Development of an artificial intelligent model for pre-endoscopic screening of precancerous lesions in gastric cancer

Autor: Lan Wang, Qian Zhang, Peng Zhang, Bowen Wu, Jun Chen, Jiamin Gong, Kaiqiang Tang, Shiyu Du, Shao Li
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Chinese Medicine, Vol 19, Iss 1, Pp 1-11 (2024)
Druh dokumentu: article
ISSN: 1749-8546
DOI: 10.1186/s13020-024-00963-5
Popis: Abstract Background 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. Methods From December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital. Results A multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for overall PLGC (95% confidence interval (CI) 0.71–0.76, p
Databáze: Directory of Open Access Journals
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