Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis

Table 5 Characteristics of the studies about diagnosis of invasion depth of esophageal cancers

Ref.	Format	Scale	Continent	Depth	Architecture of CNN	Image type	Histological type	Real-time	External validation	Quality	Endoscopist control	Patients training set	Images training set	Patients test set	Images test set	TP	FP	FN	TN
Horie et al[29], 2019	Retrospective	Unicenter	Asia	T1a, T1b vs T2-4	SSD	WLI/NBI	ESCC/EAC	Yes	No	High	No	384	8428	NM	168	142	2	1	23
Nakagawa et al[31], 2019	Retrospective	Unicenter	Asia	pEP-SM1, pEP-MM	SSD	WLI/NBI/BLI	ESCC	No	No	High	16	804	14338	155	914	714	24	60	132
Tokai et al[57], 2020	Retrospective	Unicenter	Asia	pEP-SM1	SSD	NBI/WLI	ESCC	No	No	High	13	NM	10179	NM	279	159	24	30	66

WLI: White-light imaging; NBI: Narrow-band imaging; BLI: Blue-laser imaging; TP: True positive; FP: False positive; FN: False negative; TN: True negative; EAC: Esophageal adenocarcinoma; ESCC: Esophageal squamous cell carcinoma; HGD: High-grade dysplasia; NM: Not mentioned; CNN: Convolutional neural network; pEP-SM1: Pathological intraepithelial-submucosal microinvasive; pEP-MM: Pathological intraepithelial-muscularis mucosa.