Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs

Background and purpose — Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior...

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Detalles Bibliográficos
Autores principales: Yamada, Yutoku, Maki, Satoshi, Kishida, Shunji, Nagai, Haruki, Arima, Junnosuke, Yamakawa, Nanako, Iijima, Yasushi, Shiko, Yuki, Kawasaki, Yohei, Kotani, Toshiaki, Shiga, Yasuhiro, Inage, Kazuhide, Orita, Sumihisa, Eguchi, Yawara, Takahashi, Hiroshi, Yamashita, Takeshi, Minami, Shohei, Ohtori, Seiji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023868/
https://www.ncbi.nlm.nih.gov/pubmed/32783544
http://dx.doi.org/10.1080/17453674.2020.1803664

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023868/
https://www.ncbi.nlm.nih.gov/pubmed/32783544
http://dx.doi.org/10.1080/17453674.2020.1803664

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