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.