Artificial intelligence in colonoscopy

Table 1 Colorectal polyp detection

Ref.	Study design	Algorithm type	Dataset	Results
Karkanis et al[8]	Retrospective	CADe (Wavelet Decomposition)	180 images	Sensitivity: 93.6%
				Specificity: 99.3%
Urban et al[2]	Retrospective	CADe (DCNN)	8461 images &20 colonoscopy videos	Accuracy: 96.4%
				False Positive: 7%
Klare et al[12]	ProspectiveIn vivo	CADe	55 colonoscopies	ADR of: CAD 29.1% and Endoscopist 30.9%
Wang et al[5]	Non-blinded RCT	CADe using Shanghai Wision Al Co. Ltd. (DCNN)	Randomized 522 patients to CADe and 536 to control group	ADR of CAD 29.1% vs control 20.3%
Wang et al[4]	Double blinded RCT	CADe using EndoScreener (DCNN)	Randomized 484 patients to CAD and 478 to sham system	ADR of CAD 34% vs control 28%
Gong et al[13]	Partially blinded RCT	CADe using ENDOANGEL (DCNN)	Randomized 355 patients to CAD and 349 to control	ADR of CAD 16% vs control 8%
Repici et al[14]	Partially-blinded RCT	CADe using GI-Genius (CNN)	Randomized 341 patients to CAD and 344 to control	ADR of CAD 54.8% vs control 40.4%
Liu et al[15]	Non-blinded RCT	CADe using Henan Xuanweitang Medical Information Technology Co. Ltd (convolutional 3D network)	Randomized 508 patients to CAD and 518 control	ADR of CAD 39% vs control 23%
Su et al[16]	Partially blinded RCT	Automatic quality control system (ACQS)(DCNN)	Randomized 308 patients to AQCS and 315 to control	ADR of AQCS 28.9% vs control 16.5%

CADe: Computer-aided detection; CAD: Computer-aided diagnosis; DCNN: Deep convolutional neural network; ADR: Adenoma detection rate.

Table 2 White light endoscopy

Ref.	Study design	Algorithm type	Dataset	Results
Komeda et al[23]	Diagnostic model development	CAD-neural network combination to assist WL endoscopy	1200 training images then tested on 10 new images	Cross-validation accuracy: 0.751
Zheng et al[24]	Diagnostic model development	WL endoscopy using YOLO (CNN)	196 WL images from an independent public database	Accuracy: 79.3%
				Sensitivity: 68.3%
Wang et al[25]	Prospective crossover study	Traditional WL endoscopy vs CAD colonoscopy	369 patients from a single hospital in China	Adenoma miss rate of 13.9% in the CAD group vs 40% in the traditional group, P < 0.0001
Yang et al[26]	Diagnostic model development	Validation of a deep learning model called “ResNet-152” to classify colorectal lesions	3828 WL colonoscopy images from 1339 patients	Mean model accuracy: 79.2% for advanced CRC, early CRC/HGD, TA, and non-neoplastic
				AUC: 0.818

CAD: Computer-aided diagnosis; WL: White light; CNN: Convoluted neural network.

Table 3 Narrow band imaging

Ref.	Study design	Algorithm type	Dataset	Results
Tischendorf et al[29]	Prospective Ex vivo	CAD – NBI (support vector machine)	209 polyp images	Accuracy: 85.3%
				Sensitivity: 90%
				Specificity: 70.2%
Gross et al[27]	Prospective Ex vivo	CAD – NBI (support vector machine)	434 polyp images	Accuracy: 93.1%
				Sensitivity: 95%
				Specificity: 90.3%
				NPV: 92.4%
Chen et al[31]	Retrospective	CAD – NBI (DCNN)	284 polyp images	Accuracy: 90.1%
				Sensitivity: 96.3%
				Specificity: 78.1%
				PPV: 89.6%
				NPV: 91.5%
Byrne et al[30]	Retrospective	CAD—NBI (DCNN)	125 polyp videos	Accuracy: 94%
				Sensitivity: 98%
				Specificity: 83%
				PPV: 90%
				NPV: 97%
Kominami et al[32]	Prospective	CAD –NBI (support vector machine)	118 polyps	Accuracy: 94.9%
				Sensitivity: 95.9%
				Specificity: 93.3%
				PPV: 95.9%
				NPV: 93.3%
Mori et al[33]	Prospective	CAD – NBI (support vector machine)	466 polyps	NPV: 95.2% to 96.5%
Song et al[35]	Prospective In vivo	CAD –NBI (DCNN)	363 polyps	Accuracy: 82.4%

CAD: Computer-aided diagnosis; NBI: Narrow band imaging; DCNN: Deep convolutional neural network.

Table 4 Laser-induced fluorescence spectroscopy

Ref.	Study design	Algorithm type	Dataset	Results
Kuiper et al[37]	Diagnostic model development	Diagnostic performance of WavSTAT	87 patients	Accuracy: 73.4%
				NPV: 74.4%
Rath et al[38]	Diagnostic model development	Diagnostic performance of WavSTAT for predicting polyp histology	27 patients	Accuracy: 84.7%
				Sensitivity: 81.8%
				Specificity: 85.2%
				NPV: 96.1%
Min et al[39]	Randomized controlled trial	Linked color imaging with laser endoscopic system vs WL	141 patients from 3 hospitals in China	Polyp detection rate of 91% in the LCI group, 73% in the WL group, P < 0.0001

WL: White light.

