AIMC Topic: Dental Amalgam

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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.

Journal of dentistry
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 re...

Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education.

Journal of dental education
PURPOSE AND OBJECTIVE: Objective, valid, and reliable evaluations are needed in order to develop haptic skills in dental education. The aim of this study is to investigate the validity and reliability of the machine learning method in evaluating the ...

Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs.

Journal of dentistry
OBJECTIVES: Intraoral photographs might be considered the machine-readable equivalent of a clinical-based visual examination and can potentially be used to detect and categorize dental restorations. The first objective of this study was to develop a ...