A novel machine-learning-based model for prediction of open gingival embrasures between mandibular central incisors after clear aligners treatment: a retrospective cohort study.

objectiveTo develop a machine-learning-based model and construct a nomogram that integrates ClinCheck features and clinical risk factors for accurately predicting open gingival embrasures (OGE) between mandibular central incisors after clear aligner treatment (CAT).

methodsA total of 297 patients (163 normal and 134 with OGE) who underwent Invisalign

resultsSix clinical features, including age, gingival papilla angle, interproximal reduction, crown morphology and two types of tooth movement, were selected through LASSO regression. The combined model that consisted of OGE-area and clinical features demonstrated superior predictive capacity (AUC: 0.880; 95% CI: 0.840-0.916), outperforming both clinical model (AUC: 0.815; 95% CI: 0.767-0.861; P < 0.001) and ClinCheck model (AUC: 0.860; 95% CI: 0.817-0.900; P < 0.05). The corrected C-statistic of the combined nomogram was 0.888, and the calibration curve exhibited great performance with a mean absolute error of 0.012. In the DCA curve, the combined model showed higher net benefit than the clinical model when the threshold probability exceeded 0.13, and higher than the ClinCheck model between 0.13 and 0.63.

conclusionThe integration of clinical features and ClinCheck in the machine-learning-based model demonstrated favorable predictive capabilities for OGE between lower central incisors. This comprehensive nomogram may contribute to precisely prediction and prevention of OGE in clinical practice.