Machine learning algorithms for predicting quality of life improvements after digital orthodontic treatment: a retrospective analysis.

Abstract

Background: Digital orthodontic treatment has revolutionized clinical practice, yet predicting individual patient outcomes remains challenging. This retrospective study developed and validated machine learning algorithms to predict quality of life improvements following digital orthodontic treatment.

Methods: Clinical data from 386 patients who underwent clear aligner therapy between January 2020 and December 2023 were analyzed. The dataset included demographic information, clinical parameters, imaging data, and standardized quality of life assessments using OHIP-14, IOTN, and VAS scales. Three machine learning algorithms-random forest, support vector machine, and neural network-were trained and evaluated using 70% training, 15% validation, and 15% test sets.

Results: Digital orthodontic treatment demonstrated 92.3 ± 5.8% tooth movement accuracy and reduced average treatment duration to 18.5 ± 4.2 months. Quality of life assessments revealed significant improvements, with OHIP-14 scores decreasing from 24.6 ± 8.2 to 8.2 ± 4.3 (66.7% reduction, P < 0.001), and VAS aesthetic satisfaction increasing from 28.4 ± 12.3 to 85.6 ± 8.7 (P < 0.001). The random forest algorithm achieved superior predictive performance with 87.9% accuracy, 89.5% sensitivity, 85.7% specificity, and 0.93 AUC. Feature importance analysis identified baseline OHIP-14 scores (0.142), crowding severity (0.131), treatment duration (0.121), and patient compliance (0.111) as primary predictive factors. Clinical implementation of the prediction system improved treatment understanding (92.3% vs. 78.6%, P < 0.01) and patient satisfaction (94.8% vs. 83.2%, P < 0.01) compared to conventional consultations.

Conclusion: This study demonstrates that machine learning can accurately predict orthodontic treatment outcomes and enhance clinical decision-making. The developed predictive system provides a valuable tool for outcome prediction and identification of factors associated with treatment success, potentially transforming orthodontic practice by enabling data-driven, patient-specific care strategies that optimize treatment outcomes and patient satisfaction.