Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs Article Swipe

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Nils Hendrix , Ernst T. Scholten , Bastiaan Vernhout , Stefan Bruijnen , Bas Maresch , Mathijn de Jong , S.C.E. Diepstraten , Stijn Bollen , Steven Schalekamp , Maarten de Rooij , Alexander Scholtens , Ward Hendrix , Tijs Samson , Lee‐Ling Sharon Ong , Eric Postma , Bram van Ginneken , Matthieu Rutten ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1148/ryai.2021200260

The developed CNN achieved radiologist-level performance in detecting scaphoid bone fractures on conventional radiographs of the hand, wrist, and scaphoid.Keywords: Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Feature Detection-Vision-Application Domain, Computer-Aided DiagnosisSee also the commentary by Li and Torriani in this issue.Supplemental material is available for this article.©RSNA, 2021.

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Medicine Radiography Receiver operating characteristic Convolutional neural network Scaphoid fracture Wrist Segmentation Scaphoid bone Artificial intelligence Nuclear medicine Radiology Internal medicine Computer science

Metadata

Type: article
Language: en
Landing Page: https://doi.org/10.1148/ryai.2021200260
OA Status: green
Cited By: 44
References: 22
Related Works: 10
OpenAlex ID: https://openalex.org/W3157550358

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