Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions

Table 1 Summary of artificial intelligence studies on the detection and characterization of Barrett’s epithelium and associated neoplasia[8,29,30,33,34,37,39-42,45]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
de Groof et al[39]	Real-time Det + Ch of early BE neoplasia	Prospective study	1247 i	297 i	WLI	Hybrid: ResNet-UNet	Acc: 89%; Se: 90%; Sp: 88%
de Groof et al[40]	Real-time Det + Ch of early BE neoplasia	Prospective pilot study	1544 i	144 i	WLI	Hybrid: ResNet-UNet	Acc: 90%; Se 91%; Sp: 89%
Hashimoto et al[41]	Distinguish dysplastic from nondysplastic BE	Prospective pilot study	1374 i	458 i	WLI; NBI	Inception-ResNet-v2	Acc: 95%; Se: 96%; Sp: 94%
Ali et al[33]	BE risk stratification	Prospective pilot study	10000 i	194 v	WLI; NBI	ResNet-50; DeepLabv3 +	Acc: 98%
Hussein et al[45]	Det and delineation of BE dysplasia	Retrospective study	148936 v	276 v	WLI; i-scan	ResNet-101	Se: 91%; Sp: 79%; AUC: 0.93
Abdelrahim et al[37]	Det and localization of BE neoplasia	Prospective multicenter trial	1090171 i + v	471 i; 75 v	WLI	VGG16; SegNet	I: Acc/Se/Sp: 95%; v: Acc: 92%; Se: 95%; Sp: 91%
Tsai et al[30]	Det of BE	Retrospective study	771 i	160 i	NBI	EfficientNetV2B2	Acc: 94%; Se: 94%; Sp: 94%
Xin et al[8]	Det of early BE neoplasia	Retrospective multicenter	14046 i	400 i; 188 v		EfficientNet-Lite1; MobileNetV2; DeepLabv3 +	I: Se: 88%; Sp: 90%; v: Se: 79%; Sp: 94%
Meinikheim et al[34]	AI impact on nonexp BE neoplasia Det	Multicenter RCT	51273 i	96 v	WLI; NBI; Chromo	DeepLabv3; ResNet-50	Se: 70% to 78% w/AI; Sp: 67% to 73% w/AI
Jukema et al[42]	AI vs exp/nonexp in BE neoplasia Det	Prospective	3468 i	161 i; 161 v	NBI	EfficientNet-Lite1	Se: 84% to 96% w/AI; Sp: 90% to 98% w/AI
Jong et al[29]	Ensure AI reliability in BE neoplasia Det	Retrospective real word experience	1102 i; 12011 v	117 i	WLI; NBI	Hybrid: ResNet-50-vision transformer	Se: 85% (high-quality I); 62% (mod); 47% (low)

Det: Detection; Ch: Characterization; BE: Barrett’s epithelium; AI: Artificial intelligence; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; WLI: White light imaging; NBI: Narrow-band imaging; IEE: Image-enhanced endoscopy; Chromo: Chromoendoscopy with acetic acid and indigo carmine; ResNet: Residual network; UNet: U-shaped network; Deeplab: Deep convolutional neural networks for semantic image segmentation; SegNet: Segmentation network; VGG: Visual geometry group; EfficientNet: Efficient convolutional neural network; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; AUC: Area under the curve; mod: Moderate; w: With.

