Accuracy of an Artificial Intelligence Model to Predict Dementia Development with Additional Dental Checkup Data: A Retrospective Cohort Study

Abstract

Background: This retrospective cohort study developed an artificial intelligence (AI) model to predict incident dementia and evaluated its predictive performance using a validation cohort. The study participants were 7384 older adults (age ≥ 75 years) who underwent regional dental checkup in Gifu Prefecture, Japan, in 2018 and 2020. Methods: The National Database of Health Insurance of Japan (NDB) was integrated with dental checkup data, and the participants were randomly divided into two datasets: training (n = 5169) and validation (n = 2215). A data analytics tool was utilized to create the AI model with training data in 2018 and data on the presence or absence of dementia development in 2020. Results: The AI model trained solely on NDB data showed a sensitivity of 0.73 and specificity of 0.91 in predicting the presence or absence of dementia development after 2 years. By contrast, the AI model trained on NDB and dental checkup data showed a sensitivity of 0.75 and specificity of 0.95, indicating improvement in both metrics. Conclusions: Combining different sets of data, such as NDB and dental checkup data, for training may be useful for improving the accuracy of AI models to predict dementia development.

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