Alveolar Bone Segmentation Methods in Assessing the Effectiveness of Periodontal Defect Regeneration Through Machine Learning of CBCT Data: A Systematic Review

Abstract

Objectives: To evaluate various segmentation methods used for cone-beam computed tomography (CBCT) images of alveolar bone, assessing their effectiveness and potential benefits in digital workflows for periodontal defect regeneration. Data: This review adheres to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) Checklist. Source: A comprehensive literature search was conducted from May 2024 to June 2025 using MeSH terms on PubMed, Scopus, Web of Science, and Medline databases, with publication date restricted to 5 years. The PRISMA guidelines were followed to ensure a systematic review process, and the review protocol was registered with Prospero. The QUADAS-2 checklist was used to evaluate the risk of bias in the included studies. Study Selection: The initial search yielded 834 articles, which were systematically filtered down to 23 eligible studies. Deep learning methods, particularly U-Net, were the most frequently employed segmentation techniques. Four studies utilized semi-automated methods, while the remaining studies relied on manual or other segmentation methods. The Dice similarity (DC) index, ranging from 76% to 98%, was the primary metric used to assess segmentation performance. Conclusions: Significant differences were observed between the segmentation of healthy and defective alveolar bone, underscoring the need to enhance deep learning–based methods. Accurate segmentation of periodontal defects in DICOM images is a crucial first step in the scaffold workflow, as it enables precise assessment of defect morphology and volume. This information directly informs scaffold design, ensuring that the scaffold geometry is tailored to the patient-specific defect. Prospero Registration: CRD42024590957.