 98.52%
Zhang et al[84]	2017	Retrospective	CADe based on DCNN	Still images	NA	2262/150 random, 30 NBI (colonoscopy still images)	Accuracy = 85.9%; sensitivity = 98%; PPV = 99%; precision = 87.3%; recall rate = 87.6%; AUC = 1.0
Wang et al[85]	2018	Retrospective	CADe based on DNN	Still images	1290/1138 (2428) patients	27113/5545 (colonoscopy images)	Sensitivity = 94.38%, 95%CI = 93.80%-94.96% in images with polyp; AUC = 0.984
Misawa et al[34]	2018	Retrospective	CADe based on CNN	Videos	59/14 (73)	411/135 (colonoscopy videos containing 150 polyps)	Per-polyp sensitivity = 94%; per-frame sensitivity = 90%; specificity = 63.3%; accuracy = 76.5%; false positive rate = 60%; AUC = 0.87
Yamada et al[33]	2019	Retrospective	CADe based on DNN	Videos	NA/77 (number of videos)	13983/4840 (colonoscopy videos)	Sensitivity = 97.3%, 95%CI = 95.9%–98.4%; specificity = 99.0%, 95%CI = 98.6%–99.2%; AUC = 0.975, 95%CI = 0.964–0.986)
Urban et al[35]	2018	Retrospective	CADe based on deep learning CNN	Videos	Several training and validation sets: (1) Cross-validation on the 8641 images; (2) Training on the 8641 images and testing on the 9 videos, 11 videos, and independent dataset; and (3) Training on the 8641 images and 9 videos and testing on the 11 videos and independent dataset		Sensitivity = 96.9%; specificity: 95%; AUC = 0.991; accuracy = 96.4%; false positive rate = 7%
Klare et al[37]	2019	Prospective	Automated polyp detection software (“KoloPol,” Fraunhofer IIS, Erlangen, Germany) based on CNN	Live colonoscopy videos	NA	NA/55 (colonoscopy videos)	Per-polyp sensitivity = 75.3%, 95%CI = 62.3%-84.9%; PDR = 50.9%, 95%CI = 37.1%-64.4%; ADR = 29.1%, 95%CI = 17.6%-42.9%
Ozawa et al[86]	2020	Retrospective	CADe based on DCNN	Still images	12895 patients	16418/7077	Sensitivity = 92%; PPV = 86%; accuracy = 83%; identified adenomas = 97%

CADe: Computer-assisted detection system; CNN: Convolutional neural network; DCNN: Deep learning convolutional neural network; AUC: Area Under the Receiver Operating Characteristic curve; PPV: Positive predictive value; NPV: Negative predictive value; PDR: Polyp detection rate; ADR: Adenoma detection rate; CI: Confidence interval.