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A personalized periodontitis risk based on nonimage electronic dental records by machine learning
Laura Swinckels1, Ander de Keijzer2, Bruno G Loos3
1Academic 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.
Objective:
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).
Methods:
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.
Results:
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 %).
Conclusion:
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.
Clinical Significance:
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.
