Abstract
The aim of this systematic review was to assess the accuracy and reliability of automatic
landmarking for cephalometric analysis of three-dimensional craniofacial images. We
searched for studies that reported results of automatic landmarking and/or measurements
of human head computed tomography or cone beam computed tomography scans in MEDLINE,
Embase and Web of Science until March 2019. Two authors independently screened articles
for eligibility. Risk of bias and applicability concerns for each included study were
assessed using the QUADAS-2 tool. Eleven studies with test dataset sample sizes ranging
from 18 to 77 images were included. They used knowledge-, atlas- or learning-based
algorithms to landmark two to 33 points of cephalometric interest. Ten studies measured
mean localization errors between manually and automatically detected landmarks. Depending
on the studies and the landmarks, mean errors ranged from <0.50 mm to>5 mm. The two best-performing algorithms used a deep learning method and reported mean
errors <2 mm for every landmark, approximating results of operator variability in manual landmarking.
Risk of bias regarding patient selection and implementation of the reference standard
were found, therefore the studies might have yielded overoptimistic results. The robustness
of these algorithms needs to be more thoroughly tested in challenging clinical settings.
PROSPERO registration number: CRD42019119637.
Key words
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References
- Automatic cephalometric analysis: a systematic review.Angle Orthod. 2008; 78: 145-151https://doi.org/10.2319/120506-491.1
- Accuracy and reliability of craniometric measurements on lateral cephalometry and 3D measurements on CBCT scans.Angle Orthod. 2011; 81: 26-35https://doi.org/10.2319/032210-166.1
- 3D CT-based cephalometric analysis: 3D cephalometric theoretical concept and software.Neuroradiology. 2006; 48: 853-862https://doi.org/10.1007/s00234-006-0140-x
- Three-dimensional architectural and structural analysis—a transition in concept and design from Delaire’s cephalometric analysis.Int J Oral Maxillofac Surg. 2014; 43: 1154-1160https://doi.org/10.1016/j.ijom.2014.03.012
- Three-dimensional cephalometric analysis in orthodontics: a systematic review.Orthod Craniofac Res. 2014; 17: 69-91https://doi.org/10.1111/ocr.12034
- CBCT in orthodontics: assessment of treatment outcomes and indications for its use.Dentomaxillofac Radiol. 2015; 4420140282https://doi.org/10.1259/dmfr.20140282
- Three-Dimensional Cephalometry: A Color Atlas and Manual.Springer-Verlag, Berlin Heidelberg2006
- Experimental and clinical assessment of three-dimensional cephalometry: a systematic review.J Craniomaxillofac Surg. 2014; 42: 1795-1801https://doi.org/10.1016/j.jcms.2014.06.017
- Intraexaminer and interexaminer reliabilities of landmark identification on digitized lateral cephalograms and formatted 3-dimensional cone-beam computerized tomography images.Am J Orthod Dentofacial Orthop. 2010; 137: 598-604https://doi.org/10.1016/j.ajodo.2008.07.018
- Precision of identifying cephalometric landmarks with cone beam computed tomography in vivo.Eur J Orthod. 2013; 35: 38-44https://doi.org/10.1093/ejo/cjr050
- Reliability of different three-dimensional cephalometric landmarks in cone-beam computed tomography: a systematic review.Angle Orthod. 2018; 89: 317-332https://doi.org/10.2319/042018-302.1
- Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms.Sci Rep. 2016; 6: 33581
- A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images.Int J Comput Assist Radiol Surg. 2015; 10: 1737-1752https://doi.org/10.1007/s11548-015-1173-6
- Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks.in: Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science. vol 10434. Springer, Cham2017
- QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.Ann Intern Med. 2011; 155: 529https://doi.org/10.7326/0003-4819-155-8-201110180-00009
- Chapter 9: Assessing methodological quality.in: Deeks J.J. Bossuyt P.M. Gatsonis C. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy, Version 1.0.0. The Cochrane Collaboration, 2009
- The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images.BMC Med Imaging. 2014; 14: 32https://doi.org/10.1186/1471-2342-14-32
- Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model and multiscale statistical features.IEEE Trans Biomed Eng. 2016; 63: 1820-1829https://doi.org/10.1109/TBME.2015.2503421
- Computer-aided cephalometric landmark annotation for CBCT data.Int J Comput Assist Radiol Surg. 2017; 12: 113-121https://doi.org/10.1007/s11548-016-1453-9
- Automated human skull landmarking with 2D Gabor wavelets.Phys Med Biol. 2018; 63105011
- Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes.Am J Orthod Dentofac Orthop. 2018; 154: 140-150https://doi.org/10.1016/j.ajodo.2017.08.028
- Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull.Dentommaxillofacial Radiol. 2018; 4720170054https://doi.org/10.1259/dmfr.20170054
- Deep geodesic learning for segmentation and anatomical landmarking.IEEE Trans Med Imaging. 2019; 38: 919-931https://doi.org/10.1109/TMI.2018.2875814
- Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3d CT data.in: Leal-Taixé L. Roth S. Computer Vision – ECCV 2018 Workshops. Springer International Publishing, 2019: 470-484
- Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm.Int J Comput Assist Radiol Surg. 2016; 11: 1297-1309https://doi.org/10.1007/s11548-015-1334-7
- The efficacy of diagnostic imaging.Med Decis Mak Int J Soc Med Decis Mak. 1991; 11: 88-94https://doi.org/10.1177/0272989X9101100203
- Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach.Sci Rep. 2018; 8https://doi.org/10.1038/s41598-017-18993-5
- A benchmark for comparison of dental radiography analysis algorithms.Med Image Anal. 2016; 31: 63-76https://doi.org/10.1016/j.media.2016.02.004
- Cone Beam CT for dental and maxillofacial radiology (evidence based guidelines).European Commission, 2012
Article info
Publication history
Published online: March 10, 2020
Accepted:
February 24,
2020
Identification
Copyright
© 2020 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.
