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 created an artificial intelligence (AI) model to predict dementia development and compared the prediction accuracy of the training data. The study participants were 7,384 older adults (age ≥ 75 years) who underwent regional dental checkups in Gifu Prefecture, Japan, in 2018 and 2020. Methods: The National Da-tabase of Health Insurance of Japan (NDB) was integrated with dental checkup data, and the participants were randomly divided into two datasets: training (n = 5,169) and valida-tion (n = 2,215). A data analytics tool was utilized to create the AI model with training da-ta in 2018 and data on the presence or absence of dementia development in 2020. Results: The AI model trained solely on NDB data showed 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 sensitivity of 0.75 and specificity of 0.95, indicating improvement in both metrics. Conclusions: Combining dif-ferent 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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