Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs

This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Periapical radiographs of 600 pediatric patients (age r...

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Detalles Bibliográficos
Autores principales: Jeon, Kug Jin, Ha, Eun-Gyu, Choi, Hanseung, Lee, Chena, Han, Sang-Sun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9470664/
https://www.ncbi.nlm.nih.gov/pubmed/36100696
http://dx.doi.org/10.1038/s41598-022-19753-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9470664/
https://www.ncbi.nlm.nih.gov/pubmed/36100696
http://dx.doi.org/10.1038/s41598-022-19753-w

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