Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis

Table 1 Summary of all studies investigating the development of machine learning algorithms for the endoscopic ultrasound diagnosis of subepithelial lesions

Ref.	Geographical setting	Study type	Patients	Sex (male/female)	GIST	GIL	Other SELs	Images	Tumor location	AI model	External validation	Endoscopists comparison	Histopathology
Minoda et al[29], 20221	Eastern	Retrospective	52	33/19	36	14	2	2718	Esophagus, duodenum, and colon	CNN	Yes	Yes	Yes
Hirai et al[33], 2022	Eastern	Retrospective	664	231/188	435	97	100	16110	Esophagus, stomach, and duodenum	CNN	Yes	Yes	Yes
Tanaka et al[34], 2022	Eastern	Retrospective	53	28/25	42	11	-	10600	Stomach	CNN	No	Yes	Yes
Yang et al[32], 2022	Eastern	Retrospective	752	337/415	348	404	-	10439	Esophagus, stomach, duodenum, colon, and rectum	CNN	Yes	Yes	Yes
Oh et al[30], 2021	Eastern	Retrospective	168	NI	125	43	-	546	Stomach	CNN	Yes	Yes	Yes
Seven et al[31], 2022	Eastern	Retrospective	145	72/73	109	36	-	1362	Esophagus, stomach, and duodenum	CNN	Yes	Yes	Yes
Kim et al[27], 2020	Eastern	Retrospective	248	111/137	157	55	35	1117	Stomach	CNN	Yes	Yes	Yes
Minoda et al[28], 2020	Eastern	Retrospective	273	138/135	184	65	24	3980	Stomach	CNN	Yes	Yes	Yes

¹The author utilized the same software developed in the previous study (2020), however evaluating non-gastric subepithelial lesions.

NI: Non-information; GIST: Gastrointestinal stromal tumor; AI: Artificial intelligence; CNN: Convolutional neural network; GIL: Gastrointestinal leiomyoma.