INTRODUCTION: In this study, we investigated the landmark identification errors on cone-beam computed tomography (CBCT)-derived cephalograms and conventional digital cephalograms. METHODS: Twenty patients who had both a CBCT-derived cephalogram and a conventional digital cephalogram were recruited. Twenty commonly used lateral cephalometric landmarks and 2 fiducial points were identified on each cephalogram by 11 observers at 2 time points. The mean positions of the landmarks identified by all observers were used as the best estimate to calculate the landmark identification errors. In addition to univariate analysis, regression analysis of landmark identification errors was conducted for identifying the predicting variables of the observed landmark identification errors. To properly handle the multilayer correlations among the clustered observations, a marginal multiple linear regression model was fitted to our correlated data by using the well-known generalized estimating equations method. In addition to image modality, many variables potentially affecting landmark identification errors were considered, including location and characteristics of the landmark, seniority of the observer, and patient information (sex, age, metallic dental restorations, and facial asymmetry). RESULTS: Image modality was not the significant variable in the final generalized estimating equations model. The regression coefficient estimates of the significant landmarks for the overall identification error ranged from -0.99 (Or) to 1.42 mm (Ba). The difficulty of identifying landmarks on structural images with multiple overlapping--eg, Or, U1R, L1R, Po, Ba, UMo, and LMo--increased the identification error by 1.17 mm. In the CBCT modality, the identification errors significantly decreased at Ba (-0.76 mm). CONCLUSIONS: The overall landmark identification errors on CBCT-derived cephalograms were comparable to those on conventional digital cephalograms, and Ba was more reliable on CBCT-derived cephalograms.
INTRODUCTION: In this study, we investigated the landmark identification errors on cone-beam computed tomography (CBCT)-derived cephalograms and conventional digital cephalograms. METHODS: Twenty patients who had both a CBCT-derived cephalogram and a conventional digital cephalogram were recruited. Twenty commonly used lateral cephalometric landmarks and 2 fiducial points were identified on each cephalogram by 11 observers at 2 time points. The mean positions of the landmarks identified by all observers were used as the best estimate to calculate the landmark identification errors. In addition to univariate analysis, regression analysis of landmark identification errors was conducted for identifying the predicting variables of the observed landmark identification errors. To properly handle the multilayer correlations among the clustered observations, a marginal multiple linear regression model was fitted to our correlated data by using the well-known generalized estimating equations method. In addition to image modality, many variables potentially affecting landmark identification errors were considered, including location and characteristics of the landmark, seniority of the observer, and patient information (sex, age, metallic dental restorations, and facial asymmetry). RESULTS: Image modality was not the significant variable in the final generalized estimating equations model. The regression coefficient estimates of the significant landmarks for the overall identification error ranged from -0.99 (Or) to 1.42 mm (Ba). The difficulty of identifying landmarks on structural images with multiple overlapping--eg, Or, U1R, L1R, Po, Ba, UMo, and LMo--increased the identification error by 1.17 mm. In the CBCT modality, the identification errors significantly decreased at Ba (-0.76 mm). CONCLUSIONS: The overall landmark identification errors on CBCT-derived cephalograms were comparable to those on conventional digital cephalograms, and Ba was more reliable on CBCT-derived cephalograms.
Authors: M Cummaudo; M Guerzoni; L Marasciuolo; D Gibelli; A Cigada; Z Obertovà; M Ratnayake; P Poppa; P Gabriel; S Ritz-Timme; C Cattaneo Journal: Int J Legal Med Date: 2013-03-21 Impact factor: 2.686
Authors: Fenik Kaml Muhammed; Adil O Abdullah; Zhwan Jamal Rashid; Tamara Pusic; Mohammed F Shbair; Yi Liu Journal: Oral Radiol Date: 2018-04-02 Impact factor: 1.852