Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases

Table 1 Application of artificial intelligence on common gastrointestinal benign diseases

Ref.	Aim and disease	Prospective/retrospective	AI method	Endoscopy image	Training dataset	Validation dataset	Result sensitivity	Result specificity	Result accuracy/AUC
Esophageal benign diseases
de Groof et al[12]	Detecting Barrett’sneoplasia	Retrospective	CAD	WLI images	40 images	A leave one out cross validation	92%	95%	85%1
Jisu et al[39]	Distinguishing BE	Retrospective	CNNs	Endomicroscopic images	262 images	Image distortion methods			80.77%1
Ebigbo et al[40]	Distinguishing BE	Retrospective	CNNs (ResNet)	WLI images	129 images	62 images	83.7%	100.0%	89.9%1
Sehgal et al[41]	Detecting dysplasia in BE	Retrospective	ML (decision trees)	Video recordings(AAC)	40 patients with NDBE and DBE		97%	88%	92%1
de Groof et al[14]	Detecting Barrett’sneoplasia	Retrospective	CNN (CAD (ResNet-UNet))	WLI images	494364 images	1704 images (early stage neoplasia in BE and NDBE from 669 patients)	90%	88%	89%1
Dong et al[16]	Screening high risk EV	Retrospective	ML (Random forest)		238 patients	109 patients			Training set (0.84); Validation set (0.82)
Gastric benign diseases
Zhang et al[42]	Diagnosing CAG	Retrospective	CNNs (DenseNet)	WLI images	5470 images	Five-fold cross validation	94.5%	94.0%	94.2%1
Guimarães et al[43]	DiagnosingCAG	Retrospective	CNNs (VGG16)	WLI images	200 images	70 images(ten-fold cross validation)			93%1/0.98
Horiuchi et al[44]	Differentiating CAG	Retrospective	CNNs (GoogLeNet)	ME-NBI images	1078 images	107 images	95.4%	71.0%	85.3%1/0.85
Zhang et al[7]	Diagnosing PU	Retrospective	CNNs (ResNet34)	WLI images	4200 images	228 images	78.9%	88.4%	86.4%1
Lee et al[45]	Differentiating PU	Retrospective	CNNs (ResNet-50/ Inception v3/VGG16 model)	WLI images	200 images	20 images			92.6%1/85.24%1/91.2%1
Namikawa et al[46]	Classifying gastriccancers and ulcers	Retrospective	CNNs (SSD)	WLI/NBI/chromoendoscopy images	373 images	720 images	93.3%	99.0%	93.3 %1
Zhang et al[26]	Detecting GP	Retrospective	CNNs (SSD-GPNet)	WLI images	404 images	50 images			93.92%1
Intestinal benign diseases
Hwang et al[29]	Classifying hemorrhagic and ulcerations	Retrospective	CNNs (VGGNet)	Capsule endoscopy	7556 images	5760 images	Model 1 vs Model 2; 97.61% vs 95.07%	Model 1 vs Model 2; 96.04% vs 98.18%	Model 1 vs Model 2; 96.83%1 vs 96.62%1
Aoki et al[47]	Detecting erosions and ulcerations	Retrospective	CNNs (SSD)	Capsule endoscopy	5360 images	10440 images	88.2%	90.9%	90.8%1/0.958
Aoki et al[48]	Detecting erosions and ulcerations	Retrospective	CNNs (SSD)	Capsule endoscopy		20 videos
Ding et al[49]	Detecting small bowel diseases	Retrospective	CNNs (ResNet)	Capsule endoscopy	158235 images	5000 patients	99.88% per patient99.90% per lesion	100% per patient100 % per lesion
Fan et al[50]	Detecting erosions and ulcerations	Retrospective	CNNs (AlexNet)	Capsule endoscopy	Ulcer 2000; Erosion 2720	Ulcer 500; Erosion 690	Ulcer: 96.80%; Erosion: 93.67%	Ulcer: 94.79%; Erosion: 95.98%	Ulcer: 95.16%1; Erosion: 95.34%1/0.98
Leenhardt et al[51]	Detecting small bowel angiectasia	Retrospective	CNNs	Capsule endoscopy	300 videos with angiectasia	300 videos with angiectasia	100%	96%
