Zhou J, Zhou H, Pu L, Gao Y, Tang Z, Yang Y, et al. Development of an artificial intelligence system for the automatic evaluation of cervical vertebral maturation status. Diagnostics. 2021;11:2200. https://doi.org/10.3390/diagnostics11122200.

Article  PubMed  PubMed Central  Google Scholar 

Yu Y, Lin H, Meng J, Wei X, Guo H, Zhao Z. Deep transfer learning for modality classification of medical images. Information. 2017;8:91. https://doi.org/10.3390/info8030091.

Article  Google Scholar 

Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J. Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res. 2020;99:249–56. https://doi.org/10.1177/0022034520901715.

Article  CAS  PubMed  Google Scholar 

Xie L, Tang W, Izadikhah I, Chen X, Zhao Z, Zhao Y, et al. Intelligent quantitative assessment of skeletal maturation based on multi-stage model: a retrospective cone-beam CT study of cervical vertebrae. Oral Radiol. 2022;38:378–88. https://doi.org/10.1007/s11282-021-00566-y.

Article  PubMed  Google Scholar 

Wang C-W, Huang C-T, Lee J-H, Li C-H, Chang S-W, Siao M-J, Lai T-M, Ibragimov B, Vrtovec T, Ronneberger O, et al. A Benchmark for Comparison of Dental Radiography Analysis Algorithms. Med Image Anal. 2016;31:63–76. https://doi.org/10.1016/j.media.2016.02.004.

Article  CAS  PubMed  Google Scholar 

van Nistelrooij N, Schitter S, van Lierop P, Ghoul KE, König D, Hanisch M, et al. Detecting mandible fractures in CBCT scans using a 3-stage neural network. J Dent Res. 2024;103:1384–91. https://doi.org/10.1177/00220345241256618.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Turner S, Harrison JE, Sharif FN, Owens D, Millett DT. Orthodontic Treatment for Crowded Teeth in Children. Cochrane Database Syst Rev. 2021;12:CD003453. https://doi.org/10.1002/14651858.CD003453.pub2.

Article  PubMed  PubMed Central  Google Scholar 

Trakinienė G, Smailienė D, Kučiauskienė A. Evaluation of skeletal maturity using maxillary canine, mandibular second and third molar calcification stages. Eur J Orthod. 2016;38:398–403. https://doi.org/10.1093/ejo/cjv051.

Article  PubMed  Google Scholar 

Toodehzaeim M-H, Rafiei E, Hosseini S-H, Haerian A, Hazeri-Baqdad-Abad M. Association between mandibular second molars calcification stages in the panoramic images and cervical vertebral maturity in the lateral cephalometric images. J Clin Exp Dent. 2020;12:e148-53. https://doi.org/10.4317/jced.56402.

Article  PubMed  PubMed Central  Google Scholar 

Tehranchi A, Younessian F, Fadaei V, Arabgol F, Shirvani A. The effect of methylphenidate on cervical vertebral maturation and dental age in patients with attention deficit hyperactivity disorder. J Dent Shiraz. 2018;19:197–205.

PubMed  PubMed Central  Google Scholar 

Tayebi A, Tofangchiha M, Fard M-A, Gosili A. The relationship of mandibular radiomorphometric indices to skeletal age, chronological age and skeletal malocclusion type. J Clin Exp Dent. 2017;9:e970-5. https://doi.org/10.4317/jced.53819.

Article  PubMed  PubMed Central  Google Scholar 

Talpur S, Azim F, Rashid M, Syed SA, Talpur BA, Khan SJ. Uses of different machine learning algorithms for diagnosis of dental caries. J Healthc Eng. 2022;2022:5032435. https://doi.org/10.1155/2022/5032435.

Article  PubMed  PubMed Central  Google Scholar 

Tajmir SH, Lee H, Shailam R, Gale HI, Nguyen JC, Westra SJ, et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol. 2019;48:275–83. https://doi.org/10.1007/s00256-018-3033-2.

Article  PubMed  Google Scholar 

Szemraj A, Wojtaszek-Słomińska A, Racka-Pilszak B. Is the cervical vertebral maturation (CVM) method effective enough to replace the hand-wrist maturation (HWM) method in determining skeletal maturation?-a systematic review. Eur J Radiol. 2018;102:125–8. https://doi.org/10.1016/j.ejrad.2018.03.012.

