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Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury
The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTIC...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304372/ https://www.ncbi.nlm.nih.gov/pubmed/35864281 http://dx.doi.org/10.1038/s41598-022-16313-0 |
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author | Heo, Sejin Ha, Juhyung Jung, Weon Yoo, Suyoung Song, Yeejun Kim, Taerim Cha, Won Chul |
author_facet | Heo, Sejin Ha, Juhyung Jung, Weon Yoo, Suyoung Song, Yeejun Kim, Taerim Cha, Won Chul |
author_sort | Heo, Sejin |
collection | PubMed |
description | The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety. |
format | Online Article Text |
id | pubmed-9304372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93043722022-07-23 Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury Heo, Sejin Ha, Juhyung Jung, Weon Yoo, Suyoung Song, Yeejun Kim, Taerim Cha, Won Chul Sci Rep Article The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety. Nature Publishing Group UK 2022-07-21 /pmc/articles/PMC9304372/ /pubmed/35864281 http://dx.doi.org/10.1038/s41598-022-16313-0 Text en © The Author(s) 2022 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 Heo, Sejin Ha, Juhyung Jung, Weon Yoo, Suyoung Song, Yeejun Kim, Taerim Cha, Won Chul Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury |
title | Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury |
title_full | Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury |
title_fullStr | Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury |
title_full_unstemmed | Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury |
title_short | Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury |
title_sort | decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304372/ https://www.ncbi.nlm.nih.gov/pubmed/35864281 http://dx.doi.org/10.1038/s41598-022-16313-0 |
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