Artificial intelligence in gastroenterology: A narrative review

Table 1 Overview of findings from studies evaluating the detection accuracy of computer-aided detection for Barrett’s esophagus-related neoplasia

Ref.	Country	Study design	AI Classifier	Lesions	Training dataset	Test dataset	Sensitivity (%)	Specificity (%)	Accuracy (%)	AUROC
Swager et al[11], 2017	Netherlands	Retrospective	ML2 methods	NPL	-	60 VLE images	90	93	-	0.95
van der Sommen et al[10], 2016	Netherlands	Retrospective	SVM	NPL	-	100 WLE images	83	83	-	-
Hong et al[17], 2017	South Korea	Retrospective	CNN	NPL, IM, GM	236 endomicroscopy images	26 endomicroscopy images	-	-	80.77	-
de Groof et al[13], 2019	Netherlands, Germany, Belgium	Prospective	SVM	NPL	-	60 WLE images	95	85	91.7	0.92
Ebigbo et al[21], 2019	Germany, Brazil	Retrospective	CNN	EAC	Augsburg dataset: 148 WLE images and NBI; MICCAI dataset: 100 WLE images		97; 94a; 92	88; 80a; 100	-	-
Ghatwary et al[24], 2019	England, Egypt	Retrospective	Multiple CNNs	EAC	Images from 21 patients	Images from 9 patients	96	92	-	-
de Groof et al[14], 2020	Netherlands, France, Sweden, Germany, Belgium, Australia	Ambispective	CNN	NPL	Dataset 1: 494364 images; Dataset 2:1; 247 images; Dataset 3: 297 images	Dataset 3: 297 images; Dataset 4: 80 images; Dataset 5: 80 images	90b	87.5b	88.8b	-
de Groof et al[15], 2020	Netherlands, Belgium	Prospective	CNN	NPL	495611 images	20 patients; 144 WLE images	75.8	86.5	84	-
Ebigbo et al[18], 2020	Germany, Brazil	Prospective	CNN	EAC	129 images	62 images	83.7	100	89.9	-
Hashimoto et al[19], 2020	United States	Retrospective	CNN	NPL	1374 images	458 images	96.4	94.2	95.4	-
Struyvenberg et al[12], 2020	Netherlands	Prospective	ML2 methods	NPL	-	3060 VLE frames	-	-	-	0.91
Iwagami et al[25], 2021	Japan	Retrospective	CNN	EJC	3443 images	232 images	94	42	66	-
Struyvenberg et al[16], 2021	Netherlands, Sweden, Belgium	Retrospective	CNN	NPL	495611 images	157 NBI zoom videos; 30021 frames	851; 75	831; 90	831; 85	-
Hussein et al[20], 2022	England, Spain, Belgium, Austria	Prospective	CNN	DPL	148936 frames	264 iscan-1 images	91	79	-	0.93

^aSensitivity and specificity reported by white light endoscopy images from the Augsburg dataset, narrow band images from the Augsburg dataset, and from the MICCAI dataset respectively.

^bResults found from convolutional neural network analyzing dataset 4.

¹Sensitivity, specificity and accuracy obtained from per-video analysis and from per-frame analysis respectively.

²Multiple machine learning (ML) methods tested. Results from best performing ML method reported.

AI: Artificial intelligence; AUROC: Area under the receiver operating characteristic; CNN: Convolutional neural network; DPL: Dysplasia; EAC: Esophageal adenocarcinoma; EJC: Esophagogastric junctional adenocarcinoma; GM: Gastric metaplasia; IM: Intestinal metaplasia; ML: Machine learning; NBI: Narrow band images; NPL: Neoplasia; SVM: Support vector machine; VLE: Volumetric laser endomicroscopy; WLE: White light endoscopy.

Table 2 Overview of findings from studies evaluating the detection accuracy of computer-aided detection for gastric cancer

