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. 2020 Oct 30;3:144. doi: 10.1038/s41746-020-00352-w

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a The deep-learning system used an ensemble of 10 convolutional neural networks. To produce a prediction, radiographs are processed by each network in the ensemble, averaged, and then post-processed to generate an overall fracture determination and bounding boxes. b Example outputs for each of the 16 anatomical regions supported by the deep-learning system.