Table 2 Summary of artificial intelligence studies on the detection and characterization of premalignant and early esophageal squamous cell carcinoma[49-54,66,67,70]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
Horie et al[67]	Det of early ESCC	Retrospective	8428 i	1118 i	WLI; NBI	SSD	Acc/Se: 98%; NPV: 95%; PPV: 40%
Ohmori et al[70]	Det of early ESCC	Retrospective	11283 non-ME i; 11279 ME i	523 non-ME i; 204 ME i	WLI; NBI; BLI	SSD	Non-ME Acc: 79%; Se: 95%; Sp: 69%; ME: Acc: 77%; Se: 98%; Sp: 56%
Fukuda et al[66]	AI vs exp in early ESCC Det and Ch	Retrospective	17274 i	144 v	NBI; BLI	SSD	Det: Acc: 63%; Se: 91%; Sp: 51%; Ch: Acc: 88%; Se: 86%; Sp: 89%
Yang et al[52]	AI vs exp in Det of early ESCC	Retrospective	22994 i	222 i; 104 v	WLI; NBI; BLI	YOLOv3; ResNet-v2	Acc: 99%; Se: 100%; Sp: 99%
Yuan et al[51]	Prediction of ID in early ESCC	Retrospective multicenter	7094 i	1589 i	NBI	HRNet; OCRNet	Acc: 91%; ID prediction: 74%
Tani et al[50]	Real-time Det of early ESCC	Prospective single center trial	25048 i	237 i	WLI; NBI	ResNet-101	Acc: 81%; Se: 68%; Sp: 83%
Li et al[49]	Det of early ESCC and precancerous lesions	RCT	26543 i	3117 i	WLI; NBI	ENDOANGEL-ELD	Acc: 98%; Se: 90%; Sp: 98%
Ma et al[54]	Det + optical biopsy for early ESCC	Prospective	25056 i	2442 i; 187 v	WLI; pCLE	iCLE inception-ResNet-v2	Acc: 98%; Se: 95%; Sp: 99%
Aoyama et al[53]	AI vs exp/nonexp in early ESCC Det	Prospective	280 v	115 v	NBI	YOLOv3	Acc: 77%; Se: 76%; Sp: 79%

Det: Detection; Ch: Characterization; ESCC: Esophageal squamous cell carcinoma; AI: Artificial intelligence; ID: Invasion depth; IEE: Image-enhanced endoscopy; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; ME: Magnified endoscopy; WLI: White light imaging; NBI: Narrow-band imaging; BLI: Blue-laser imaging; pCLE: Probe-based confocal laser endomicroscopy; SSD: Single shot multi-box detector; YOLO: You only look once; ResNet: Residual network; HRNet: High-resolution network; OCRNet: Object-contextual representations network; ELD: Esophageal lesion detection; iCLE: Integrated confocal laser endomicroscopy; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; NPV: Negative predictive value; PPV: Positive predictive value.

Table 3 Summary of artificial intelligence studies on the detection and characterization of gastric premalignant conditions[81,83-90,92,94-100]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
Zhang et al[92]	Real-time Det of gastric polyps	RCT	708 i	50 i	WLI	SSD-GPNet	mAP: 90%; PDR by > 10%
