Digital Dentistry - Группа авторов

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Güttler, F., Wendt, S. et al. (2018). Forensic odontology: automatic identification of persons comparing antemortem and postmortem panoramic radiographs using computer vision. Rofo 190 (12): 1152–1158.

      26 26 de Medeiros, F.C.F.L., Kudo, G.A.H., Leme, B.G. et al. (2018). Dental implants in patients with osteoporosis: a systematic review with meta‐analysis. Int. J. Oral Maxillofac. Surg. 47: 480–491.

      27 27 Kavitha, M.S., An, S.Y., An, C.H. et al. (2015). Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 119 (3): 346–356.

      28 28 Kavitha, M.S., Ganesh Kumar, P., Park, S.Y. et al. (2016). Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches. Dentomaxillofac. Radiol. 45 (7): 20160076.

      29 29 Tanny, L., Huang, B., Shaweesh, A., and Currie, G. (2021). Characterisation of anterior open bite in primary school‐aged children: a preliminary study with artificial neural network analysis. Int. J. Paediatr. Dent. 31 (5): 576–582.

      30 30 Auconi, P., Caldarelli, G., Scala, A. et al. (2011). A network approach to orthodontic diagnosis. Orthod. Craniofac. Res. 14 (4): 189–197.

      31 31 Kwak, G.H., Kwak, E.J., Song, J.M. et al. (2020). Automatic mandibular canal detection using a deep convolutional neural network. Sci. Rep. 10 (1): 5711.

      32 32 Xu, J., Liu, J., Zhang, D. et al. (2021). Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates. Int. J. Comput. Assist. Radiol. Surg. 16: 1785–1794.

      33 33 Kurt Bayrakdar, S., Orhan, K., Bayrakdar, I.S. et al. (2021). A deep learning approach for dental implant planning in cone‐beam computed tomography images. BMC Med. Imaging 21 (1): 86.

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