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Automated Protocoling for MRI Exams—Challenges and Solutions
Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analy...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582071/ https://www.ncbi.nlm.nih.gov/pubmed/36042118 http://dx.doi.org/10.1007/s10278-022-00610-1 |
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author | Denck, Jonas Haas, Oliver Guehring, Jens Maier, Andreas Rothgang, Eva |
author_facet | Denck, Jonas Haas, Oliver Guehring, Jens Maier, Andreas Rothgang, Eva |
author_sort | Denck, Jonas |
collection | PubMed |
description | Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning. |
format | Online Article Text |
id | pubmed-9582071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-95820712022-10-21 Automated Protocoling for MRI Exams—Challenges and Solutions Denck, Jonas Haas, Oliver Guehring, Jens Maier, Andreas Rothgang, Eva J Digit Imaging Original Paper Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning. Springer International Publishing 2022-08-30 2022-10 /pmc/articles/PMC9582071/ /pubmed/36042118 http://dx.doi.org/10.1007/s10278-022-00610-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Original Paper Denck, Jonas Haas, Oliver Guehring, Jens Maier, Andreas Rothgang, Eva Automated Protocoling for MRI Exams—Challenges and Solutions |
title | Automated Protocoling for MRI Exams—Challenges and Solutions |
title_full | Automated Protocoling for MRI Exams—Challenges and Solutions |
title_fullStr | Automated Protocoling for MRI Exams—Challenges and Solutions |
title_full_unstemmed | Automated Protocoling for MRI Exams—Challenges and Solutions |
title_short | Automated Protocoling for MRI Exams—Challenges and Solutions |
title_sort | automated protocoling for mri exams—challenges and solutions |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582071/ https://www.ncbi.nlm.nih.gov/pubmed/36042118 http://dx.doi.org/10.1007/s10278-022-00610-1 |
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