Explainable Deep Learning Approaches for Risk Screening of Periodontitis

Abstract

Several pieces of evidence have been reported regarding the association between periodontitis and systemic diseases. Despite the emphasized significance of prevention and early diagnosis of periodontitis, there is still a lack of a clinical tool for early screening of this condition. Therefore, this study aims to use explainable artificial intelligence (XAI) technology to facilitate early screening of periodontitis. This is achieved by analyzing various clinical features and providing individualized risk assessment using XAI. We used 1,012 variables for a total of 30,465 participants data from National Health and Nutrition Examination Survey (NHANES). After preprocessing, 9,632 and 5,601 participants were left for all age groups and the over 50 y age group, respectively. They were used to train deep learning and machine learning models optimized for opportunistic screening and diagnosis analysis of periodontitis based on Centers for Disease Control and Prevention/ American Academy of Pediatrics case definition. Local interpretable model-agnostic explanations (LIME) were applied to evaluate potential associated factors, including demographic, lifestyle, medical, and biochemical factors. The deep learning models showed area under the curve values of 0.858 ± 0.011 for the opportunistic screening and 0.865 ± 0.008 for the diagnostic dataset, outperforming baselines. By using LIME, we elicited important features and assessed the combined impact and interpretation of each feature on individual risk. Associated factors such as age, sex, diabetes status, tissue transglutaminase, and smoking status have emerged as crucial features that are about twice as important than other features, while arthritis, sleep disorders, high blood pressure, cholesterol levels, and overweight have also been identified as contributing factors to periodontitis. The feature contribution rankings generated with XAI offered insights that align well with clinically recognized associated factors for periodontitis. These results highlight the utility of XAI in deep learning-based associated factor analysis for detecting clinically associated factors and the assistance of XAI in developing early detection and prevention strategies for periodontitis in medical checkups.