Three-Dimensional Cone-Beam Computed Tomography Volume Registration for the Analysis of Alveolar Bone Changes Public Deposited

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  • March 20, 2019
  • Green, Peter
    • Affiliation: School of Dentistry, Oral Pathology Section, Oral and Maxillofacial Radiology Graduate Program
  • Objectives: 1. Determine accuracy of detecting alveolar bone loss affecting tooth support with registered cone-beam computed tomography (CBCT) compared to intraoral radiographs (IO). 2. Assess repeatability of measurements with CBCT compared to IO. 3. Identify factors which may affect defect detection. 4. Determine effect of bucco-lingual bone thickness on defect detection. Methods: Defects were created in mandibles and imaged pre-, post-defect with IO and CBCT. Six observers viewed IO radiographs pre-, post-defect followed by CBCTs to determine defect presence and extent. Receiver Operating Characteristic (ROC), sensitivity, specificity, logistic regression were used. Inter-, intra-observer agreement were assessed by intraclass correlation coefficient and weighted kappa. Results: Mean ROC Az for CBCT (0.90) was not statistically different from mean Az of IO (0.81). CBCT sensitivity was higher than IO sensitivity (0.85 vs. 0.63, p<0.05). CBCT specificity was equivalent to IO specificity (0.91 vs. 0.84, p>0.05). Bone thickness, imaging modality, observer had significant effects on bone loss detection. Odds ratio for CBCT vs. IO diagnostic accuracy was 2.29. Odds ratio for bucco-lingual bone thickness was 1.52. There was moderate agreement between observers and substantial agreement within observers for detection of bone loss and measurement of extent. Conclusions: CBCT showed equivalent diagnostic efficacy and specificity for defect detection, but higher sensitivity than IO. CBCT more than doubles the odds of accurate bone loss assessment compared to IO. Odds of bone loss detection increase by approximately 50% per millimeter of bucco-lingual bone loss.
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  • In Copyright
  • Tyndall, Donald
  • Mol, André
  • Kohltfarber, Heidi
  • Moretti, Antonio
  • Master of Science
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2017

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