Swinckels Laura, de Keijzer Ander, Loos Bruno G, Applegate Reuben Joseph, Kookal Krishna Kumar, Kalenderian Elsbeth, Bijwaard Harmen, Bruers Josef
Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, The Netherlands; Department Oral Hygiene, Faculty of Health, Sports and Welfare, Inholland University of Applied Sciences, Amsterdam, The Netherlands; Centre of Expertise Prevention in Health and Social Care, Inholland University of Applied Sciences, Haarlem, The Netherlands; Medical Technology Research Group, Inholland University of Applied Sciences, The Netherlands; Data Driven Smart Society Research Group, Inholland University of Applied Sciences, Alkmaar, The Netherlands.
Data Driven Smart Society Research Group, Inholland University of Applied Sciences, Alkmaar, The Netherlands; Applied Responsible Artificial Intelligence Research Group, Avans University of Applied Sciences, Breda, The Netherlands.
J Dent. 2025 Feb;153:105469. doi: 10.1016/j.jdent.2024.105469. Epub 2024 Nov 19.
This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).
By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing. By learning from their data, a model was trained. The ability of the developed model to predict PD was evaluated by the accuracy, sensitivity, specificity and area under the curve (AUROC) and the most important features were determined. The best-performing model was applied to the validation set.
The final study population included 43,331 participants. Based on the development set, the Random Forest model performed with high sensitivity (81 %) and had an excellent AUROC (94 %), compared to four other ML and deep learning techniques. The most important predictors were bleeding proportion, age, the number of visits, prior preventive treatment, smoking and drugs usage. When the model was applied to the validation set, the model could detect almost all cases (91 %), but overestimated controls (specificity=0.54). When EDRs were retrieved 3 years before the PD diagnosis, the predictions for PD were still sensitive (89 %).
Based on consistent and complete EDR, ML has an excellent ability to assist with the early detection and prevention of PD cases. Further research is required to follow-up high-risk controls and improve the model's internal and external validation. Improved EDR documentation is an important first step.
If such ML models become clinically applied, clinicians can be assisted with personalized risk predictions based on the individual. If the key riskcontributing factors for the individual are revealed/provided, ML can suggest targeted prevention interventions. These advancements can contribute to a reduced workload, sustainable EDRs, data-based dental care, and, ultimately, improved patient outcomes.
本研究旨在开发一种机器学习（ML）模型，以基于非图像电子牙科记录（EDR）预测牙周疾病（PD）的风险。
通过使用在BigMouth数据库中收集的EDR，纳入了来自美国的牙科患者。根据PD诊断、治疗和牙周袋情况，将患者标记为病例或对照。通过学习他们的数据，训练了一个模型。通过准确性、敏感性、特异性和曲线下面积（AUROC）评估所开发模型预测PD的能力，并确定最重要的特征。将性能最佳的模型应用于验证集。
最终研究人群包括43331名参与者。基于开发集，与其他四种ML和深度学习技术相比，随机森林模型具有较高的敏感性（81%）和出色的AUROC（94%）。最重要的预测因素是出血比例、年龄、就诊次数、既往预防性治疗、吸烟和药物使用情况。当将该模型应用于验证集时，该模型几乎可以检测到所有病例（91%），但高估了对照（特异性=0.54）。当在PD诊断前3年检索EDR时，对PD的预测仍然具有敏感性（89%）。
基于一致且完整的EDR，ML具有出色的能力来协助早期检测和预防PD病例。需要进一步研究对高风险对照进行随访并改善模型的内部和外部验证。改进EDR文档记录是重要的第一步。
如果此类ML模型在临床上得到应用，临床医生可以基于个体进行个性化风险预测。如果揭示/提供了个体的关键风险因素，ML可以建议有针对性的预防干预措施。这些进展有助于减轻工作量、实现可持续的EDR、基于数据的牙科护理，并最终改善患者预后。
