Monsarrat Paul, Bernard David, Marty Mathieu, Cecchin-Albertoni Chiara, Doumard Emmanuel, Gez Laure, Aligon Julien, Vergnes Jean-Noël, Casteilla Louis, Kemoun Philippe
RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, 31100 Toulouse, France.
Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France.
J Pers Med. 2022 Feb 4;12(2):217. doi: 10.3390/jpm12020217.
Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.
早期诊断对于易患可能导致牙齿脱落的牙齿支持组织疾病（如牙周炎）的个体至关重要，以便预防全身影响并维持生活质量。本研究的目的是提出一种个性化的可解释机器学习算法，该算法仅基于可在诊所轻松收集的非侵入性预测指标，以识别有患牙周疾病风险的受试者。为此，对532名受试者的个人数据和牙周健康状况进行了评估。使用了一种结合特征选择步骤、多层感知器和SHapley加性解释（SHAP）可解释性的机器学习管道来构建该算法。健康牙周组织和牙周炎的预测分数最终F1分数分别为0.74和0.68，而牙龈炎症则更难预测（F1分数为0.32）。年龄、体重指数、吸烟习惯、全身病理学、饮食、酒精、教育水平和激素状态被发现是牙周健康预测中最具贡献性的变量。该算法清楚地显示了35岁前后不同的风险概况，并表明了在易患牙龈炎症或牙周炎方面的转变年龄。这种结合机器学习和最新可解释性算法的系统性牙周疾病风险概况的创新方法，为新的牙周健康预测策略铺平了道路。
