Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis

Table 2 Technical characteristics of artificial intelligence-enhanced real-time computer-aided detection systems utilized in included studies

Ref.	Year	AI model type	Hardware configuration	Processing speed	Dataset size	Risk stratification tool
van der Putten et al[11]	2020	Integrated U-Net + Transfer Learning	Titan Xp 12GB GPU	< 2 sec/frame	500000 frames	None
Yan et al[12]	2020	EfficientNetB4	Not specified	20 fps	11000 frames	None
Struyvenberg et al[13]	2021	VGG16	Not specified	56 fps	318 video cases	None
Hussein et al[14]	2022	ResNet101 + FCN ResNet50	Not specified	48-56 fps	150000 frames	None
Zhao et al[15]	2022	U-Net	Not specified	Not specified	Not specified	None
Zhao et al[16]	2022	U-Net	GeForce RTX 3090	30 fps	Not specified	None
Abdelrahim et al[17]	2022	VGG16 + SegNet Hybrid Model	GeForce RTX 2080 Ti	30 fps	Training: 109071 frames; Testing: 75 video cases	None
Zhao et al[18]	2023	U-Net Extended Model	GeForce RTX 3090	30 fps	Not specified	OLGA
Meinikheim et al[19]	2024	DeepLabV3+ + ResNet50 + Mean-Teacher	Not specified	30 fps	Training: 51273 frames; Testing: 96 video cases	None
Tao et al[20]	2024	UNet++ + ResNet50 Dual Model System	Not specified	Not specified	Training: 119 video cases; Testing: 102 video cases	Kimura-Takemoto

AI: Artificial intelligence.