Precision of orthodontic cephalometric measurements on ultra low dose-low dose CBCT reconstructed cephalograms

Bunningen, R. H. van; Dijkstra, Pieter U.; Dieters, Arjan; Meer, Wicher J. van der; Kuijpers-Jagtman, Anne Marie; Ren, Yijin

doi:10.1007/s00784-021-04127-9

Cited by 16 publications

(19 citation statements)

References 25 publications

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“…However, both examiners had ICC medians for all three types of cephalometric analysis methods higher than 0.75, indicating that all methods had good (between 0.75 and 0.9) to excellent (greater than 0.9) intra-examiner reliability [ 34 ]. The ICCs are similar to or higher than those reported in previous publications by other groups [ 37 , 38 , 39 , 40 ], demonstrating consistency and accuracy of the present study.…”

Section: Discussionsupporting

confidence: 91%

“…In addition, an ultra-low-dose CBCT imaging system has also been introduced in the dental field [ 44 , 45 ]. Excitingly, outside the range of 2 mm or degrees, there is no statistical difference in performing cephalometric analysis on the full-volume CBCTs taken under the ultra-low-dose protocol and standard protocol [ 38 , 45 ]. However, it has been noted that measurements based on images taken with the standard protocol have significantly smaller standard deviations than those taken with the ultra-low-dose protocol [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…Excitingly, outside the range of 2 mm or degrees, there is no statistical difference in performing cephalometric analysis on the full-volume CBCTs taken under the ultra-low-dose protocol and standard protocol [ 38 , 45 ]. However, it has been noted that measurements based on images taken with the standard protocol have significantly smaller standard deviations than those taken with the ultra-low-dose protocol [ 38 ]. Moreover, previous studies indicate the patient scanning positions influence the accuracy of 3D cephalometry analysis, which could be sufficient to attract clinical attention [ 46 , 47 ].…”

Section: Discussionmentioning

confidence: 99%

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The Reliability of Two- and Three-Dimensional Cephalometric Measurements: A CBCT Study

Teixeira

Tanna

et al. 2021

Diagnostics

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Cephalometry is a standard diagnostic tool in orthodontic and orthognathic surgery fields. However, built-in magnification from the cephalometric machine produces double images from left- and right-side craniofacial structures on the film, which poses difficulty for accurate cephalometric tracing and measurements. The cone-beam computed tomography (CBCT) images not only allow three-dimensional (3D) analysis, but also enable the extraction of two-dimensional (2D) images without magnification. To evaluate the most reliable cephalometric analysis method, we extracted 2D lateral cephalometric images with and without magnification from twenty full-cranium CBCT datasets; images were extracted with magnification to mimic traditional lateral cephalograms. Cephalometric tracings were performed on the two types of extracted 2D lateral cephalograms and on the reconstructed 3D full cranium images by two examiners. The intra- and inter-examiner intraclass correlation coefficients (ICC) were compared between linear and angular parameters, as well as between CBCT datasets of adults and children. Our results showed that overall, tracing on 2D cephalometric images without magnification increased intra- and inter-examiner reliability, while 3D tracing reduced inter-examiner reliability. Angular parameters and children’s images had the lowest inter- and intra-examiner ICCs compared with adult samples and linear parameters. In summary, using lateral cephalograms extracted from CBCT without magnification for tracing/analysis increased reliability. Special attention is needed when analyzing young patients’ images and measuring angular parameters.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The Reliability of Two- and Three-Dimensional Cephalometric Measurements: A CBCT Study

Teixeira

Tanna

et al. 2021

Diagnostics

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“…The main goal of cephalometric landmarking is to perform linear and angular measurements that will ultimately provide clinical guidance. In order to evaluate the clinical usefulness of our DL-based method, our outcome set included lateral cephalometric measurements commonly found in R&R studies (van Bunningen et al 2022) as well as 3 additional frontal measurements. We chose to perform 2D cephalometric measurements based on orthogonally projected landmarks because 3D cephalometric analysis remains complex and thus beyond the scope of this study (Gateno et al 2011).…”

Section: Discussionmentioning

confidence: 99%

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

et al. 2022

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The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set ( n = 160) and a test set ( n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator( n = 178) or twice by 3 operators ( n = 20, test set only). After inference on the test set, 1 CT scan showed “very low” confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were −0.3 ± 1.3° and −0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland–Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

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“…The main goal of cephalometric landmarking is to perform linear and angular measurements which will ultimately provide clinical guidance. In order to evaluate the clinical usefulness of our DL-based method, our outcome set included lateral cephalometric measurements commonly found in R&R studies (van Bunningen et al 2022) as well as three additional frontal measurements. We chose to perform 2D cephalometric measurements based on orthogonally projected landmarks because 3D cephalometric analysis remains complex, and thus beyond the scope of this study (Gateno et al 2011).…”

Section: Discussionmentioning

confidence: 99%

Automatic Three-Dimensional Cephalometric Landmarking via Deep Learning

Dot

Schouman

Chang

et al. 2022

Preprint

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The increasing use of three-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform tedious manual landmarking. In this study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted in 33 landmarks, manually localized once by 1 operator (n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, one CT scan showed "very low" confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3mm, while success detection rates for 2.0, 2.5 and 3.0mm were 90.4%, 93.6% and 95.4%, respectively. Mean errors were -0.3 ± 1.3° and -0.1 ± 0.7mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland-Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

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Precision of orthodontic cephalometric measurements on ultra low dose-low dose CBCT reconstructed cephalograms

Cited by 16 publications

References 25 publications

The Reliability of Two- and Three-Dimensional Cephalometric Measurements: A CBCT Study

The Reliability of Two- and Three-Dimensional Cephalometric Measurements: A CBCT Study

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

Automatic Three-Dimensional Cephalometric Landmarking via Deep Learning

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