Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

OBJECTIVES: To develop a Deep Learning Artificial Intelligence (AI) model that automatically localizes the position of radiographic stent gutta percha (GP) markers in cone beam computed tomography (CBCT) images to identify proposed implant sites within the images, and to test the performance of the...

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Detalles Bibliográficos
Autores principales: Alsomali, Mona, Alghamdi, Shatha, Alotaibi, Shahad, Alfadda, Sara, Altwaijry, Najwa, Alturaiki, Isra, Al-Ekrish, Asma'a
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9346930/
https://www.ncbi.nlm.nih.gov/pubmed/35935725
http://dx.doi.org/10.1016/j.sdentj.2022.01.002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9346930/
https://www.ncbi.nlm.nih.gov/pubmed/35935725
http://dx.doi.org/10.1016/j.sdentj.2022.01.002

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