Deep learning cone-beam computed tomography image segmentation for the 3D visualization of mandibular infraosseous periodontal defects.
Authors
Affiliations (7)
Affiliations (7)
- Department of Periodontology, Semmelweis University, Budapest, Hungary.
- Department of Periodontology, University of Bern, Bern, Switzerland.
- Dent.AI Medical Imaging Ltd., Budapest, Hungary.
- Empresa de Base Technológica Internacional de Canarias, S.L. (EBATINCA), Las Palmas De Gran Canaria, Spain.
- School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Campus, St. Thomas' Hospital, London, UK.
- Department of Image Processing and Computer Graphics, University of Szeged, Szeged, Hungary.
- Section of Periodontology, Faculty of Odontology, University Complutense of Madrid, Madrid, Spain.
Abstract
The accurate assessment of infraosseous periodontal defects is crucial for effective diagnosis and treatment planning. Cone-beam computed tomography (CBCT) enables detailed imaging of these defects; however, to leverage their full potential, CBCT images must be reconstructed in 3 dimensions (3D). Manual and semi-automatic (SA) segmentation methods are time-consuming and prone to human error. This study aimed to evaluate the performance of a deep learning (DL) model in segmenting mandibular infraosseous periodontal defects on CBCT scans. A multi-stage Segmentation Residual Network (SegResNet)-based DL model was used to segment CBCT scans from patients with stages III to IV periodontitis. Linear and volumetric measurements of infraosseous defects from DL-generated 3D models were compared to those obtained using SA segmentation. The depth (INFRA), width (WIDTH), angle (ANGLE), and volume of 48 infraosseous defects were assessed on both DL and SA segmentations. Measurements made on the DL and SA segmentations correlated strongly. The intraclass correlation coefficient (ICC) was 0.941 (p < 0.0001) for INFRA, 0.943 (p < 0.0001) for WIDTH, 0.889 (p < 0.0001) for ANGLE, and 0.948 (p < 0.0001) for defect volume. These results indicate high reliability of the DL model in capturing key characteristics of infraosseous periodontal defects. These findings support the use of DL-based CBCT segmentation as a valuable tool for enhancing periodontal diagnosis. However, as this study was limited to mandibular defects, applicability to maxillary cases remains to be validated.