Cross-Sectional Representation Learning for Childhood Caries Prognosis with Limited Longitudinal Supervision

Background/Objectives: Longitudinal dental examinations provide prognostic outcomes but are costly to collect. We investigated whether representations learned from public cross-sectional examinations could support prediction of new caries experience in baseline-sound permanent first molars when longitudinal labels are limited. Methods: An encoder learned masked tooth-state reconstruction from 5837 US National Health and Nutrition Examination Survey examinations. Among 919 baseline-eligible Saudi schoolchildren, 849 had complete eligible-tooth follow-up, with 263 events across 16 schools; frozen representations were evaluated for approximately one-year new caries experience. Nested internal–external evaluation across schools compared 15 prediction pipelines and representation controls, using development-only calibration and matched training-school budgets. Discrimination, probability accuracy, follow-up allocation, incomplete charts and outcome uncertainty were assessed. Results: Frozen transfer achieved an area under the receiver-operating-characteristic curve (AUROC) of 0.6848 versus 0.6643 for random forest, with Brier scores of 0.19461 and 0.19868; paired difference intervals included zero. At nominal 20% school capacity, 164 children were selected (19.32% realised capacity), capturing 89 versus 82 observed future cases: a difference of 2.66 percentage points (95% interval 0.31–5.86), conditional on fitted predictions. Exploratory training with eight of 15 development schools yielded AUROCs of 0.6663 and 0.6280, respectively. Allocation captured only four of 75 events among children aged 5–7. Conclusions: Transferred information may support limited-label prognosis, but clinically meaningful incremental value remains unconfirmed and selection coverage varies by age. Independent longitudinal validation and clinical-impact evaluation are needed before preventive-care deployment.