Delayed Dental Development in Children With Non-Syndromic Hypodontia: A Cross-Sectional Study Using a Machine Learning Approach to Dental Age Estimation.

objectivesTo investigate the influence of non-syndromic hypodontia on radiographic dental development and to estimate dental age in children with bilateral mandibular agenesis using a machine learning approach. MATERIALS AND

methodsThis retrospective cross-sectional study included 626 children aged 6-15 years (311 with dental agenesis, 315 matched controls). Dental age (DA) was assessed using the original Demirjian method. In cases with bilateral congenitally missing teeth in the mandible, DA was estimated using supervised machine learning models based on specific random forests, following a secondary-diagnosis approach. Dental developmental delay was calculated as the difference between dental and chronological age (DA-CA) and compared across groups defined by agenesis status, severity and pattern. Multiple linear regression was applied to evaluate the effects of hypodontia, sex, chronological age and their interactions on DA-CA.

resultsEight random-forest models were trained, achieving good age-prediction accuracy (MAE = 0.08-0.28 years, R

conclusionOur results were consistent with broader evidence linking hypodontia to altered developmental timing. Machine learning imputation offers a robust approach for missing teeth and can be implemented for age estimation in larger cohorts for orthodontic or forensic purposes.