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

Table 3 Summary of the non-controlled studies involving computer-aided diagnosis for colonoscopy including studies with combined detection and diagnosis systems

Ref.	Year	Study design	Study aim	System	Number of patients/colonoscopies used for training/test datasets (total)	Number of colonoscopy/polyp images/videos used in training/test datasets	Diagnostic properties
Tischendorf et al[38]	2010	Prospective pilot	Distinguishing adenomas from non-adenomas	CADx based on SVMs	NA/128; Colonoscopy videos	NA/209 polyps containing 160 neoplastic and 49 non-neoplastic polyps in the test dataset	CADx: Sensitivity = 90%, specificity = 70%, correct classification rate = 85.3%. Consensus decision between the human. Observers: Sensitivity = 93.8%, specificity = 85.7%, correct classification rate = 91.9%. “Safe” decision, when there was interobserver discrepancy: Sensitivity = 96.9%, specificity = 71.4%, correct classification rate = 90.9%
Aihara et al[47]	2013	Prospective	Distinguishing neoplastic from non-neoplastic lesion	CADx based on numerical color analysis of autofluorescence endoscopy as an Adobe AIRapplication	NA/32 patients in the test dataset	NA/102 lesions containing 75 neoplastic lesions in the test dataset	Sensitivity = 94.2%; specificity = 88.8%; PPV = 95.6%; NPV = 85.2%
Mori et al[87]	2015	Retrospective pilot	Distinguishing small (≤ 10 mm) neoplastic from non-neoplastic lesion	CADx (EC-CAD) based on CNN	NA/152 patients in the test dataset	NA/176 small polyps in the test dataset containing 137 neoplastic and 39 non-neoplastic polyps for the test dataset	Accuracy = 89.2%, 95%CI = 83.7%-93.4%; Sensitivity = 92.0%, 95%CI = 86.1%-95.9%; specificity of 79.5%, 95%CI = 63.5%-90.7%
Kuiper et al[49]	2015	Retrospective	Distinguishing small (≤ 9 mm) neoplastic from non-neoplastic lesion	CADx (WavSTAT) based on CNN	NA/87 patients in the test dataset	NA/207 small lesions in the test dataset	Accuracy = 74.4%, 95%CI = 68.1%–79.9%; sensitivity = 85.3%, 95%CI = 0.78–0.90; specificity = 58.8%, 95%CI = 0.48–0.69; PPV = 74.8%, 95%CI = 0.67–0.81; NPV = 73.5%; accuracy of on-site recommended surveillance interval = 73.7%
Misawa et al[34]	2018	Retrospective	Distinguishing neoplastic from non-neoplastic lesion categorized	CADx based on SVMs	NA	979 images containing 381 non-neoplasms and 598 neoplasms in the training dataset/100 images containing 50 non-neoplasms and 50 neoplasms in the test dataset	Accuracy = 90.0%, 95%CI = 82.4–95.1; sensitivity = 84.5%, 95%CI = 72.6–92.7; specificity = 97.6%, 95%CI = 87.4–99.9; PPV = 98.0%, 95%CI = 89.4–99.9; NPV = 82.0%, 95%CI = 68.6–91.4
Byrne et al[51]	2018	Retrospective	Distinguishing neoplastic from non-neoplastic lesions	CADx + CADe based on an improved DCNN model using NBI	NA	NA/21804 unseen frames in the test dataset	Accuracy = 99.94%; sensitivity = 95.95%; specificity = 91.66%; NPV = 93.6%; prediction of polyp videos = 97.6%
Mori et al[48]	2018	Prospective	Distinguishing diminutive (≤ 5 mm) neoplastic from non-neoplastic lesions	CADx based on SVMs used with NBI and endocytoscope	NA/791 patients in the test dataset	61925/466 polyps from 325 patients in the test dataset	CADx-NBI: Sensitivity = 92.7%, 95%CI = 89.1–95.4; specificity = 89.8%, 95%CI = 84.4–93.9; PPV = 93.7%, 95%CI = 90.2–96.2; NPV = 88.3%, 95%CI = 82.7–92.6. CADx-endocytoscope: Sensitivity = 91.3%, 95%CI = 87.5–94.3; specificity = 88.7%, 95%CI = 83.1–93.0; PPV = 92.9%, 95%CI = 89.3–95.6; NPV = 86.3%, 95%CI = 80.4–90.9
Byrne et al[45]	2019	Retrospective	Distinguishing diminutive (≤ 5 mm) neoplastic from non-neoplastic lesions	CADx based on DCNN		Training dataset: 60089 frames from 223 polyp videos (29% NICE type 1, 53% NICE type 2 and 18% of normal mucosa with no polyp)/validation dataset: 40 videos (NICE type 1, NICE type 2 and two videos of normal mucosa)/test dataset: 125 consecutively identified diminutive polyps, comprising 51 hyperplastic polyps and 74 adenomas	Accuracy = 94%, 95%CI = 86%-97%; sensitivity = 98%, 95%CI = 92%-100%; Specificity = 83%, 95%CI = 67%-93%; NPV = 97%; PPV = 90%
Song et al[88]	2020	Retrospective	Distinguishing adenomas from SPs	CADx based on DCNN	NA	12480 image patches of 624 polyps/two test datasets of 545 polyp	Agreement between the true polyp histology CADx = 0.614–0.642; accuracy = 81.3%–82.4%; sensitivity = 82.1%; specificity = 93.7%; PPV = 78%; NPV = 95%; the AUC = 0.93–0.95, 0.86–0.89, and 0.89–0.91 for serrated polyps, benign adenoma/mucosal or superficial submucosal cancer, and deep submucosal cancer, respectively
Kudo et al[89]	2020	Retrospective	Distinguishing small (≤ 10 mm) neoplastic from non-neoplastic lesions	The EndoBRAIN system (CADx + CADe based on DCNN)	NA/89 patients test set	69,142 images taken at 520-fold magnification and 2,000 polyps/100 lesions (≤ 10 mm) in the test dataset	CADe: Accuracy = 98%, 95%CI = 97.3%–98.6%; sensitivity = 96.9%, 95%CI = 95.8%–97.8%; specificity = 100%, 95%CI = 99.6%–100%; PPV = 100%, 95%CI = 99.8%–100%; NPV = 94.6%, 95%CI = 92.7%–96.1%; CADx: Accuracy = 96%, 95%CI = 95.1%–96.8%; sensitivity = 96.9%, 95%CI = 95.8%–97.8%; specificity = 94.3%, 95%CI = 92.3%–95.9%; PPV = 96.9%, 95%CI = 95.8%–97.8%; NPV = 94.3%, 95%CI = 92.3%–95.9%

CADe: Computer-assisted detection system; CADx: Computer-assisted diagnosis 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; SVM: Support vector machine; SP: Serrated polyps; CI: Confidence interval.