A fully deep learning model for the automatic identification of cephalometric landmarks

PURPOSE: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. MATERIALS AND METHODS: In total, 950 lateral cephalometric images from Yonsei Dental Hos...

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Detalles Bibliográficos
Autores principales: Kim, Young Hyun, Lee, Chena, Ha, Eun-Gyu, Choi, Yoon Jeong, Han, Sang-Sun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Academy of Oral and Maxillofacial Radiology 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479429/
https://www.ncbi.nlm.nih.gov/pubmed/34621657
http://dx.doi.org/10.5624/isd.20210077

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479429/
https://www.ncbi.nlm.nih.gov/pubmed/34621657
http://dx.doi.org/10.5624/isd.20210077

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