Ref.	Country	Study design	AI classifier	Lesions	Training dataset	Test dataset	Sensitivity (%)	Specificity (%)	Accuracy (%)	AUROC
Miyaki et al[47], 2013	Japan	Prospectivea	SVM	Gastric cancer	493 FICE-derived magnifying endoscopic images	92 FICE-derived magnifying endoscopic images	84.8	97	85.9	-
Kanesaka et al[48], 2018	Japan, Taiwan	Retrospective	SVM	EGC	126 M-NBI images	81 M-NBI images	96.7	95	96.3	-
Wu et al[50], 2019	China	Retrospective	CNN	EGC	9151 images	200 images	94	91	92.5	-
Cho et al[51], 2019	South Korea	Ambispective	CNN	Advanced gastric cancer, EGC, high grade dysplasia, low grade dysplasia, non-neoplasm	4205 WLE images	812 WLE images; 200 WLE images	-	-	86.6b; 76.4	0.877b
Tang et al[49], 2020	China	Retrospective	CNN	EGC	35823 WLE images	Internal: 9417 WLE images; External: 1514 WLE images1	95.51; 85.9-92.1	81.71; 84.4-90.3	87.81; 85.1-91.2	0.941; 0.887-0.925
Namikawa et al[52], 2020	Japan	Retrospective	CNN	Gastric cancer	18410 images	1459 images	99	93.3	99	-
Horiuchi et al[56], 2020	Japan	Retrospective	CNN	EGC	2570 M-NBI images	258 M-NBI images	95.4	71	85.3	0.852
Horiuchi et al[57], 2020	Japan	Retrospective	CNN	EGC	2570 M-NBI images	174 videos	87.4	82.8	85.1	0.8684
Guo et al[54], 2021	China	Retrospective	CNN	Gastric cancer, erosions/ulcers, polyps, varices	293162 WLE images	33959 WLE images	67.52; 85.1	70.92; 90.3	-	-
Ikenoyama et al[55], 2021	Japan	Retrospective	CNN	EGC	13584 WLE and NBI images	2940 WLE and NBI images	58.4	87.3	-	-
Hu et al[58], 2021	China	Retrospective	CNN	EGC	M-NBI images from 170 patients	Internal: M-NBI from 73 patients External: M-NBI images from 52 patients	79.23; 78.2	74.53; 74.1	773; 76.3	0.8083; 0.813
Ueyama et al[59], 2021	Japan	Retrospective	CNN	EGC	5574 M-NBI images	2300 M-NBI	98	100	98.7	-
Yuan et al[53], 2022	China	Retrospective	CNN	EGC, advanced gastric cancer, submucosal tumor, polyp, peptic ulcer, erosion, and lesion-free gastric mucosa	29809 WLE images	1579 WLE images	59.24; 100	99.34; 98.1	93.54; 98.4	-

^aPresumed prospective based on manuscript.

^bAccuracy of convolutional neural network (CNN) for detecting the five different lesions and detecting gastric cancer respectively. Area under the receiver operating characteristic (AUROC) pertains to detecting gastric cancer.

¹The external dataset was comprised of images from 3 external sites. Sensitivity, specificity, accuracy and AUROC for the internal dataset and external dataset respectively.

²Sensitivity and specificity of CNN for detecting gastric cancers in a dataset comprised of images without annotations and for detecting gastric cancers in a dataset comprised of annotated images respectively.

³Sensitivity, specificity, accuracy and AUROC for the internal dataset and external dataset respectively.

⁴Sensitivity, specificity and accuracy for detecting early gastric cancer and for detecting advanced gastric cancer respectively.

“Internal” and “External” refer to internal and external datasets respectively.

CNN: Convolutional neural network; AUROC: Area under the receiver operating characteristic; EGC: Early gastric cancer; FICE: Flexible spectral imaging color enhancement; M-NBI: Magnifying endoscopy with narrow band imaging; NBI: Narrow band imaging; SVM: Support vector machine; WLE: White light endoscopy.

Table 3 Overview of findings from studies evaluating the detection accuracy of computer-aided detection for colonic polyps

Ref.	Country	Study design	Lesions	Training dataset	Test dataset	Sensitivity (%)	Specificity (%)	Accuracy (%)	AUROC
Komeda et al[139], 2017	Japan	Retrospective	Adenomas	1200 images	10 images	80	60	70	-
Misawa et al[140], 2018	Japan	Retrospective	Polyps	411 video clips	135 video clips	90	63.3	76.5	0.87
Wang et al[149], 2018	China, United States	Retrospective	Polyps	4495 images	Dataset A: 27113 images; Dataset C: 138 video clips; Dataset D: 54 full-length videos	Dataset A: 94.38; Dataset C: 91.64	Dataset A: 95.92; Dataset D: 95.4	-	Dataset A: 0.984
Horiuchi et al[154], 2019	Japan	Prospective	Diminutive polyps	-	a	80	95.3	91.5	-
Hassan et al[141], 2020	Italy, United States	Retrospective	Polyps	-	338 video clips	99.7	-	-	-
Guo et al[142], 2021	Japan	Retrospective	Polyps	1991 images	100 video clips; 15 full videos	87b	98.3b	-	-
Neumann et al[143], 2021	Germany	Retrospective1	Polyps	> 500 videos	240 polyps within full-length videos	100	0	-	-
Li et al[144], 2021	Singapore	Retrospective	Polyps	6038 images	2571 images	74.1	85.1	-	-
Livovsky et al[151], 2021	Israel	Ambispective	Polyps	3611 h of videos	1393 h of videos	97.1	0	-	-
Pfeifer et al[158], 2021	Germany, Italy, Netherlands	Retrospective	Polyps	10467 images	45 videos	90	80	-	0.92
Ahmad et al[145], 20222	England	Prospective	Polyps	Dataset A: 58849 frames; Dataset B: 10993 videos and still images	Dataset C: 110985 frames; Dataset D: 8950 frames; Dataset E: 542484 frames	Dataset C: 100, 84.1; Dataset D&E: 98.9, 85.2	Dataset C: 79.6; Dataset D&E: 79.3%
Hori et al[146], 2022	Japan	Prospective	Polyps	1456 images	600 images	97	97.7	97.3	-
Pacal et al[152], 2022	Turkey	Retrospective	Polyps	Used images from 3 publicly available datasets (SUN, PICCOLO, Etis-Larib) to create training and test datasets		91.04	-	-	-
Yoon et al[184], 2022	South Korea	Retrospective	SSL	4397 images	Validation Set 2106; SSL Temporal Validation set 133	95.44; 93.89	90.1	92.95	0.96
Nemoto et al[185], 2022	Japan	Retrospective	TA, SSL	1849 images	400 images	72	89	82	0.86
Lux et al[148], 2022	Germany	Retrospective	Polyps	506338 images	41 full-length videos	-	-	95.3	-

