Cargando…
The CNN model aided the study of the clinical value hidden in the implant images
PURPOSE: This article aims to construct a new method to evaluate radiographic image identification results based on artificial intelligence, which can complement the limited vision of researchers when studying the effect of various factors on clinical implantation outcomes. METHODS: We constructed a...
Autores principales: | Huang, Xinxu, Chen, Xingyu, Zhong, Xinnan, Tian, Taoran |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562019/ https://www.ncbi.nlm.nih.gov/pubmed/37656066 http://dx.doi.org/10.1002/acm2.14141 |
Ejemplares similares
-
Semi‐supervised classification of fundus images combined with CNN and GCN
por: Duan, Sixu, et al.
Publicado: (2022) -
Photoacoustic imaging of hidden dental caries using visible–light diode laser
por: Tasmara, Fikhri Astina, et al.
Publicado: (2023) -
Safety of magnetic resonance imaging in patients with cardiac implantable electronic devices with generator and lead(s) brand mismatch
por: Minaskeian, Nareg, et al.
Publicado: (2022) -
Quantitation of clinical feedback on image quality differences between two CT scanner models
por: Bache, Steven T., et al.
Publicado: (2017) -
3D printed testing aids for radiographic quality control
por: Ogden, Kent M., et al.
Publicado: (2019)