Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network

PURPOSE: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer’s knowledge and expertise. This study aimed to explor...

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Detalles Bibliográficos
Autores principales: Khazaei, Maryam, Mollabashi, Vahid, Khotanlou, Hassan, Farhadian, Maryam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Academy of Oral and Maxillofacial Radiology 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530293/
https://www.ncbi.nlm.nih.gov/pubmed/36238705
http://dx.doi.org/10.5624/isd.20220016

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530293/
https://www.ncbi.nlm.nih.gov/pubmed/36238705
http://dx.doi.org/10.5624/isd.20220016

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