Construction of tongue image-based machine learning model for screening patients with gastric precancerous lesions

Abstract

Screening patients with precancerous lesions of gastric cancer (PLGC) is important for gastric cancer prevention. It could improve the accuracy and convenience of PLGC screening to uncover and integrate valuable characteristics of noninvasive medical images involving in PLGC, by applying machine learning methodologies. In this study, based on unbiasedly uncovering potential associations between tongue image characteristics and PLGC and integrating gastric cancer-related canonical risk factors, including age, sex, Hp infection, we focused on tongue images and constructed a tongue image-based PLGC screening deep learning model (AITongue). Then, validation analysis on an independent cohort of 1,995 patients revealed the AITongue model could screen PLGC individuals with an AUC of 0.75, 10.3% higher than that of the model constructed with gastric cancer-related canonical risk factors. Of note, we investigated the value of the AITongue model in predicting PLGC risk by establishing a prospective PLGC follow-up cohort, reaching an AUC of 0.71. In addition, we have developed a smartphone-based App screening system to enhance the application convenience of the AITongue model in the natural population. Collectively, our study has demonstrated the value of tongue image characteristics in PLGC screening and risk prediction.