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.

Trial Registration ChiCTR2100044006

The authors have declared no competing interest.

Funding for this study was provided by the National Natural Science Foundation of China, China [81225025 and 62061160369]; and the Beijing National Research Center for Information Science and Technology, China [BNR2019TD01020 and BNR2019RC01012].

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The experimental protocol was approved by the Human Ethics Committee of Institution Review Board of Tsinghua University.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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All data produced in the present study are available upon reasonable request to the authors