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2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence
Wrist trauma is common in children and generally requires radiography for exclusion of fractures, subjecting children to radiation and long wait times in the emergency department. Ultrasound (US) has potential to be a safer, faster diagnostic tool. This study aimed to determine how reliably US could...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477281/ https://www.ncbi.nlm.nih.gov/pubmed/37666945 http://dx.doi.org/10.1038/s41598-023-41807-w |
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author | Knight, Jessica Zhou, Yuyue Keen, Christopher Hareendranathan, Abhilash Rakkunedeth Alves-Pereira, Fatima Ghasseminia, Siyavesh Wichuk, Stephanie Brilz, Alan Kirschner, David Jaremko, Jacob |
author_facet | Knight, Jessica Zhou, Yuyue Keen, Christopher Hareendranathan, Abhilash Rakkunedeth Alves-Pereira, Fatima Ghasseminia, Siyavesh Wichuk, Stephanie Brilz, Alan Kirschner, David Jaremko, Jacob |
author_sort | Knight, Jessica |
collection | PubMed |
description | Wrist trauma is common in children and generally requires radiography for exclusion of fractures, subjecting children to radiation and long wait times in the emergency department. Ultrasound (US) has potential to be a safer, faster diagnostic tool. This study aimed to determine how reliably US could detect distal radius fractures in children, to contrast the accuracy of 2DUS to 3DUS, and to assess the utility of artificial intelligence for image interpretation. 127 children were scanned with 2DUS and 3DUS on the affected wrist. US scans were then read by 7 blinded human readers and an AI model. With radiographs used as the gold standard, expert human readers obtained a mean sensitivity of 0.97 and 0.98 for 2DUS and 3DUS respectively. The AI model sensitivity was 0.91 and 1.00 for 2DUS and 3DUS respectively. Study data suggests that 2DUS is comparable to 3DUS and AI diagnosis is comparable to human experts. |
format | Online Article Text |
id | pubmed-10477281 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104772812023-09-06 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence Knight, Jessica Zhou, Yuyue Keen, Christopher Hareendranathan, Abhilash Rakkunedeth Alves-Pereira, Fatima Ghasseminia, Siyavesh Wichuk, Stephanie Brilz, Alan Kirschner, David Jaremko, Jacob Sci Rep Article Wrist trauma is common in children and generally requires radiography for exclusion of fractures, subjecting children to radiation and long wait times in the emergency department. Ultrasound (US) has potential to be a safer, faster diagnostic tool. This study aimed to determine how reliably US could detect distal radius fractures in children, to contrast the accuracy of 2DUS to 3DUS, and to assess the utility of artificial intelligence for image interpretation. 127 children were scanned with 2DUS and 3DUS on the affected wrist. US scans were then read by 7 blinded human readers and an AI model. With radiographs used as the gold standard, expert human readers obtained a mean sensitivity of 0.97 and 0.98 for 2DUS and 3DUS respectively. The AI model sensitivity was 0.91 and 1.00 for 2DUS and 3DUS respectively. Study data suggests that 2DUS is comparable to 3DUS and AI diagnosis is comparable to human experts. Nature Publishing Group UK 2023-09-04 /pmc/articles/PMC10477281/ /pubmed/37666945 http://dx.doi.org/10.1038/s41598-023-41807-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Knight, Jessica Zhou, Yuyue Keen, Christopher Hareendranathan, Abhilash Rakkunedeth Alves-Pereira, Fatima Ghasseminia, Siyavesh Wichuk, Stephanie Brilz, Alan Kirschner, David Jaremko, Jacob 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence |
title | 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence |
title_full | 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence |
title_fullStr | 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence |
title_full_unstemmed | 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence |
title_short | 2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence |
title_sort | 2d/3d ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477281/ https://www.ncbi.nlm.nih.gov/pubmed/37666945 http://dx.doi.org/10.1038/s41598-023-41807-w |
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