Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Kapetanović A, Oosterkamp BCM, Lamberts AA, Schols JGJH (2021) Orthodontic radiology: development of a clinical practice guideline. Radiol Med (Torino) 126:72–82. https://doi.org/10.1007/s11547-020-01219-6

Article  PubMed  Google Scholar 

Hwang H-W, Moon J-H, Kim M-G et al (2021) Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod 91:329–335. https://doi.org/10.2319/021220-100.1

Article  PubMed  Google Scholar 

Hans MG, Palomo JM, Valiathan M (2015) History of imaging in orthodontics from Broadbent to cone-beam computed tomography. Am J Orthod Dentofacial Orthop 148:914–921. https://doi.org/10.1016/j.ajodo.2015.09.007

Article  PubMed  Google Scholar 

Farronato G, Salvadori S, Nolet F et al (2014) Assessment of inter- and intra-operator cephalometric tracings on cone beam CT radiographs: comparison of the precision of the cone beam CT versus the latero-lateral radiograph tracing. Prog Orthod 15:1. https://doi.org/10.1186/2196-1042-15-1

Article  PubMed  Google Scholar 

Leonardi R, Annunziata A, Caltabiano M (2008) Landmark identification error in posteroanterior cephalometric radiography: a systematic review. Angle Orthod 78:761–765. https://doi.org/10.2319/0003-3219(2008)078[0761:LIEIPC]2.0.CO;2

Article  PubMed  Google Scholar 

Li C, Teixeira H, Tanna N et al (2021) The reliability of two- and three-dimensional cephalometric measurements: a CBCT study. Diagnostics 11:2292. https://doi.org/10.3390/diagnostics11122292

Article  PubMed  Google Scholar 

Farronato M, Maspero C, Abate A et al (2020) 3D cephalometry on reduced FOV CBCT: skeletal class assessment through AF-BF on Frankfurt plane—validity and reliability through comparison with 2D measurements. Eur Radiol 30:6295–6302. https://doi.org/10.1007/s00330-020-06905-7

Article  PubMed  Google Scholar 

Corbella S, Srinivas S, Cabitza F (2021) Applications of deep learning in dentistry. Oral Surg Oral Med Oral Pathol Oral Radiol 132:225–238. https://doi.org/10.1016/j.oooo.2020.11.003

Article  PubMed  Google Scholar 

Khanagar SB, Al-ehaideb A, Maganur PC et al (2021) Developments, application, and performance of artificial intelligence in dentistry––a systematic review. J Dent Sci 16:508–522. https://doi.org/10.1016/j.jds.2020.06.019

Article  PubMed  Google Scholar 

Bichu YM, Hansa I, Bichu AY et al (2021) Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod 22:18. https://doi.org/10.1186/s40510-021-00361-9

Article  PubMed  Google Scholar 

Płotka S, Włodarczyk T, Szczerba R, et al (2021) Convolutional neural networks in orthodontics: a review

Dot G, Rafflenbeul F, Arbotto M et al (2020) Accuracy and reliability of automatic three-dimensional cephalometric landmarking. Int J Oral Maxillofac Surg 49:1367–1378. https://doi.org/10.1016/j.ijom.2020.02.015

Article  CAS  PubMed  Google Scholar 

Schwendicke F, Chaurasia A, Arsiwala L et al (2021) Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Clin Oral Investig 25:4299–4309. https://doi.org/10.1007/s00784-021-03990-w

Article  PubMed  Google Scholar 

Lee SM, Kim HP, Jeon K et al (2019) Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning. Phys Med Biol 64:055002. https://doi.org/10.1088/1361-6560/ab00c9

Article  PubMed  Google Scholar 

Torosdagli N, Liberton DK, Verma P et al (2019) Deep geodesic learning for segmentation and anatomical landmarking. IEEE Trans Med Imaging 38:919–931. https://doi.org/10.1109/TMI.2018.2875814

Article  PubMed  Google Scholar 

O’Neil AQ, Kascenas A, Henry J, et al (2018) Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data

Gupta A, Kharbanda OP, Sardana V et al (2015) A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 10:1737–1752. https://doi.org/10.1007/s11548-015-1173-6

Article  PubMed  Google Scholar 

Montúfar J, Romero M, Scougall-Vilchis RJ (2018) Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop 154:140–150. https://doi.org/10.1016/j.ajodo.2017.08.028

Article  PubMed  Google Scholar 

Neelapu BC, Kharbanda OP, Sardana V et al (2018) Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofacial Radiol 47:20170054. https://doi.org/10.1259/dmfr.20170054

Article  Google Scholar 

Shahidi S, Bahrampour E, Soltanimehr E et al (2014) The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med Imaging 14:32. https://doi.org/10.1186/1471-2342-14-32

Article  PubMed  Google Scholar 

Codari M, Caffini M, Tartaglia GM et al (2017) Computer-aided cephalometric landmark annotation for CBCT data. Int J Comput Assist Radiol Surg 12:113–121. https://doi.org/10.1007/s11548-016-1453-9

Article  PubMed  Google Scholar 

Zhang J, Gao Y, Wang L et al (2016) Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model and multiscale statistical features. IEEE Trans Biomed Eng 63:1820–1829. https://doi.org/10.1109/TBME.2015.2503421

