Hakobyan G. New trends oral and maxillofacial surgery past two decades. J Surg Curr Trend Innov. 2020;13:S1006.

Google Scholar 

Hammoudeh JA, Howell LK, Boutros S, Scott MA, Urata MM. Current status of surgical planning for orthognathic surgery: traditional methods versus 3D surgical planning. Plastic Reconstr Surg Global Open. 2015;3(2):e307. https://doi.org/10.1097/GOX.0000000000000184.

Article  Google Scholar 

Mathew N, Gandhi S, Singh I, Solanki M, Bedi NS. 3D models revolutionizing surgical outcomes in oral and maxillofacial surgery: experience at our center. J Maxillofac Oral Surg. 2020;19(2):208–16. https://doi.org/10.1007/s12663-019-01275-0.

Article  PubMed  Google Scholar 

Nilsson J, Hindocha N, Thor A. Time matters – Differences between computer-assisted surgery and conventional planning in cranio-maxillofacial surgery: a systematic review and meta-analysis. J Cranio-Maxillofac Surg. 2020;48(2):132–40. https://doi.org/10.1016/j.jcms.2019.11.024.

Article  Google Scholar 

Rogers-Vizena CR, FlathSporn S, Daniels KM, Padwa BL, Weinstock P. Cost-benefit analysis of three-dimensional craniofacial models for midfacial distraction: a pilot study. Cleft Palate-Craniofac J. 2017;54(5):612–7. https://doi.org/10.1597/15-281.

Article  PubMed  Google Scholar 

Wallner J, Schwaiger M, Streckbein P, Zemann W. 9 - Clinical practice (Graz, Austria and Gießen, Germany). In: Egger J, Chen X, editors. Computer-aided oral and maxillofacial surgery. San Diego: Academic Press; 2021. p. 201–22.

Chapter  Google Scholar 

Ganry L, Hersant B, Quilichini J, Leyder P, Meningaud JP. Use of the 3D surgical modelling technique with open-source software for mandibular fibula free flap reconstruction and its surgical guides. J Stomatol Oral Maxillofac Surg. 2017;118(3):197–202. https://doi.org/10.1016/j.jormas.2017.03.002.

Article  CAS  PubMed  Google Scholar 

Lee Ventola C. Medical applications for 3D printing: current and projected uses. P & T. 2014;39(10):704–11.

Google Scholar 

Andersen SAW, Varadarajan VV, Moberly AC, Hittle B, Powell KA, Wiet GJ. Patient-specific virtual temporal bone simulation based on clinical cone-beam computed tomography. Laryngoscope. 2021;131(8):1855–62. https://doi.org/10.1002/lary.29542.

Article  PubMed  Google Scholar 

Werz SM, Zeichner SJ, Berg B-I, Zeilhofer H-F, Thieringer F. 3D printed surgical simulation models as educational tool by maxillofacial surgeons. Eur J Dent Educ. 2018;22(3):e500–5. https://doi.org/10.1111/eje.12332.

Article  CAS  PubMed  Google Scholar 

Ma Y, Xie L, Yang B, Tian W. Three-dimensional printing biotechnology for the regeneration of the tooth and tooth-supporting tissues. Biotechnol Bioeng. 2019;116(2):452–68. https://doi.org/10.1002/bit.26882.

Article  CAS  PubMed  Google Scholar 

Li J, Erdt M, Janoos F, Chang T, Egger J. 1 - Medical image segmentation in oral-maxillofacial surgery. In: Egger J, Chen X, editors. Computer-aided oral and maxillofacial surgery. San Diego: Academic Press; 2021. p. 1–27.

Google Scholar 

Wallner J, Schwaiger M, Hochegger K, Gsaxner C, Zemann W, Egger J. A review on multiplatform evaluations of semi-automatic open-source based image segmentation for cranio-maxillofacial surgery. Comput Methods Programs Biomed. 2019;182: 105102. https://doi.org/10.1016/j.cmpb.2019.105102.

Article  PubMed  Google Scholar 

Weiss R, Read-Fuller A. Cone beam computed tomography in oral and maxillofacial surgery: an evidence-based review. Dent J. 2019;7(2):2. https://doi.org/10.3390/dj7020052.

Article  Google Scholar 

Serte S, Serener A, Al-Turjman F. Deep learning in medical imaging: a brief review. Trans Emerg Telecommun Technol. 2022;33(10): e4080. https://doi.org/10.1002/ett.4080.