Zhang et al[99]	Det of AG	Retrospective	3829 i	1641 i	WLI; i-scan	CNN-CAG	Acc: 94%; Se: 95%; Sp: 94%
Xu et al[85]	AI vs exp/nonexp in Det of GPMC	Retrospective multicenter	5198 i	1052 i; 98 v	ME-NBI; ME-BLI	ENDOANGEL	AG: Acc: 86% (i); 88% (v); GIM: Acc: 86% (i); 90% (v)
Lin et al[90]	Det of AG/GIM	Retrospective multicenter	2193 i	273 i	WLI	TResNet	AG: Acc: 96%; AUC: 0.98; Se: 96%; Sp: 96%; GIM: Acc: 98%; AUC: 0.99; Se: 98%; Sp: 97%
Watanabe et al[100]	Det of gastric indefinite for dysplasia lesions	Retrospective	2961 i	248 i	WLI	SSD + miR148a DNA methylation	AUC: 0.93 (exp) > 0.83 (AI + miR148a) > 0.59 (trainees)
Kodaka et al[95]	AG Det and OLGA staging in patients w/Helicobacter pylori infection	Retrospective	11497 i	7724 i	WLI	ResNet-50	AUC: 0.75 (AI + Kyoto) > 0.67 (AI + OLGA) > 0.66 (AI alone)
Zhao and Chi[98]	Severity classification of AGAI vs endoscopists	Prospective	2922 i	268 v	NBI	UNet	(Increase) DR of mod AG (16% vs 8%) and severe AG (7% vs 3%); (decrease) unnecessary Bx
Zhao et al[89]	Det of AGAI vs endoscopists	Prospective case-control	4175 i	676 v	NBI	UNet	Acc: 91% vs 72%; Se: 84% vs 63%; Sp: 97% vs 82%; AUC: 0.91 vs 0.74
Yang et al[81]	Det of AG/GIM	Retrospective	21420 i	5355 i	WLI; LCI	SE-ResNet	AG: Acc: 97%; Se: 99%; Sp: 95%; GIM: Acc: 99%; Se: 99%; Sp: 99%
Li et al[96]	Severity classification of GIM	Retrospective	837 i	278 i	NBI; LCI	CDCN	Acc: 84%
Shi et al[88]	Det of AG	Retrospective	6216 i	600 i; 118 v	WLI	GAM-EfficientNet	i: Acc: 94%; Se: 93%; Sp: 94%; v: Acc: 92%; Se: 96%; Sp: 89%
Iwaya et al[87]	Det of GIM and OLGIM staging	Retrospective	5753 i	1150 i	HE slides	ResNet-50	Se: 98%; Sp: 95%; OLGIM stage III/IV classified in 18%
Fang et al[86]	AI vs pathologists in Det and grading of AG/GIM	Prospective multicenter	1745 i	545 i	Pathology slides	GasMIL	(Increase) pathologists’ performance (AUC: 0.95 vs 0.88)
Tao et al[97]	AG Det and risk stratification vs exp	Retrospective	5856 i	869 i; 119 v	WLI	UNet ++ ResNet-50 ENDOANGEL	(Increase) Se vs exp i: 93% vs 77%; v: 95% vs 86%
Niu et al[94]	GIM grading and OLGIM staging	Retrospective multicenter	470 i	333 i	ME-NBI	Faster R-CNN	Pred high-risk stage Acc: 84%
Zou et al[83]	Det of Helicobacter pylori infection AI vs endoscopists	Multicenter RCT	7377 i	2080 i	WLI	EfficientNet	Acc: 93% vs 76%; Se: 92% vs 79%; Sp: 93% vs 75%
Xu et al[84]	AI vs exp/nonexp in Det of AG/GIM	Single center RCT	NA	1968 v	WLI	ENDOANGEL	(Increase) DR of AG: 23% vs 17%; (Increase) DR of GIM: 14% vs 9%