Article  PubMed  Google Scholar 

Staley H, Shiraz A, Shreeve N, Bryant A, Martin-Hirsch PP, Gajjar K. Interventions targeted at women to encourage the uptake of cervical screening. Cochrane Database Syst Rev. 2021;9:CD002834. https://doi.org/10.1002/14651858.CD002834.pub3.

Article  PubMed  PubMed Central  Google Scholar 

Srkoc T, Mestrović S, Anić-Milosević S, Slaj M. Association between dental and skeletal maturation stages in Croatian subjects. Acta Clin Croat. 2015;54:445–52.

PubMed  Google Scholar 

Srinivasan B, Padmanabhan S, Chitharanjan AB. Constancy of cervical vertebral maturation indicator in adults: a cross-sectional study. Int Orthod. 2018;16:486–98. https://doi.org/10.1016/j.ortho.2018.06.015.

Article  PubMed  Google Scholar 

Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep learning in diagnosis of dental anomalies and diseases: a systematic review. Diagnostics. 2023;13:2512. https://doi.org/10.3390/diagnostics13152512.

Article  PubMed  PubMed Central  Google Scholar 

Singh T, Poterba T, Curtis D, Akil H, Al Eissa M, Barchas JD, et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature. 2022;604:509–16. https://doi.org/10.1038/s41586-022-04556-w.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shin H-C, Tenenholtz NA, Rogers JK, Schwarz CG, Senjem ML, Gunter JL, et al. Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks. In: Gooya A, Goksel O, Oguz I, Burgos N, editors. Proceedings of the Simulation and Synthesis in Medical Imaging. Cham: Springer International Publishing; 2018. p. 1–11.

Sella Tunis T, Masarwa M, Finkelstein T, Grinstein Koren O, Kats L, Manor Y, et al. The reliability of a modified three-stage cervical vertebrae maturation method for estimating skeletal growth in males and females. BMC Oral Health. 2024;24:1255. https://doi.org/10.1186/s12903-024-05028-5.

Article  PubMed  PubMed Central  Google Scholar 

Shafiq M, Gu Z. Deep residual learning for image recognition: a survey. Appl Sci. 2022;12:8972. https://doi.org/10.3390/app12188972.

Article  CAS  Google Scholar 

Shafi I, Fatima A, Afzal H, Díez I, Lipari V, Breñosa J, et al. A comprehensive review of recent advances in artificial intelligence for dentistry E-health. Diagnostics. 2023;13:2196. https://doi.org/10.3390/diagnostics13132196.

Article  PubMed  PubMed Central  Google Scholar 

Schwendicke F, Chaurasia A, Wiegand T, Uribe SE, Fontana M, Akota I, et al. Artificial intelligence for oral and dental healthcare: core education curriculum. J Dent. 2023;128:104363. https://doi.org/10.1016/j.jdent.2022.104363.

Article  PubMed  Google Scholar 

Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99:769–74. https://doi.org/10.1177/0022034520915714.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schoretsaniti L, Mitsea A, Karayianni K, Sifakakis I. Cervical vertebral maturation method: reproducibility and efficiency of chronological age estimation. Appl Sci. 2021;11:3160. https://doi.org/10.3390/app11073160.

Article  CAS  Google Scholar 

Schneider L, Rischke R, Krois J, Krasowski A, Büttner M, Mohammad-Rahimi H, et al. Federated vs local vs central deep learning of tooth segmentation on panoramic radiographs. J Dent. 2023;135:104556. https://doi.org/10.1016/j.jdent.2023.104556.

Article  PubMed  Google Scholar 

Sarker IH. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Comput Sci. 2022;3:158. https://doi.org/10.1007/s42979-022-01043-x.

Article  PubMed  PubMed Central  Google Scholar 

Sadeghi TS, Ourang SA, Sohrabniya F, Sadr S, Shobeiri P, Motamedian SR. Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis. BMC Oral Health. 2025;25:187. https://doi.org/10.1186/s12903-025-05482-9.

Article  PubMed  PubMed Central  Google Scholar 

Rodrigues JA, Krois J, Schwendicke F. Demystifying artificial intelligence and deep learning in dentistry. Braz Oral Res. 2021;35:e094. https://doi.org/10.1590/1807-3107bor-2021.vol35.0094.