Automatic estimation of single-tooth width from standardized two-dimensional occlusal photographs using deep learning.

Abstract

OBJECTIVE: To develop a deep learning-based fully automated model for estimating the mesiodistal width of teeth in standardized occlusal photographs, enabling accurate and efficient assessment of individual tooth dimensions. METHODS: The dataset comprised 14,403 teeth, including 12,079 teeth from an internal dataset and 2,328 teeth from an independent external test set. Clinically obtained mesiodistal crown widths derived from three-dimensional (3D) intraoral scans served as reference values. Corresponding mesial and distal keypoints were annotated on two-dimensional (2D) intraoral photographs. A two-stage deep learning framework was developed, consisting of tooth keypoint detection followed by a depth-informed regression model to estimate mesiodistal crown widths. Model performance was evaluated using key metrics, including mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R²). RESULTS: The deep learning model demonstrated high accuracy across 12,079 teeth: MAE = 0.34 mm (95% confidence interval [CI]: 0.33–0.34); RMSE = 0.44 mm; R² = 0.94. External validation on 2,328 independent teeth demonstrated consistent performance (MAE = 0.33 mm, RMSE = 0.42 mm). CONCLUSIONS: Our deep learning approach reliably estimates mesiodistal crown widths from standardized occlusal photographs, demonstrating strong agreement with 3D intraoral scan reference measurements and good clinical reproducibility across independent datasets within the same standardized workflow. Future studies will further investigate its robustness across heterogeneous imaging devices and acquisition conditions.