AI: Artificial intelligence; IEE: Image-enhanced endoscopy; Det: Detection; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; AG: Atrophic gastritis; GPMC: Gastric premalignant conditions; GIM: Gastric intestinal metaplasia; OLGA: Operative link on atrophic gastritis assessment; AGAI: Atrophic gastritis artificial intelligence; OLGIM: Operative link for gastric intestinal metaplasia assessment; w: With; WLI: White light imaging; NBI: Narrow-band imaging; BLI: Blue-laser imaging; ME: Magnified endoscopy; HE: Hematoxylin-eosin; SSD: Single shot multi-box detector; GPNet: Gated pyramid network; CNN: Convolutional neural networks; CAG: Chronic atrophic gastritis; TResNet: Transformer-residual network; ResNet: Residual network; UNet: U-shaped network; R-CNN: Region-based convolutional neural networks; mAP: Mean average precision; PDR: Polyp detection rate; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; AUC: Area under the curve; DR: Detection rate; Bx: Biopsies; mod: Moderate; LCI: Linked-color imaging; CDCN: Color-guided deformable convolution network; Pred: Prediction; NA: Not applicable.

Table 4 Summary of artificial intelligence studies on the detection and characterization of early gastric cancer[101-108,110,112-117,119,122]

Ref.	Purpose	Design	Training set	Test set	IEE	AI model	Performance
Zhu et al[122]	ID prediction of EGC	Retrospective	790 i	203 i	WLI	ResNet-50	Acc: 89%; Se: 76%; Sp: 96%; AUC: 0.94
Horiuchi et al[112]	AI vs exp in EGC Det	Retrospective	2570 i	174 v	ME-NBI	GoogLeNet	Acc: 85%; Se: 95%; Sp: 71%; AUC: 0.87
Wu et al[108]	Det of EGC	Multicenter RCT	NA	1050 v	WLI	ENDOANGEL	Acc: 85%; Se: 100%; Sp: 84%
Wu et al[107]	Det of EGC	RCT	NA	1812 v	WLI	ENDOANGEL-LD	(Decrease) miss rate (AI 6% vs endoscopists 27% RR = 0.22)
Wu et al[117]	AI vs exp in ID and DS of EGC	Prospective multicenter	1131 i	100 v	ME-NBI	ENDOANGEL	ID: Acc: 79% vs 64%; DS: Acc: 71% vs 64%
Wu et al[116]	Real time Det of EGC	Prospective single center trial	9824 i	2010 v	WLI	ENDOANGEL-LD	Acc: 92%; Se: 92%; Sp: 92%; PPV: 25%; NPV: 100%
Ueyama et al[103]	Det of EGC	Retrospective	5574 i	2300 i	ME-NBI	ResNet-50	Acc: 99%; Se: 99%; Sp: 98%
He et al[115]	Det of EGC	Retrospective multicenter	4667 i	4702 i; 187 v	ME-NBI	ENDOANGEL-ME	Acc: 90%; Se: 93%; Sp: 94%
Li et al[110]	AI vs exp in EGC Det	Retrospective	1630 i	267 i; 77 v	ME-NBI	ENDOANGEL-LA	i: Acc: 89%; Se: 86%; Sp: 92%; v: Acc: 87%; Se: 84%; Sp: 88%
Tang et al[114]	Det of EGC	Retrospective multicenter	13151 i	1577 i; 20 v	NBI	YOLOv3	Acc: 93%; AUC: 0.95
Jin et al[113]	Det of EGC AI vs exp	Prospective	5708 i	1425 i; 10 v	WLI; NBI	Mask R-CNN	Acc: 90%; Se: 91%; Sp: 89%
Gong et al[106]	Real-time Det and ID prediction of EGC	RCT	5017 i	2524 v	WLI	CDSS	DR: 96%; ID Acc: 86%; lesion class: Acc: 82%
Lee et al[101]	EGC pathological Ch	Retrospective	4336 i; 153 v	436 i; 89 v	WLI	ENAD CAD-G	Acc: UH: 90%; SMI: 88%; LVI: 88%; LNM: 93%
Chang et al[119]	Classification of EGC	Retrospective real-world data	21918 i	6785 i; 296 v	WLI	ENAD CAD-G	i: Acc: EGC: 82%; dysplasia: 88%; v: Acc: EGC: 88%; dysplasia: 91%
Zhao et al[102]	Det of EGC LCI vs WLI	Retrospective	9021 i	116 v	WLI; LCI	CADe	(Increase) Se: LCI: 94% vs WLI: 79%; Sp: 93% in both
Lee et al[101]	Det of EGC	Retrospective	30000 i	500 i	WLI	CADe (ALPHAON®)	Acc: 88%; Se: 93%; Sp: 87%; AUC: 0.96
Soong et al[105]	Raman spectroscopy for EGC risk strat	RCT	NA	25 v	WLI	SPECTRA IMDx™	Acc: 100%; Se: 80%; Sp: 92%
Feng et al[104]	AI vs exp/nonexp in EGC Det	Prospective	12000 i	1289 i; 130 v	WLI	DCNN	Se: 97%; Sp: 89%; AUC: 0.93

EGC: Early-stage gastric cancer; ID: Invasion depth; AI: Artificial intelligence; IEE: Image-enhanced endoscopy; Det: Detection; exp: Expert endoscopists; nonexp: Nonexpert endoscopists; RCT: Randomized controlled trial; i: Images; v: Videos; DS: Differentiation status; Ch: Characterization; LCI: Linked-color imaging; WLI: White light imaging; strat: Stratification; NA: Not applicable; NBI: Narrow-band imaging; ME: Magnified endoscopy; ResNet: Residual network; GoogLeNet: Google network; LD: Lesion detection; LA: Logical anthropomorphic diagnostic system; YOLO: You only look once; R-CNN: Region-based convolutional neural networks; CDSS: Clinical decision support system; ENAD: Endoscopy neural assisted detection; CAD-G: Computer-aided detection for gastric lesions; CADe: Computer aided detection; DCNN: Deep convolutional neural network; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; AUC: Area under the curve; RR: Relative risk; NPV: Negative predictive value; PPV: Positive predictive value; UH: Undifferentiated histology; SMI: Submucosal invasion; LVI: Lympho vascular invasion; LNM: Lymph node metastasis.

