Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-cr...

Descripción completa

Detalles Bibliográficos
Autores principales: Singh, Varun, Danda, Varun, Gorniak, Richard, Flanders, Adam, Lakhani, Paras
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646608/
https://www.ncbi.nlm.nih.gov/pubmed/31073816
http://dx.doi.org/10.1007/s10278-019-00229-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646608/
https://www.ncbi.nlm.nih.gov/pubmed/31073816
http://dx.doi.org/10.1007/s10278-019-00229-9

Ejemplares similares