Objectives: This study developed and validated a deep learning-based method to automatically segment and number teeth in panoramic radiographs across primary, mixed, and permanent dentitions.
Methods: A total of 6,046 panoramic radiographs were collected and annotated. The dataset encompassed primary, mixed and permanent dentitions and dental abnormalities such as tooth number anomalies, dental diseases, dental prostheses, and orthodontic appliances. A deep learning-based algorithm consisting of a U-Net-based region of interest extraction model, a Hybrid Task Cascade-based teeth segmentation and numbering model, and a post-processing procedure was trained on 4,232 images, validated on 605 images, and tested on 1,209 images. Precision, recall and Intersection-over-Union (IoU) were used to evaluate its performance.
Results: The deep learning-based teeth identification algorithm achieved good performance on panoramic radiographs, with precision and recall for teeth segmentation and numbering exceeding 97%, and the IoU between predictions and ground truths reaching 92%. It generalized well across all three dentition stages and complex real-world cases.
Conclusions: By utilizing a two-stage training framework with a large-scale heterogeneous dataset, the automatic teeth identification algorithm achieved a performance level comparable to that of dental experts.
Clinical significance: Deep learning can be leveraged to aid clinical interpretation of panoramic radiographs across primary, mixed, and permanent dentitions, even in the presence of real-world complexities. This robust teeth identification algorithm could contribute to the future development of more advanced, diagnosis- or treatment-oriented dental automation systems.
Keywords: Deep learning; Dental image interpretation; Panoramic radiograph; Teeth identification.
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