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Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs
Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach m...
| Autores principales: | , , , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599208/ https://www.ncbi.nlm.nih.gov/pubmed/33145440 http://dx.doi.org/10.1038/s41746-020-00352-w |
| _version_ | 1783602822840844288 |
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| author | Jones, Rebecca M. Sharma, Anuj Hotchkiss, Robert Sperling, John W. Hamburger, Jackson Ledig, Christian O’Toole, Robert Gardner, Michael Venkatesh, Srivas Roberts, Matthew M. Sauvestre, Romain Shatkhin, Max Gupta, Anant Chopra, Sumit Kumaravel, Manickam Daluiski, Aaron Plogger, Will Nascone, Jason Potter, Hollis G. Lindsey, Robert V. |
| author_facet | Jones, Rebecca M. Sharma, Anuj Hotchkiss, Robert Sperling, John W. Hamburger, Jackson Ledig, Christian O’Toole, Robert Gardner, Michael Venkatesh, Srivas Roberts, Matthew M. Sauvestre, Romain Shatkhin, Max Gupta, Anant Chopra, Sumit Kumaravel, Manickam Daluiski, Aaron Plogger, Will Nascone, Jason Potter, Hollis G. Lindsey, Robert V. |
| author_sort | Jones, Rebecca M. |
| collection | PubMed |
| description | Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation. |
| format | Online Article Text |
| id | pubmed-7599208 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2020 |
| publisher | Nature Publishing Group UK |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-75992082020-11-02 Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs Jones, Rebecca M. Sharma, Anuj Hotchkiss, Robert Sperling, John W. Hamburger, Jackson Ledig, Christian O’Toole, Robert Gardner, Michael Venkatesh, Srivas Roberts, Matthew M. Sauvestre, Romain Shatkhin, Max Gupta, Anant Chopra, Sumit Kumaravel, Manickam Daluiski, Aaron Plogger, Will Nascone, Jason Potter, Hollis G. Lindsey, Robert V. NPJ Digit Med Brief Communication Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation. Nature Publishing Group UK 2020-10-30 /pmc/articles/PMC7599208/ /pubmed/33145440 http://dx.doi.org/10.1038/s41746-020-00352-w Text en © 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/. |
| spellingShingle | Brief Communication Jones, Rebecca M. Sharma, Anuj Hotchkiss, Robert Sperling, John W. Hamburger, Jackson Ledig, Christian O’Toole, Robert Gardner, Michael Venkatesh, Srivas Roberts, Matthew M. Sauvestre, Romain Shatkhin, Max Gupta, Anant Chopra, Sumit Kumaravel, Manickam Daluiski, Aaron Plogger, Will Nascone, Jason Potter, Hollis G. Lindsey, Robert V. Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs |
| title | Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs |
| title_full | Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs |
| title_fullStr | Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs |
| title_full_unstemmed | Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs |
| title_short | Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs |
| title_sort | assessment of a deep-learning system for fracture detection in musculoskeletal radiographs |
| topic | Brief Communication |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599208/ https://www.ncbi.nlm.nih.gov/pubmed/33145440 http://dx.doi.org/10.1038/s41746-020-00352-w |
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