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.

中文翻译：

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用于牙周炎风险筛查的可解释深度学习方法


关于牙周炎与全身性疾病之间关联的几项证据已被报道。尽管强调了牙周炎预防和早期诊断的重要性，但仍然缺乏早期筛查这种情况的临床工具。因此，本研究旨在使用可解释的人工智能 （XAI） 技术来促进牙周炎的早期筛查。这是通过分析各种临床特征并使用 XAI 提供个体化风险评估来实现的。我们使用了 1,012 个变量，总共有 30,465 名参与者数据来自全国健康和营养检查调查 （NHANES）。预处理后，所有年龄组和 50 岁以上年龄组分别留下 9,632 名和 5,601 名参与者。它们用于训练深度学习和机器学习模型，这些模型针对基于疾病控制和预防中心/美国儿科学会病例定义的机会性牙周炎筛查和诊断分析进行了优化。应用局部可解释模型不可知解释 （LIME） 来评估潜在的相关因素，包括人口统计学、生活方式、医学和生化因素。深度学习模型的曲线下面积值为 0.858 ± 0.011（机会性筛查）和 0.865 ± 0.008（诊断数据集）的曲线下面积值，优于基线。通过使用 LIME，我们引出了重要特征，并评估了每个特征对个体风险的综合影响和解释。 年龄、性别、糖尿病状况、组织谷氨酰胺转移酶和吸烟状况等相关因素已成为关键特征，其重要性大约是其他特征的两倍，而关节炎、睡眠障碍、高血压、胆固醇水平和超重也被确定为导致牙周炎的因素。使用 XAI 生成的特征贡献排名提供了与临床公认的牙周炎相关因素非常一致的见解。这些结果突出了 XAI 在基于深度学习的相关因子分析中检测临床相关因素的效用，以及 XAI 在体检中制定牙周炎早期检测和预防策略的帮助。