Table 5 Summary of artificial intelligence studies on the detection of polyps and protruding lesions by video capsule endoscopy or small bowel endoscopy[135,138-140,213-221]

Ref.	Endoscopy type	Study design	Training set	Test set	AI model	Performance
Barbosa et al[135]	VCE	Retrospective	104 tumors, 100 normal	700 tumors, 2300 normal	CNN	Se: 94%; Sp: 93%
Li and Meng[213]	VCE	Retrospective	540 tumors, 540 normal	60 tumors, 60 normal	ML (SVM)	Acc: 84%; Se: 82%; Sp: 8%
Constantinescu et al[138]	VCE	Prospective	54 videos	90 images (32 polyps, 58 normal)	ANN	Acc: 98%; Se: 94%; Sp: 91%
Liu et al[214]	VCE	Retrospective	1800 (105 patients, 89 videos)	89 videos (15 tumors, 74 normal)	ML (SVM)	Se: 98%; Sp: 97%
Faghih Dinevari et al[215]	VCE	Retrospective	300 tumors, 300 normal	100 tumors, 100 normal	ML (SVM)	Acc: 94%; Se: 94%; Sp: 93%
Yuan and Meng[216]	VCE	Retrospective	1000 polyps, 3000 normal	200 tumors, 600 normal	DL	Acc: 98%
Ding et al[217]	VCE	Retrospective	158, 235	113, 268, 334	CNN	Se: PPA: 99.9%; PLA: 99.9%; Sp: PPA and PLA: 100%
Saito et al[218]	VCE	Retrospective	30, 584	17507 (7507 protruding lesions)	CNN	Se: 91%; Sp: 80%
Xie et al[219]	VCE	Retrospective	148, 357, 922	146, 956, 145	CNN	Se: 99%
Cardoso et al[139]	Enteroscopy	Retrospective	6340	507 protruding lesions, 1078 normal	CNN	Acc: 97%; Se: 97%; Sp: 97%
Inoue et al[220]	EGD	Retrospective	531	1080	CNN	Se: 95%; Sp: 87%
Ding et al[221]	VCE	Retrospective	280, 426	240 videos	CNN	Acc: 81%; Se: 96%; Sp: 98%
Zhu et al[140]	DBE	Retrospective	8222	3148	CNN	Det model: Se: 92%; Sp: 93%; Class model: Se: 80%-93%; Acc: 86%

AI: Artificial intelligence; VCE: Video capsule endoscopy; EGD: Esophagogastroduodenoscopy; DBE: Double balloon enteroscopy; CNN: Convolutional neural network; ML: Machine learning; SVM: Support vector machine; ANN: Artificial neural network; DL: Deep learning; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; PPA: Per patient analysis; PLA: Per lesion analysis.

Table 6 Summary of retrospective and prospective studies on computer aided detection for detecting colorectal polyps[150,222-229]

Ref.	Study design	Number	Country	Outcomes
Park et al[222]	Retrospective ex vivo	562 images	United States	Se: 86%; Sp: 85%
Billah et al[223]	Retrospective ex vivo	14000 images	Bangladesh	Se: 99%; Sp: 99%
Lequan et al[224]	Retrospective ex vivo	38 videos	China	Se: 71%; PPV: 88%
Zhang et al[225]	Retrospective ex vivo	2442 images	China	Se: 98%; PPV: 99%; Acc: 86%
Misawa et al[226]	Retrospective ex vivo	546 videos	Japan	Per-frame Se: 90%; Sp: 63%; Acc: 76%; Per-polyp Se: 94%
Urban et al[150]	Retrospective ex vivo	63559 images, 40 videos	United States	Se: 90%; Acc: 96%
Yamada et al[227]	Retrospective ex vivo	144823 video images	Japan	Se: 97%; Sp: 99%
Klare et al[228]	Prospective in vivo	55 colonoscopies	Germany	Per-polyp Se: 75%; ADR: 29%; PDR: 51% (31% and 56% in endoscopist)
Ozawa et al[229]	Ex vivo	23495 images	Japan	Se: 92%; PPV: 93%; Acc: 85%

Acc: Accuracy; Se: Sensitivity; Sp: Specificity; PPV: Positive predictive value; ADR: Adenoma detection rate; PDR: Polyp detection rate.

