Artificial intelligence in Barrett’s esophagus: A renaissance but not a reformation

Table 1 Computer-aided diagnosis of Barrett’s esophagus

Ref.	Year	Study design	Lesions	Imaging modality	Image qualification	Teaching dataset	Validation method	Outcomes	Compared to expert/current standard
van der Sommen et al[11]	2016	Retrospective	HGD, early EAC	WLI	High quality, clear visible/absence of lesions	100 images	LOO	Per-image SPEC/SENS: 83%/83%; Per-patient SPEC/SENS: 86%/87%	Inferior
de Groof et al[12]	2019	Retrospective	Non-dysplastic and dysplastic BE	WLI	1280 × 1024 pixels – HD	60 images	LOO	Accuracy: 0.92; SENS: 0.95; SPEC: 0.85	NA
Swager et al[13]	2017	Retrospective	HGD, early EAC	VLE	High quality image database	60 images	LOO	AUC: 0.95, 0.89, 0.91	Superior
Ebigbo et al[15]	2020	Prospective	Early EAC	WLI	1350 × 1080 pixels and 1600 × 1200 pixels – HD	129 images	LOO	Accuracy: 0.899; SENS: 0.837; SPEC: 1.00	NA

AUC: Area under the curve; BE: Barrett’s esophagus; EAC: Esophageal adenocarcinoma; HD: High definition; HGD: High-grade dysplasia; LOO: LEAVE-one-out; NA: Not available; SENS: Sensitivity; SPEC: Specificity; VLE: Volumetric laser endomicroscopy; WLI: White light imaging.