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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...

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Autores principales: Heo, Sejin, Ha, Juhyung, Jung, Weon, Yoo, Suyoung, Song, Yeejun, Kim, Taerim, Cha, Won Chul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
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.
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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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