Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees

Lateral cephalograms provide important information regarding dental, skeletal, and soft-tissue parameters that are critical for orthodontic diagnosis and treatment planning. Several machine learning methods have previously been used for the automated localization of diagnostically relevant landmarks...

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Detalles Bibliográficos
Autores principales: Suhail, Sameera, Harris, Kayla, Sinha, Gaurav, Schmidt, Maayan, Durgekar, Sujala, Mehta, Shivam, Upadhyay, Madhur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687964/
https://www.ncbi.nlm.nih.gov/pubmed/36354530
http://dx.doi.org/10.3390/bioengineering9110617

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687964/
https://www.ncbi.nlm.nih.gov/pubmed/36354530
http://dx.doi.org/10.3390/bioengineering9110617

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