^aTested CADe in a cohort of 95 patients.

^bPer-frame analysis from full-length video dataset.

¹Presumed retrospective based on manuscript.

²Sensitivity is reported as per-polyp and per-frame respectively. Specificity is reported as per-frame.

AUROC: Area under the receiver operating characteristic; SSL: Sessile serrate lesion; TA: Tubular adenoma. All studies used a convolutional neural network to classify images.

Table 4 Overview of findings from studies evaluating computer-aided detection for adenoma detection rate and polyp detection rate

Ref.	Country	Study design	Patients (n)		PDR (%)			ADR (%)
			CADe	SC	CADe	SC	P value	CADe	SC	P value
Wang et al[168], 2019	China, United States	Randomized	522	536	45.02	29.1	< 0.001	29.12	20.34	< 0.001
Becq et al[155], 2020	United States, Turkey, Costa Rica	Prospective	50b		82	62	Not reported	-	-	-
Gong et al[166], 2020	China	Randomized	355	349	47	34	0.0016	16	8	0.001
Liu et al[171], 2020	China, United States	Randomized	393	397	47.07	33.25	< 0.001	29.01	20.91	0.009
Liu et al[173], 2020	China	Prospective	508	518	43.65	27.81	< 0.001	39.1	23.89	< 0.001
Repici et al[170], 2020	Italy, Kuwait, United States, Germany	Randomized	341	344	-	-	-	54.8	40.4	< 0.001
Su et al[169], 2020	China	Randomized	308	315	38.3	25.4	0.001	28.9	16.5	< 0.001
Wang et al[156], 2020	China, United States	Prospective, Tandem1	184	185	65.59	55.14	0.099	42.39	35.68	0.186
Wang et al[167], 2020	China, United States	Randomized	484	478	52	37	< 0.0001	34	28	0.03
Kamba et al[164], 2021	Japan	Randomized, Tandem2	172	174	69.8	60.9	0.084	64.5	53.6	0.036
Luo et al[174], 2021	China	Randomized, Tandem1	72	78	38.7	34	< 0.001	-	-	-
Pfeifer et al[158], 2021	Germany, Italy, Netherlands	Prospective, Tandem1	42b		50	38	0.023	36	26	0.044
Shaukat et al[157], 2021	United States, England	Prospective	83	283	-	-	-	54.2	40.6	0.028
Shen et al[150], 2021	China	Ambispective	64	64	78.1	56.3	0.008	53.1	29.7	0.007
Xu et al[172], 2021	China	Randomized	1177	1175	38.8	36.2	0.183	-	-	-
Glissen Brown et al[163], 2022	China, United States	Randomized, Tandem2	113	110	70.8	65.45	0.3923	50.44	43.64	0.3091
Ishiyama et al[159], 2022	Japan, Norway	Prospective	918	918	59	52.1	0.003	26.4	19.9	0.001
Lux et al[148], 2022	Germany	Retrospective	41	-	-	-	-	-	41.5	-
Quan et al[153], 2022	United States	Prospective	300	300	-	-	-	43.7a; 66.7	37.8a; 59.72	0.37a; 0.35
Repici et al[165], 2022	Italy, Switzerland, United States, Germany	Randomized	330	330	-	-	-	53.3	44.5	0.017
Shaukat et al[162], 2022	United States	Randomized	682	677	64.4	61.2	0.242	47.8	43.9	0.065
Zippelius et al[160], 2022	Germany, United States	Prospective	150b		-	-	-	50.7	52	0.5

^aQuan et al[153] reported results by indication, screening and surveillance respectively.

^bThe same patients were used to compare CADe versus standard colonoscopy; Becq et al[155] recorded 50 colonoscopy videos that were analyzed by CADe and reviewed by endoscopists separately; Pfeifer et al[158] performed standard colonoscopy followed by CADe-assisted colonoscopy in all 42 patients; Zippelius et al[160] had their CADe analyze their patients while the endoscopists performed their colonoscopies.

¹Performed analyses using data obtained from whole process.

²Performed analyses using data obtained from first pass.

ADR: Adenoma detection rate; CADe: Computer-aided detection; PDR: Polyp detection rate; SC: Standard colonoscopy. All studies used a convolutional neural network to classify images.