Validation of Machine Learning Models for Craniofacial Growth Prediction

This study identified the most accurate model for predicting longitudinal craniofacial growth in a Japanese population using statistical methods and machine learning. Longitudinal lateral cephalometric radiographs were collected from 59 children (27 boys and 32 girls) with no history of orthodontic...

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Detalles Bibliográficos
Autores principales: Kim, Eungyeong, Kuroda, Yasuhiro, Soeda, Yoshiki, Koizumi, So, Yamaguchi, Tetsutaro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647775/
https://www.ncbi.nlm.nih.gov/pubmed/37958265
http://dx.doi.org/10.3390/diagnostics13213369

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647775/
https://www.ncbi.nlm.nih.gov/pubmed/37958265
http://dx.doi.org/10.3390/diagnostics13213369

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