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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...

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Autores principales: Knight, Jessica, Zhou, Yuyue, Keen, Christopher, Hareendranathan, Abhilash Rakkunedeth, Alves-Pereira, Fatima, Ghasseminia, Siyavesh, Wichuk, Stephanie, Brilz, Alan, Kirschner, David, Jaremko, Jacob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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.
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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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