Page 87 - C:\Users\lpnan\Documents\Flip PDF\revue ODF 1 2021\

Deep Learning in orthodontics: going forward on a new patient-practitioner relationship

31. Montúfar, J.; Romero, M.; Scougall-Vilchis, R.J. Hybrid approach for automatic cephalometric landmark annotation on cone-beam
computed tomography volumes. Am. J. Orthod. Dentofac. Orthop. Off. Publ. Am. Assoc. Orthod. Its Const. Soc. Am. Board Orthod.
2018, 154, 140–150, doi:10.1016/j.ajodo.2017.08.028.
32. OECD L’intelligence artificielle dans la société. 2019. https://www.oecd-ilibrary.org/sites/b7f8cd16-fr/index.html?itemId=/
content/publication/b7f8cd16-fr.
33. Okada, K.; Rysavy, S.; Flores, A.; Linguraru, M.G. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT
scans. Med. Phys. 2015, 42, 1653–1665, doi:10.1118/1.4914418.
34. Orhan, K.; Bayrakdar, I.S.; Ezhov, M.; Kravtsov, A.; Özyürek, T. Evaluation of artificial intelligence for detecting periapical pathosis on
cone-beam computed tomography scans. Int. Endod. J. 2020, 53, 680-689, doi:10.1111/iej.13265.
35. Schuhbaeck, A.; Otaki, Y.; Achenbach, S.; Schneider, C.; Slomka, P.; Berman, D.S.; Dey, D. Coronary calcium scoring from contrast
coronary CT angiography using a semiautomated standardized method. J. Cardiovasc. Comput. Tomogr. 2015, 9, 446-453,
doi:10.1016/j.jcct.2015.06.001.
36. Schwendicke, F.; Golla, T.; Dreher, M.; Krois, J. Convolutional neural networks for dental image diagnostics: A scoping review. J. Dent.
2019, 91, 103226, doi:10.1016/j.jdent.2019.103226.
37. Shahidi, S.; Bahrampour, E.; Soltanimehr, E.; Zamani, A.; Oshagh, M.; Moattari, M.; Mehdizadeh, A. The accuracy of a
designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med. Imaging 2014, 14, 32,
doi:10.1186/1471-2342-14-32.
38. Shannon, C.E. Programming a Computer for Playing Chess. In Computer Chess Compendium; Levy, D., Ed.; Springer: New York, NY,
1988; pp. 2–13 ISBN 978-1-4757-1968-0.
39. Suhail, Y.; Upadhyay, M.; Chhibber, A.; Kshitiz, null machine learning for the Diagnosis of Orthodontic Extractions: A Computational
Analysis Using Ensemble Learning. Bioeng. Basel Switz. 2020, 7, doi:10.3390/bioengineering7020055.
40. Thanathornwong, B. Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment. Healthc. Inform.
Res. 2018, 24, 22–28, doi:10.4258/hir.2018.24.1.22.
41. Torosdagli, N.; Liberton, D.K.; Verma, P.; Sincan, M.; Lee, J.S.; Bagci, U. Deep Geodesic Learning for Segmentation and Anatomical
Landmarking. IEEE Trans. Med. Imaging 2019, 38, 919-931, doi:10.1109/TMI.2018.2875814.
42. Turing, A.M. I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind 1950, LIX, 433-460, doi:10.1093/mind/LIX.236.433.
43. Villani, C.; Schoenauer, M.; Bonnet, Y.; Berthet, C.; Cornut, A.-C.; Levin, F.; Rondepierre, B. Donner un sens à l’intelligence artificielle :
Pour une stratégie nationale et européenne; 2018;
44. Wirtz, A.; Mirashi, S.G.; Wesarg, S. Automatic Teeth Segmentation in Panoramic X-Ray Images Using a Coupled Shape Model in
Combination with a Neural Network. In Proceedings of the Medical Image Computing and Computer Assisted Intervention – MICCAI
2018; Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G., Eds.; Springer International Publishing: Cham,
2018; pp. 712-719.
45. World Intellectual Property WIPO Technology Trends 2019 - Artificial Intelligence; WIPO, 2019; ISBN 978-92-805-3007-0.
46. Xu, X.; Liu, C.; Zheng, Y. 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. IEEE Trans. Vis. Comput.
Graph. 2019, 25, 2336-2348, doi:10.1109/TVCG.2018.2839685.

47. Yilmaz, E.; Kayikcioglu, T.; Kayipmaz, S. Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone
beam computed tomography. Comput. Methods Programs Biomed. 2017, 146, 91-100, doi:10.1016/j.cmpb.2017.05.012.
48. Zanjani, F.G.; Moin, D.A.; Verheij, B.; Claessen, F.; Cherici, T.; Tan, T.; With, P.H.N. de Deep Learning Approach to Semantic
Segmentation in 3D Point Cloud Intra-oral Scans of Teeth. In Proceedings of the International Conference on Medical Imaging with
Deep Learning; PMLR, 2019; pp. 557-571.
49. Zhang, K.; Wu, J.; Chen, H.; Lyu, P. An effective teeth recognition method using label tree with cascade network structure. Comput.
Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc. 2018, 68, 61–70, doi:10.1016/j.compmedimag.2018.07.001.
50. Zhong, F.; Xing, J.; Li, X.; Liu, X.; Fu, Z.; Xiong, Z.; Lu, D.; Wu, X.; Zhao, J.; Tan, X.; et al. Artificial intelligence in drug design. Sci.
China Life Sci. 2018, 61, 1191-1204, doi:10.1007/s11427-018-9342-2.
51. Zhong, Z.; Li, J.; Zhang, Z.; Jiao, Z.; Gao, X. An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms.
In Proceedings of the Medical Image Computing and Computer Assisted Intervention – MICCAI 2019; Shen, D., Liu, T., Peters, T.M.,
Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A., Eds.; Springer International Publishing: Cham, 2019; pp. 540-548.

RODF 2021;55(1):73-87 87

21/01/2021 16:19
RODF-2021-1.indb 87 21/01/2021 16:19
RODF-2021-1.indb 87