ArticleJournal of periodontology2026
Deep learning cone-beam computed tomography image segmentation for the 3D visualization of mandibular infraosseous periodontal defects.
Article in Journal of periodontology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe 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.
methodsA 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.
resultsMeasurements 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.
conclusionsThese 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.
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