A deep learning model for multiclass tooth segmentation on cone-beam computed tomography scans.

Abstract

Introduction: Machine learning, a common artificial intelligence technology in medical image analysis, enables computers to learn statistical patterns from pairs of data and annotated labels. Supervised learning in machine learning allows the computer to predict how a specific anatomic structure should be segmented in new patients. This study aimed to develop and validate a deep learning algorithm that automatically creates 3-dimensional surface models of human teeth from a cone-beam computed tomography scan.

Methods: A multiresolution dataset, including 216 × 272 × 272, 512 × 512 × 512, and 576 × 768 × 768. Ground truth labels for teeth segmentation were generated. Random partitioning was applied to allocate 140 patients to the training set, 40 to the validation set, and 30 scans for testing and model performance evaluation. Different evaluation metrics were used for assessment.

Results: Our teeth identification model has achieved an accuracy of 87.92% ± 4.43% on the test set. The general (binary) teeth segmentation model achieved a notably higher accuracy, segmenting the teeth with 93.16% ± 1.18%.

Conclusions: The success of our model not only validates the efficacy of using artificial intelligence for dental imaging analysis but also sets a promising foundation for future advancements in automated and precise dental segmentation techniques.