Establishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.

Background/purpose: Artificial intelligence (AI) can assist in medical diagnosis owing to its high accuracy and efficiency. This study aimed to develop a diagnostic system for automatically determining the degree of tooth wear (TW) using intraoral photographs with deep learning. Materials and methods: The study included 388 intraoral photographs. A tooth segmentation model was first established using the Mask R-CNN architecture, which incorporated U-Net and SGE attention mechanisms. Subsequently, 2774 individual tooth images output from the segmentation model were included into the classification task, labeled and randomized into training, validation, and test sets with 6.0:2.0:2.0 ratio. A vision transformer model optimized using a mask mechanism was constructed for TW degree classification. The models were evaluated using the accuracy, precision, recall, and F1-score metrics. The time required for AI analysis was calculated. Results: The accuracy of the tooth segmentation model was 0.95. The average accuracy, precision, recall, and F1-score in the classification task were 0.93, 0.91, 0.88, and 0.89, respectively. The F1-score differed in different grades (0.97 for grade 0, 0.90 for grade 1, 0.88 for grade 2, and 0.82 for grade 3). No significant difference was observed in the accuracy between different surfaces. The AI system reduced the time required to grade an individual tooth surface to 0.07 s, compared to the 2.67 s required by clinicians. Conclusion: The developed system provides superior accuracy and efficiency in determining TW degree using intraoral photographs. It might assist clinicians in the decision-making for TW treatment and help patients perform self-assessments and disease follow-ups.