Deep learning-assisted cone-beam computed tomography volumetric assessment of mandibular anterior alveolar bone changes with and without periodontally accelerated osteogenic orthodontics during skeletal Class III orthodontic-orthognathic treatment.
Authors
Affiliations (5)
Affiliations (5)
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China.
- Department of Orthodontics, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China.
- Center of Digital Dentistry, Peking University School and Hospital of Stomatology and National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China. Electronic address: [email protected].
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China. Electronic address: [email protected].
Abstract
Patients with skeletal Class III malocclusion often have thin anterior alveolar bone and an increased periodontal risk. This study evaluated 3-dimensional (3D) alveolar bone volume changes in patients treated with or without periodontally accelerated osteogenic orthodontics (PAOO) using a deep learning-assisted cone-beam computed tomography (CBCT) approach. A total of 42 adults undergoing orthodontic-orthognathic treatment were divided into a PAOO group (group S, n = 21) and a matched non-PAOO control group (group NS, n = 21). CBCT scans were obtained at baseline (T1) and posttreatment (T3), with a 6-month post-PAOO scan (T2) for group S. Labial, lingual, and total alveolar bone volumes were quantified using a deep learning-assisted automated segmentation tool. Linear mixed-effects models were used to analyze longitudinal changes and treatment effects, adjusting for baseline root length. In group S, labial alveolar bone volume increased from T1 (42.94 ± 23.15 mm<sup>3</sup>) to T3 (84.15 ± 36.82 mm<sup>3</sup>; P <0.001), contributing to an increase in total alveolar bone volume (139.92 ± 86.42 mm<sup>3</sup> at T3 vs 114.20 ± 66.86 mm<sup>3</sup> at T1; P <0.001). Group NS showed no change in labial bone volume (P = 0.846) but a reduction in total bone volume at T3 (93.76 ± 67.24 mm<sup>3</sup>; P <0.001), primarily driven by lingual loss. Group S had greater labial and total alveolar bone volumes than group NS at T3 (P <0.001). PAOO was associated with increased and maintained labial alveolar bone volume, which may help preserve total alveolar support during combined treatment. Deep learning-assisted CBCT segmentation can serve as an efficient volumetric evaluation tool.