Table 7 Summary of randomized controlled trials on computer aided detection for the detection of colorectal polyps[151-159,166,175-178,230-235]

Ref.	Study design	Number of patients	Country	Outcomes (AI vs control)
Wang et al[151]	Single center RCT	1058	China	ADR: 29% vs 20%
Wang et al[156]	Single center RCT (double blind)	962	China	ADR: 34% vs 28%
Gong et al[157]	Single center RCT	704	China	ADR: 16% vs 8%
Su et al[158]	Single center RCT	659	China	ADR: 29% vs 17%
Liu et al[159]	RCT	1026	China	ADR: 39% vs 23%
Wang et al[175]	Tandem RCT	369	China	AMR: 14% vs 40%
Repici et al[152]	Multicenter RCT	685	Italy	ADR: 55% vs 40%
Kamba et al[176]	Tandem RCT	358	Japan	AMR: 14% vs 37%
Glissen Brown et al[177]	Multicenter tandem RCT	223	United States	AMR: 20% vs 31%
Repici et al[153]	RCT	660	Italy, Switzerland	ADR: 53% vs 45%
Wallace et al[178]	Tandem RCT	240	Italy, United Kingdom, United States	AMR: 16% vs 32%
Shaukat et al[166]	RCT	1359	United States	APC: 1 vs 0.8
Xu et al[230]	Multicenter RCT	3059	China	ADR: 40% vs 32%
Gimeno-García et al[231]	Single center RCT	370	Spain	ADR: 55% vs 41%
Nakashima et al[154]	Single center RCT	415	Japan	ADR: 59% vs 48%
Karsenti et al[232]	Single center RCT	2592	France	ADR: 38% vs 34%
Lau et al[155]	Single center RCT	766	China	ADR: 58% vs 45%
Maas et al[233]	Multicenter RCT	916	Germany, Netherlands, United States, Israel	ADR: AI 37% vs 30%
Thiruvengadam et al[234]	Single center RCT	1100	United States	ADR: 43% vs 34%
Park et al[235]	Multicenter RCT	805	Korea	ADR: 35% vs 28%

AI: Artificial intelligence; ADR: Adenoma detection rate; AMR: Adenoma miss rate; APC: Adenoma per colonoscopy; RCT: Randomized controlled trial.

Table 8 Summary of retrospective studies on computer-aided diagnosis for characterization of colorectal polyps[185,188-192,195,196,229,236-238]

Ref.	Country	AI system	Number	WLI/IEE	Primary outcomes	Results
Tischendorf et al[236]	Germany	SVM	209 polyps	Magnifying NBI	Adenomas vs non adenomas	Se: 90%; Sp: 70%; Acc: 85%
Takemura et al[237]	Japan	SVM	1519 polyps, 371 images	Magnifying NBI	Neoplastic vs non neoplastic	Se: 97%; Sp: 97%; Acc: 97%
Misawa et al[195]	Japan	EndoBRAIN	1179 images	Endocytoscopy, NBI	Prediction of histology	Se: 84%; Sp: 97%; Acc: 90%; PPV: 98%; NPV: 82%
Komeda et al[188]	Japan	CNN	1800 images, 10 videos	WLI	Adenomas vs non adenomas	Acc: 75%
Byrne et al[185]	Canada	DCNN	117000 images, 388 videos	NBI	Adenomas vs non adenomas (DP)	Se: 98%; Sp: 83%; PPV: 90%; NPV: 97%; Acc: 94%
Chen et al[238]	China	DCNN	2441 images	NBI	Neoplastic vs hyperplastic (DP)	Se: 96%; Sp: 78%; PPV: 89%; NPV: 90%; Acc: 90%
Sánchez-Montes et al[189]	Spain	ML	225 polyps	HD WLI	Adenomas vs non adenomas (DP)	Se: 92%; NPV: 91%; Acc: 90%; DP: NPV: 96%; Acc: 87%
Song et al[190]	Korea	ENAD	12480 images, 451 polyps	NBI	Adenomas vs SSLs	Se: 82% vs 84%; Sp: 93% vs 88%; Acc: 81% vs 82%
Kudo et al[196]	Japan	EndoBRAIN-EYE	69142 images	Endocytoscopy, NBI	Neoplastic vs non neoplastic	Se: 96%; Sp: 94%; PPV: 96%; NPV: 94%; Acc: 96%
Zachariah et al[192]	United States	CAD-EYE	5912 images	WLI, NBI	Diminutive adenomas vs SSLs/hyperplastic polyps	Se: 96%; Sp: 90%; PPV: 94%; NPV: 93%; Acc: 94%
Jin et al[191]	Korea	DCNN	2450 polyps	NBI	Adenomas vs non adenomas (DP)	Se: 83%; Sp: 91%; Acc: 87%
Ozawa et al[229]	Japan	DCNN (SSD)	27508 images	WLI, NBI	Adenomas vs non adenomas (DP)	WLI: NPV: 85%; NBI: NPV: 91%

