A deep learning framework for automated dental segmentation and diagnostic report generation from cone-beam computed tomography

The two-stage pipeline model effectively integrates automated tooth segmentation and auxiliary diagnosis. The segmentation module demonstrates high accuracy, while the classification module exhibits strong diagnostic performance. The final output seamlessly combines segmentation and the diagnostic results, enabling the automated generation of structured auxiliary diagnostic reports with tooth numbering. By significantly enhancing the diagnostic efficiency, the proposed method offers substantial support for clinical decision-making in dentistry and holds great potential for real-world applications.