Machine Learning for the Classification of Surgical Patients in Orthodontics
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49404-8_21
Reference22 articles.
1. Zhou, Z., Liu, F., Shen, S., Shang, L., Shang, L., Wang, X.: Prevalence of and factors affecting malocclusion in primary dentition among children Xi’an, China. BMC Oral Health 16(1), 1–11 (2016). https://doi.org/10.1186/S12903-016-0285-X
2. Almeida, A.B., Leite, I.C.G., Melgaço, C.A., Marques, L.S.: Dissatisfaction with dentofacial appearance and the normative need for orthodontic treatment: determinant factors. Dental Press J. Orthod. 19(3), 120–126 (2014). https://doi.org/10.1590/2176-9451.19.3.120-126.OAR
3. Jawad, Z., Bates, C., Hodge, T.: Who needs orthodontic treatment? Who gets it? And who wants it? Br. Dent. J. 218(3), 99–103 (2015). https://doi.org/10.1038/sj.bdj.2015.51
4. Gravely, J.F., Johnson, D.B.: Angle’s classification of malocclusion: an assessment of reliability. Br. J. Orthod. 1(3), 79–86 (1974). https://doi.org/10.1179/BJO.1.3.79
5. Santos, R.R., Nayme, J.G., Garbin, A.J., Saliba, N., Garbin, C.A., Moimaz, S.A.: Prevalence of malocclusion and related oral habits in 5- to 6-year-old children. Oral Health Prev. Dent. 10(4), 311–8 (2012). https://doi.org/10.3290/J.OHPD.A28901
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