Assessing the readiness of dental electronic health records for machine learning prediction of procedure outcomes: Insights from the bigmouth repository on composite and amalgam restoration survival rates.

OBJECTIVE: Dental electronic health records (EHRs) often lack comprehensive data for evaluating procedure outcomes. Machine learning (ML) enables predictive modeling but its applicability to dental EHR data remains unclear. This study assessed the readiness of dental EHRs for predicting restoration failure using classical and ML models. METHODS: Data from the BigMouth Dental Data Repository was analyzed, focusing on posterior restorations in permanent molars of vital teeth. Failure was defined as root canal treatment or extraction within five years. Predictors included restoration type, caries depth, number of surfaces filled, time to failure, age, and gender. Cox proportional hazards analysis and ML models compared amalgam and composite restorations. RESULTS: A total of 21,510 restorations placed between 2011 and 2020 across nine universities were identified. Both classical and ML analyses indicated superior five-year survival for composite restorations. Classic survival analysis showed a higher failure rate for amalgam (8.84 %) compared to composite (4.36 %). Among ML survival models, Cox Proportional Hazards (Cox pH), Random Survival Forest (RSF), and DeepSurv, Cox pH had the highest Concordance Index (C-Index) at 0.64, while RSF and DeepSurv both achieved 0.63. RSF recorded the highest Time-Dependent Area Under Curve (0.620). Age, restoration type, and number of surfaces filled were the most significant predictors of restoration survival. CONCLUSION: Our findings demonstrate that dental EHRs, in their current state, are not yet equipped to support accurate assessment and prediction of restoration failure. ML models achieved only moderate predictive performance, largely due to the absence of key clinical variables necessary for precise evaluation. CLINICAL SIGNIFICANCE: Gaps in dental EHR data limit the accuracy of ML-based risk prediction. Enhancing diagnostic coding and data standardization can improve predictive modeling, support evidence-based decision-making, and optimize treatment planning. Strengthening EHR documentation will enable Artificial Intelligence-driven tools to enhance clinical outcomes and advance precision dentistry.