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

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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Bibliographic Details
Main Authors: Yutoku Yamada, Satoshi Maki, Shunji Kishida, Haruki Nagai, Junnosuke Arima, Nanako Yamakawa, Yasushi Iijima, Yuki Shiko, Yohei Kawasaki, Toshiaki Kotani, Yasuhiro Shiga, Kazuhide Inage, Sumihisa Orita, Yawara Eguchi, Hiroshi Takahashi, Takeshi Yamashita, Shohei Minami, Seiji Ohtori
Format: Article
Language:English
Published: Taylor & Francis Group 2020-12-01
Series:Acta Orthopaedica
Online Access:http://dx.doi.org/10.1080/17453674.2020.1803664

Internet

http://dx.doi.org/10.1080/17453674.2020.1803664

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