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How should studies using AI be reported? lessons from a systematic review in cardiac MRI

Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality o...

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Autores principales: Maiter, Ahmed, Salehi, Mahan, Swift, Andrew J., Alabed, Samer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364997/
https://www.ncbi.nlm.nih.gov/pubmed/37492379
http://dx.doi.org/10.3389/fradi.2023.1112841
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author Maiter, Ahmed
Salehi, Mahan
Swift, Andrew J.
Alabed, Samer
author_facet Maiter, Ahmed
Salehi, Mahan
Swift, Andrew J.
Alabed, Samer
author_sort Maiter, Ahmed
collection PubMed
description Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies—a systematic review and recommendations for future studies. Frontiers in Cardiovascular Medicine 9:956811). 209 studies were assessed for compliance with the Checklist for AI in Medical Imaging (CLAIM), a framework for reporting. We found variable—and sometimes poor—quality of reporting and identified significant and frequently missing information in publications. Compliance with CLAIM was high for descriptions of models (100%, IQR 80%–100%), but lower than expected for descriptions of study design (71%, IQR 63–86%), datasets used in training and testing (63%, IQR 50%–67%) and model performance (60%, IQR 50%–70%). Here, we present a summary of our key findings, aimed at general readers who may not be experts in AI, and use them as a framework to discuss the factors determining quality of reporting, making recommendations for improving the reporting of research in this field. We aim to assist researchers in presenting their work and readers in their appraisal of evidence. Finally, we emphasise the need for close scrutiny of studies presenting AI tools, even in the face of the excitement surrounding AI in cardiac imaging.
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spelling pubmed-103649972023-07-25 How should studies using AI be reported? lessons from a systematic review in cardiac MRI Maiter, Ahmed Salehi, Mahan Swift, Andrew J. Alabed, Samer Front Radiol Radiology Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies—a systematic review and recommendations for future studies. Frontiers in Cardiovascular Medicine 9:956811). 209 studies were assessed for compliance with the Checklist for AI in Medical Imaging (CLAIM), a framework for reporting. We found variable—and sometimes poor—quality of reporting and identified significant and frequently missing information in publications. Compliance with CLAIM was high for descriptions of models (100%, IQR 80%–100%), but lower than expected for descriptions of study design (71%, IQR 63–86%), datasets used in training and testing (63%, IQR 50%–67%) and model performance (60%, IQR 50%–70%). Here, we present a summary of our key findings, aimed at general readers who may not be experts in AI, and use them as a framework to discuss the factors determining quality of reporting, making recommendations for improving the reporting of research in this field. We aim to assist researchers in presenting their work and readers in their appraisal of evidence. Finally, we emphasise the need for close scrutiny of studies presenting AI tools, even in the face of the excitement surrounding AI in cardiac imaging. Frontiers Media S.A. 2023-01-30 /pmc/articles/PMC10364997/ /pubmed/37492379 http://dx.doi.org/10.3389/fradi.2023.1112841 Text en © 2023 Maiter, Salehi, Swift and Alabed. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Radiology
Maiter, Ahmed
Salehi, Mahan
Swift, Andrew J.
Alabed, Samer
How should studies using AI be reported? lessons from a systematic review in cardiac MRI
title How should studies using AI be reported? lessons from a systematic review in cardiac MRI
title_full How should studies using AI be reported? lessons from a systematic review in cardiac MRI
title_fullStr How should studies using AI be reported? lessons from a systematic review in cardiac MRI
title_full_unstemmed How should studies using AI be reported? lessons from a systematic review in cardiac MRI
title_short How should studies using AI be reported? lessons from a systematic review in cardiac MRI
title_sort how should studies using ai be reported? lessons from a systematic review in cardiac mri
topic Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364997/
https://www.ncbi.nlm.nih.gov/pubmed/37492379
http://dx.doi.org/10.3389/fradi.2023.1112841
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