Tsuboi et al[52]	Detecting small bowel angiectasia	Retrospective	CNNs (SSD)	Capsule endoscopy	141 patients	28 patients	98.8%	98.4%	0.998
Colonic benign diseases
Lui et al[34]	Detecting missed colonic lesions	Retrospective and prospective	R-FCN (ResNet101)	Endoscopic videos (WLI)	52 videos	Real-time AI detected at least 1 missed adenoma in 14 patients (26.9%) and increased the total number of adenomas detected by 23.6%.
Rodriguez-Diaz et al[53]	Histologically classifying CP	Retrospective	CAD	NBI	745 images +65000 images		96%	84%
Komeda et al[54]	Diagnosing CP	Retrospective	CNNs-CAD	WLI/NBI/ chromoendoscopy images	1200 images	10-fold cross validation			75.1%1
Akbari et al[55]	Classifying CP	Retrospective	FCNs	WLI images	200 images	300 images
Chen et al[56]	Classifying diminutive CP	Retrospective	DCNNs-CAD	NBI images	96 images + 188 images		96.3%	78.1%
Gong et al[57]	Detecting CA	Prospective	DCNNs	WLI images	DCNNs system (n = 355) or unassisted (control) colonoscopy (n = 349)		58 (16%) of 35527 (8%) of 349
Byrne et al[58]	Differentiating adenomatous and hyperplastic polyps	Retrospective	DCNNs	Videos and NBI images	223 polyp videos	40 videos	98%	83%
Mori et al[59]	Identifying diminutive CP	Prospective	CAD	NBI/stained images	791 consecutive patients undergoing colonoscopy and 23 endoscopists				Pathologic prediction rate of 98.1%1
Misawa et al[60]	DetectingCP	Retrospective	CAD	WLI images	105 positive and 306 negative videos	50 positive and 85 negative videos	90.0%	63.3%	76.5%1
Taunk et al[61]	Classifying polyp histology	Retrospective	CAD	pCLE images	125 images	189 images	95%	94%	94%1
Wang et al[62]	Detecting CA	Prospective	CAD	WLI images	484 patients in the CADe group and 478 in the sham group		165 (34%) of 484; 132 (28%) of 478
Tong et al[63]	Differentiating UC, CD, and ITB	Retrospective	CNNs/RF	WLI images	6399 consecutive patients (5128 UC, 875 CD and 396 ITB)		RF (UC 97%, CD 65%, and ITB 68%); CNN (UC 99%, CD 87%, and ITB 52%)	RF (UC 97%, CD 53%, and ITB 76%); CNN (UC 97%, CD 83%, and ITB 81%)	RF (UC 0.97, CD 0.58, and ITB 0.72); CNN (UC 0.98, CD 0.85, and ITB 0.63)
Ozawa et al[36]	Diagnosing UC	Retrospective	CAD	WLI images	26304 images	3981 images			0.86 (Mayo 0); 0.98 (Mayo 0–1)
Stidham et al[37]	Grading the severity of ulcerative colitis	Retrospective	CNNs	WLI images	2465 patients	308 patients	83.0%	96.0%	0.966
Maeda et al[38]	Identifying histologic inflammation associated with UC	Retrospective	CAD	Endocytoscopic images	87 patients	100 patients	74%	97%	91%1

¹Results accuracy. AAC: Acetic acid chromoendoscopy; AI: Artificial intelligence; AUC: Area under the curve; BE: Barrett’s esophagus; CA: Colorectal adenomas; CAD: Computer-aided diagnosis; CAG: Chronic atrophic gastritis; CD: Crohn’s disease; CNN: Convolutional neural network; CP: Colorectal polyp; DBE: Dysplastic Barrett’s esophagus; DCNNs: Deep convolutional neural networks; EV: Esophageal Varices; FCNs: Fully convolutional networks; GP: Gastric polyp; ITB: Intestinal tuberculosis; ME-NBI: Magnifying narrow-band imaging; ML: Machine learning; NBI: Narrow-band imaging; NDBE: Non-dysplastic Barrett’s esophagus; pCLE: Probe-based confocal laser endomicroscopy; PU: Peptic ulcer; RF: Random forest; R-FCNs: Region-based fully connected convolutional neural networks; SSD: Single shot detector; UC: Ulcerative colitis; WLI: White-light imaging.