Experimental validation of computer-vision methods for the successful detection of endodontic treatment obturation and progression from noisy radiographs

PURPOSE: (1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics. METHODS: The study conformed to the STARD 2015 a...

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Detalles Bibliográficos
Autores principales: Hasan, Habib Al, Saad, Farhan Hasin, Ahmed, Saif, Mohammed, Nabeel, Farook, Taseef Hasan, Dudley, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504118/
https://www.ncbi.nlm.nih.gov/pubmed/37097541
http://dx.doi.org/10.1007/s11282-023-00685-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504118/
https://www.ncbi.nlm.nih.gov/pubmed/37097541
http://dx.doi.org/10.1007/s11282-023-00685-8

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