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

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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

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 · OA: W3157550358

The developed CNN achieved radiologist-level performance in detecting scaphoid bone fractures on conventional radiographs of the hand, wrist, and scaphoid.<b>Keywords:</b> 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.<i>Supplemental material is available for this article.</i>©RSNA, 2021.