Artificial Intelligence for Early Prediction of Poor Compliance in Clear Aligner Therapy
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Background: Successful clear aligner therapy depends on sustained patient adherence, but inadequate wear is often recognised only after treatment progression has been affected. Objective: To develop and internally validate models for predicting poor compliance during weeks 5–12 using baseline characteristics and behavioural data collected during the first four weeks of treatment. Methods: This prospective observational study included 250 adults receiving clear aligner therapy in dental clinics in Lahore, Pakistan. Demographic and clinical characteristics, daily wear time, digital check-ins, aligner-change behaviour, pain scores, and appointment attendance were recorded. Poor compliance was defined as wear below 20 hours per day on at least 30% of monitored days during weeks 5–12. Logistic regression, random forest, and gradient boosting were evaluated using repeated stratified 10-fold cross-validation. Results: Poor compliance occurred in 72 participants (28.8%). Compared with compliant participants, those with poor compliance had 3.20 fewer hours of early daily wear, more missed digital check-ins, higher odds of delayed aligner changes (OR: 7.50; 95% CI: 4.04–13.92), and higher odds of missed or rescheduled appointments (OR: 3.81; 95% CI: 1.99–7.30). Gradient boosting produced an AUC of 0.90, sensitivity of 83%, specificity of 84%, accuracy of 84%, and Brier score of 0.11. Conclusion: Early behavioural data showed promising internally validated performance for identifying subsequent poor compliance. External validation and comparison with simpler prediction approaches are required before clinical implementation
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