AI: Artificial intelligence; IEE: Image-enhanced endoscopy; WLI: White light imaging; CNN: Convolutional neural network; SVM: Support vector machine; DCNN: Deep convolutional neural network; ML: Machine learning; ENAD: Endoscopy neural assisted detection; CAD: Computer assisted device; SSD: Single shot multi-box detector; NBI: Narrow band imaging; HD: High definition; DP: Diminutive polyps; SSL: Sessile serrated lesion; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.

Table 9 Summary of prospective studies on computer-aided diagnosis for the characterization of colorectal polyps[194,200,201,208,209,239-243]

Ref.	Country	AI system	Number	WLI/IEE	Primary outcomes	Results
Gross et al[239]	Germany	SVM	434 small polyps (< 10 mm)	Magnifying NBI	Adenomas vs non adenomas (small polyps)	Se: 95%; Sp: 90%; PPV: 93%; NPV: 92%; Acc: 93%
Kominami et al[240]	Japan	SVM	1262 polyps, 118 images	Magnifying NBI	Adenomas vs non adenomas (small polyps)	Se: 93%; Sp: 95%; PPV: 95%; NPV: 93%; Acc: 97%
Mori et al[194]	Japan	EndoBRAIN	61952 images, 466 polyps	Endocytoscopy, NBI	Adenomas vs non adenomas (DP)	Se: 95%; Sp: 92%; PPV: 96%; NPV: 96%; Acc: 98%
Barua et al[208]	Norway, United Kingdom, Japan	EndoBRAIN	892 polyps	WLI, NBI, magnification	Neoplastic vs non neoplastic polyps (DP)	Se: 90%; Sp: 86%
Hassan et al[242]	Italy	GI genius	544 polyps	NBI, BLI	Adenomas vs non adenomas (DP)	NPV: 98%; Se: 82%; Sp: 93%; Acc: 92%
Minegishi et al[200]	Japan	EndoBRAIN	395 polyps	NBI	Adenomas vs non adenomas (DP)	NPV: 94%; Se: 94%; Sp: 63%; Acc: 86%
Rondonotti et al[209]	Italy	CAD EYE	596 polyps	WLI	Adenomas vs non adenomas (DP)	NPV: 91%; Se: 89%; Sp: 88%; Acc: 88%
Li et al[241]	Singapore (multicenter)	CAD EYE	661 polyps	WLI	Neoplastic vs non neoplastic polyps	Se: 62%; Acc: 72%
Hassan et al[242]	Italy	CAD EYE; GI genius (head-to-head comparison)	319 polyps	WLI, BLI	Adenomas vs non adenomas (DP)	NPV: 97% vs 98%; Se: 82% vs 86%; Sp: 92% vs 94%
Houwen et al[201]	Netherlands (multicenter) + Spain (1 center)	POLAR	423 polyps	NBI	Neoplastic (adenomas and SSLs) vs non neoplastic	Se: 89%; Sp: 38%; Acc: 79%
Rex et al[243]	United States (multicenter)	GI genius	2695 polyps	WLI, NBI	Adenomas vs non adenomas	Se: 91%; Sp: 65%

IEE: Image-enhanced endoscopy; SVM: Support vector machine; CAD: Computer assisted device; GI: Gastrointestinal; WLI: White light imaging; NBI: Narrow-band imaging; BLI: Blue-laser imaging; DP: Diminutive polyps; SSL: Sessile serrated lesion; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.