Article  PubMed  Google Scholar 

de Jong MA, Gül A, de Gijt JP et al (2018) Automated human skull landmarking with 2D Gabor wavelets. Phys Med Biol 63:105011. https://doi.org/10.1088/1361-6560/aabfa0

Article  PubMed  Google Scholar 

Zhang J, Liu M, Wang L et al (2017) Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks. In: Descoteaux M, Maier-Hein L, Franz A et al (eds) Medical image computing and computer-assisted intervention − MICCAI 2017. Springer, Cham, pp 720–728

Chapter  Google Scholar 

Moher D (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151:264. https://doi.org/10.7326/0003-4819-151-4-200908180-00135

Article  PubMed  Google Scholar 

Cumpston M, Li T, Page MJ et al (2019) Updated guidance for trusted systematic reviews a new edition of the cochrane handbook for systematic reviews of interventions. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.ED000142

Article  PubMed  Google Scholar 

Whiting PF (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529. https://doi.org/10.7326/0003-4819-155-8-201110180-00009

Article  PubMed  Google Scholar 

Higgins JPT, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21:1539–1558. https://doi.org/10.1002/sim.1186

Article  PubMed  Google Scholar 

Chen R, Ma Y, Liu L et al (2021) Semi-supervised anatomical landmark detection via shape-regulated self-training. Neurocomputing. 471:335–345

Article  Google Scholar 

Yun HS, Jang TJ, Lee SM et al (2020) Learning-based local-to-global landmark annotation for automatic 3D cephalometry. Phys Med Biol 65:085018. https://doi.org/10.1088/1361-6560/ab7a71

Article  PubMed  Google Scholar 

Yun HS, Hyun CM, Baek SH, et al (2020) Automated 3D cephalometric landmark identification using computerized tomography

Yun HS, Hyun CM, Baek SH et al (2022) A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography. PLOS ONE 17:e0275114. https://doi.org/10.1371/journal.pone.0275114

Article  CAS  PubMed  Google Scholar 

Nishimoto S, Saito T, Ishise H et al (2021) Three-dimensional cranio-facial landmark detection in CT slices from a publicly available database, using multi-phased regression networks on a personal computer. Radiol Imaging 1:232

Google Scholar 

Ma Q, Kobayashi E, Fan B et al (2020) Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery. Int J Med Robot. https://doi.org/10.1002/rcs.2093

Article  PubMed  Google Scholar 

Lian C, Wang F, Deng HH et al (2020) Multi-task dynamic transformer network for concurrent bone segmentation and large-scale landmark localization with dental CBCT. In: Martel AL, Abolmaesumi P, Stoyanov D et al (eds) Medical image computing and computer assisted intervention – MICCAI 2020. Springer, Cham, pp 807–816

Chapter  Google Scholar 

Kang SH, Jeon K, Kang S-H, Lee S-H (2021) 3D cephalometric landmark detection by multiple stage deep reinforcement learning. Sci Rep 11:17509. https://doi.org/10.1038/s41598-021-97116-7

Article  CAS  PubMed  Google Scholar 

Liu Q, Deng H, Lian C et al (2021) SkullEngine: a multi-stage CNN framework for collaborative CBCT image segmentation and landmark detection. In: Lian C, Cao X, Rekik I et al (eds) Machine learning in medical imaging. Springer, Cham, pp 606–614

Chapter  Google Scholar 

Dot G, Schouman T, Chang S, et al (2022) Three-Dimensional Cephalometric Landmarking and Analysis of Craniomaxillofacial CT scans via Deep Learning

Chen R, Ma Y, Chen N et al (2022) Structure-aware long short-term memory network for 3D cephalometric landmark detection. IEEE Trans Med Imaging 41:1791–1801. https://doi.org/10.1109/TMI.2022.3149281

Article  PubMed  Google Scholar 

Lang Y, Lian C, Xiao D et al (2022) Localization of craniomaxillofacial landmarks on CBCT images using 3D mask R-CNN and local dependency learning. IEEE Trans Med Imaging 41:2856–2866. https://doi.org/10.1109/TMI.2022.3174513

Article  PubMed  Google Scholar 

Chen X, Lian C, Deng HH et al (2021) Fast and accurate craniomaxillofacial landmark detection via 3D Faster R-CNN. IEEE Trans Med Imaging 40:3867–3878. https://doi.org/10.1109/TMI.2021.3099509

Article  PubMed  Google Scholar 

Palazzo S, Bellitto G, Prezzavento L, et al (2021) Deep multi-stage model for automated landmarking of craniomaxillofacial CT Scans. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, Milan, Italy, pp 9982–9987

Zhang J, Liu M, Wang L et al (2021) Machine learning for craniomaxillofacial landmark digitization of 3D imaging. In: Ko C-C, Shen D, Wang L (eds) Machine learning in dentistry. Springer, Cham, pp 15–26

Chapter  Google Scholar 

Farronato M, Baselli G, Baldini B et al (2022) 3D cephalometric normality range: auto contractive maps (ACM) analysis in selected caucasian skeletal class I age groups. Bioengineering 9:216. https://doi.org/10.3390/bioengineering9050216

Article  PubMed  Google Scholar 

Baldini B, Cavagnetto D, Baselli G et al (2022) Cephalometric measurements performed on CBCT and reconstructed lateral cephalograms: a cross-sectional study providing a quantitative approach of differences and bias. BMC Oral Health 22:98. https://doi.org/10.1186/s12903-022-02131-3

Article  PubMed 

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