Article  Google Scholar 

Beheshtian E, Putman K, Santomartino SM, Parekh VS, Yi PH. Generalizability and bias in a deep learning pediatric bone age prediction model using hand radiographs. Radiology. 2023;306(2): e220505. https://doi.org/10.1148/radiol.220505.

Article  PubMed  Google Scholar 

Yu AC, Mohajer B, Eng J. External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiol Artif Intell. 2022;4(3): e210064. https://doi.org/10.1148/ryai.210064.

Article  PubMed  PubMed Central  Google Scholar 

Ricci Lara MA, Echeveste R, Ferrante E. Addressing fairness in artificial intelligence for medical imaging. Nat Commun. 2022;13(1):1. https://doi.org/10.1038/s41467-022-32186-3.

Article  CAS  Google Scholar 

Schwendicke F, et al. Artificial intelligence in dental research: checklist for authors, reviewers, readers. J Dent. 2021;107: 103610. https://doi.org/10.1016/j.jdent.2021.103610.

Article  Google Scholar 

World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191–4.

Article  Google Scholar 

Fedorov A, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30(9):1323–41. https://doi.org/10.1016/j.mri.2012.05.001.

Article  PubMed  PubMed Central  Google Scholar 

Chollet F. “keras,” 2015.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation.” arXiv [cited 2022 Feb 6]. http://arxiv.org/abs/1505.04597

Siddique N, Paheding S, Elkin CP, Devabhaktuni V. U-Net and its variants for medical image segmentation: a review of theory and applications. IEEE Access. 2021;9:82031–57. https://doi.org/10.1109/ACCESS.2021.3086020.

Article  Google Scholar 

Schindelin J, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676–82.

Article  CAS  PubMed  Google Scholar 

Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Measur. 1960. https://doi.org/10.1177/001316446002000104.

Article  Google Scholar 

McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276–82.

Article  PubMed  Google Scholar 

Qiu B, et al. Automatic segmentation of mandible from conventional methods to deep learning—a review. J Pers Med. 2021;11(7):7. https://doi.org/10.3390/jpm11070629.

Article  Google Scholar 

Amorim PHJ, Moraes TF, Da Silva JVL, Pedrini H. Mandible Segmentation from CT and CBCT Images Based on a Patch-Based Convolutional Neural Network. In: 2023 International Conference on Machine Learning and Applications (ICMLA), Dec. 2023. pp. 38–44. https://doi.org/10.1109/ICMLA58977.2023.00014.

Dot G et al. DentalSegmentator: robust deep learning-based CBCT image segmentation. medRxiv, p. 2024.03.18.24304458, 18, 2024. https://doi.org/10.1101/2024.03.18.24304458.

Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13(4):543–63. https://doi.org/10.1016/j.media.2009.05.004.

Article  PubMed  Google Scholar 

Cuadros Linares O, Bianchi J, Raveli D, Batista Neto J, Hamann B. Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering. Vis Comput. 2019;35(10):1461–74. https://doi.org/10.1007/s00371-018-1511-0.

Article  Google Scholar 

Wang H, Minnema J, Batenburg KJ, Forouzanfar T, Hu FJ, Wu G. Multiclass CBCT image segmentation for orthodontics with deep learning. J Dent Res. 2021;100(9):943–9. https://doi.org/10.1177/00220345211005338.

Article  CAS  PubMed  Google Scholar 

Lo Giudice A, Ronsivalle V, Spampinato C, Leonardi R. Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs). Orthod Craniofac Res. 2021;24(S2):100–7. https://doi.org/10.1111/ocr.12536.

Article  PubMed  Google Scholar 

Liu Q, et al. SkullEngine: a multi-stage CNN framework for collaborative CBCT image segmentation and landmark detection. Mach Learn Med Imaging. 2021;12966:606–14. https://doi.org/10.1007/978-3-030-87589-3_62.

Article  PubMed  PubMed Central  Google Scholar 

Verhelst P-J, et al. Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography. J Dent. 2021;114: 103786. https://doi.org/10.1016/j.jdent.2021.103786.

Article  CAS  PubMed  Google Scholar 

Cui Z, et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat Commun. 2022;13(1):1. https://doi.org/10.1038/s41467-022-29637-2.

Article  CAS  Google Scholar 

Liu Y, et al. Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images. Int J Oral Sci. 2024;16(1):1–12. https://doi.org/10.1038/s41368-024-00294-z.