Photography-based dental plaque detection and report generation among preschool children using PlaqueSAM

Abstract

Abstract Dental plaque is the primary etiological factor for common oral diseases such as dental caries and gingivitis, particularly affecting susceptible populations including children. Preventing plaque-induced dental diseases in pediatric patients remains a global challenge as certain plaque regions are often missed during daily cleaning. Early detection and removal of plaque are essential but often difficult to achieve consistently. A routine monitoring tool that visualizes plaque accumulation on primary teeth could allow parents to actively track their children’s oral health, enabling timely prevention and intervention for dental caries and periodontal diseases. Inspired by these, we present PlaqueSAM, an automatic plaque detection system using intraoral photographs captured by smartphones. By uploading six images captured with the assistance of alignment overlays, PlaqueSAM can generate a comprehensive plaque detection report with visual references. PlaqueSAM was developed using 3402 prospectively collected intraoral photographs from 567 preschool children across 11 kindergartens in Hong Kong using a three-stage training strategy based on a multi-task learning paradigm. The performance of PlaqueSAM was quantitatively evaluated on 81 children, achieving sensitivity, specificity, and accuracy of 76.58, 82.47, and 79.60%, respectively. These findings suggest that PlaqueSAM may support future plaque screening workflows without requiring disclosing agents or plastic retractors typically used by dental professionals, although home-based use requires further validation under real-world user-operated conditions. With further real-world validation, PlaqueSAM may support dynamic monitoring of children’s daily plaque accumulation and cleaning, thereby contributing to the advancement of teledentistry and promoting children’s oral health remotely, conveniently, and at a lower cost.