Lan Wang, Qian Zhang, Peng Zhang, Bowen Wu, Shiyu Du, Kaiqiang Tang, Shao Li
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Abstract
Objective Given the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy.
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