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Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol

INTRODUCTION: There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology. These studies, however, vary significantly in design and quality. While recent guidelines have been es...

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Autores principales: Kelly, Brendan, Judge, Conor, Bollard, Stephanie M., Clifford, Simon M., Healy, Gerard M., Yeom, Kristen W., Lawlor, Aonghus, Killeen, Ronan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726044/
https://www.ncbi.nlm.nih.gov/pubmed/33296033
http://dx.doi.org/10.1186/s13244-020-00929-9
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author Kelly, Brendan
Judge, Conor
Bollard, Stephanie M.
Clifford, Simon M.
Healy, Gerard M.
Yeom, Kristen W.
Lawlor, Aonghus
Killeen, Ronan P.
author_facet Kelly, Brendan
Judge, Conor
Bollard, Stephanie M.
Clifford, Simon M.
Healy, Gerard M.
Yeom, Kristen W.
Lawlor, Aonghus
Killeen, Ronan P.
author_sort Kelly, Brendan
collection PubMed
description INTRODUCTION: There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology. These studies, however, vary significantly in design and quality. While recent guidelines have been established to advise on ethics, data management and the potential directions of future research, systematic reviews of the entire field are lacking. We aim to investigate the use of artificial intelligence as applied to radiology, to identify the clinical questions being asked, which methodological approaches are applied to these questions and trends in use over time. METHODS AND ANALYSIS: We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and by the Cochrane Collaboration Handbook. We will perform a literature search through MEDLINE (Pubmed), and EMBASE, a detailed data extraction of trial characteristics and a narrative synthesis of the data. There will be no language restrictions. We will take a task-centred approach rather than focusing on modality or clinical subspecialty. Sub-group analysis will be performed by segmentation tasks, identification tasks, classification tasks, pegression/prediction tasks as well as a sub-analysis for paediatric patients. ETHICS AND DISSEMINATION: Ethical approval will not be required for this study, as data will be obtained from publicly available clinical trials. We will disseminate our results in a peer-reviewed publication. Registration number PROSPERO: CRD42020154790
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spelling pubmed-77260442020-12-17 Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol Kelly, Brendan Judge, Conor Bollard, Stephanie M. Clifford, Simon M. Healy, Gerard M. Yeom, Kristen W. Lawlor, Aonghus Killeen, Ronan P. Insights Imaging Critical Review INTRODUCTION: There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology. These studies, however, vary significantly in design and quality. While recent guidelines have been established to advise on ethics, data management and the potential directions of future research, systematic reviews of the entire field are lacking. We aim to investigate the use of artificial intelligence as applied to radiology, to identify the clinical questions being asked, which methodological approaches are applied to these questions and trends in use over time. METHODS AND ANALYSIS: We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and by the Cochrane Collaboration Handbook. We will perform a literature search through MEDLINE (Pubmed), and EMBASE, a detailed data extraction of trial characteristics and a narrative synthesis of the data. There will be no language restrictions. We will take a task-centred approach rather than focusing on modality or clinical subspecialty. Sub-group analysis will be performed by segmentation tasks, identification tasks, classification tasks, pegression/prediction tasks as well as a sub-analysis for paediatric patients. ETHICS AND DISSEMINATION: Ethical approval will not be required for this study, as data will be obtained from publicly available clinical trials. We will disseminate our results in a peer-reviewed publication. Registration number PROSPERO: CRD42020154790 Springer Berlin Heidelberg 2020-12-09 /pmc/articles/PMC7726044/ /pubmed/33296033 http://dx.doi.org/10.1186/s13244-020-00929-9 Text en © The Author(s) 2020 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/.
spellingShingle Critical Review
Kelly, Brendan
Judge, Conor
Bollard, Stephanie M.
Clifford, Simon M.
Healy, Gerard M.
Yeom, Kristen W.
Lawlor, Aonghus
Killeen, Ronan P.
Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol
title Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol
title_full Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol
title_fullStr Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol
title_full_unstemmed Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol
title_short Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol
title_sort radiology artificial intelligence, a systematic evaluation of methods (raise): a systematic review protocol
topic Critical Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726044/
https://www.ncbi.nlm.nih.gov/pubmed/33296033
http://dx.doi.org/10.1186/s13244-020-00929-9
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