Table 5 Autofluorescence endoscopy

Ref.	Study design	Algorithm type	Dataset	Results
Arita et al[44]	Diagnostic model development	Calculation of a color-contrast index (CCI) for AFI	43 patients who underwent both WL and AF endoscopy	Sensitivity: 95.3%
				Specificity: 63.6%
Aihara et al[45]	Diagnostic model development	CAD-assisted AF	32 patients undergoing colonoscopy in a Japanese hospital	Sensitivity: 94.2%
				Specificity: 88.9%
				PPV: 95.6%
				NPV: 85.2%
Inomata et al[46]	Diagnostic model development	CAD-assisted AF	88 patients	Accuracy: 82.8%
				Sensitivity: 83.9%
				Specificity: 82.6%
				PPV: 53.1%
				NPV: 95.6%
Horiuchi et al[47]	Diagnostic model development	CAD-assisted AF	95 patients undergoing colonoscopy	Accuracy: 91.5%
				Sensitivity: 80.0%
				Specificity: 95.3%
				PPV: 85.2%
				NPV: 93.4%

AFI: Autofluorescence imaging; WL: White light; CAD: Computer-aided diagnosis; AF: Autofluorescence.

Table 6 Magnifying chromoendoscopy

Ref.	Study design	Algorithm type	Dataset	Results
Takemura et al[51]	Partially blinded retrospective study	CAD using HuPAS	134 pit pattern images	Accuracy: 98.5%
Häfner et al[52]	Partially blinded retrospective study	CAD using Dual-Tree Complex Wavelet Transform	484 RGB pit pattern images	Accuracy: 99.59%
Qi et al[53]	Diagnostic model development	CAD using automated imaged analysis	79 colon samples (14 normal, 44 normal tissue adjacent to cancer, 21 malignant)	Automated segmentation achieved precision ratio of 0.69 and match ratio of 0.73

CAD: Computer-aided diagnosis.

Table 7 Endocytoscopy

Ref.	Study design	Algorithm type	Dataset	Results
Mori et al[54]	Pilot study	CAD using EC-CAD	176 colorectal polyps from 152 patients	Accuracy: 89.2%
				Sensitivity: 92%
				Specificity 79.5%
Takeda et al[55]	Retrospective study	CAD using EC-CAD	5543 endocytoscopy images for machine learning. 200 test images	Overall
				Accuracy: 94%
				Sensitivity: 89.4%
				Specificity: 98.9%
				PPV: 98.8%
				NPV: 90.1%
				High-confidence diagnosis
				Accuracy: 99.3%
				Sensitivity: 98.1%
				Specificity: 100%
				PPV: 100%
				NPV: 98.8%
Mori et al[33]	Single-group, open-label, prospective study	Real-time CAD during colonoscopy	466 diminutive polyps from 325 patients	Accuracy: 98.1%
				Sensitivity 93.8%
				Specificity 90.3%
				PPV 94.1%
				NPV 89.8%
Kudo et al[56]	Retrospective study	CAD using EndoBRAIN	100 polyps from 89 patients	Accuracy: 98%
				Sensitivity 96.9%
				Specificity 100%
				PPV 100%
				NPV 94.6%

CAD: Computer-aided diagnosis.

Table 8 Confocal endomicroscopy

Ref.	Study design	Algorithm type	Dataset	Results
André et al[58]	Diagnostic model development	CAD using content based image retrieval (CBIR) approach	135 polyps from 71 patients	Accuracy: 89.6%
				Sensitivity 92.5%
				Specificity 83.3%
Ştefănescu et al[59]	Diagnostic model development	CAD using NAVICAD and a two layer CNN	1035 endomicroscopy images including 725 for training, 155 for validation, and 155 for testing.	Testing decision accuracy error rate of 15.48% (24 out of 155 images)
Taunk et al[60]	Feasibility study	CAD using expectation-maximization algorithm	189 endomicroscopy images from 26 patient	Accuracy: 94.2%
				Sensitivity 94.8%
				Specificity 93.5%

CAD: Computer-aided diagnosis; CNN: Convoluted neural network.

Table 9 Robotics

Ref.	Study design	Algorithm type	Dataset	Results
Eickhoff et al[63]	Prospective, nonrandomized, unblinded feasibility study	CAD using NeoGuide Endoscopy System	10 patients	100% cecal intubation rate. Median time to cecum 20.5 min. 0 complications or adverse effects reported at discharge, 48 h, and 30 d
Pullens et al[64]	Randomized control trial with crossover design	CAD using automated lumen centralization	8 expert endoscopists and 10 endoscopy-naïve novices performing endoscopy on a validated colon model with 21 polyps	Novice
				Accuracy: 88.1%
				Time to cecum: 8 min 56 s
				Experts
				Accuracy: 69%
				Time to cecum: 13 min 1 s

CAD: Computer-aided diagnosis.