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Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements
BACKGROUND: Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to impro...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8988415/ https://www.ncbi.nlm.nih.gov/pubmed/35387651 http://dx.doi.org/10.1186/s12968-022-00855-3 |
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author | Alandejani, Faisal Alabed, Samer Garg, Pankaj Goh, Ze Ming Karunasaagarar, Kavita Sharkey, Michael Salehi, Mahan Aldabbagh, Ziad Dwivedi, Krit Mamalakis, Michail Metherall, Pete Uthoff, Johanna Johns, Chris Rothman, Alexander Condliffe, Robin Hameed, Abdul Charalampoplous, Athanasios Lu, Haiping Plein, Sven Greenwood, John P. Lawrie, Allan Wild, Jim M. de Koning, Patrick J. H. Kiely, David G. Van Der Geest, Rob Swift, Andrew J. |
author_facet | Alandejani, Faisal Alabed, Samer Garg, Pankaj Goh, Ze Ming Karunasaagarar, Kavita Sharkey, Michael Salehi, Mahan Aldabbagh, Ziad Dwivedi, Krit Mamalakis, Michail Metherall, Pete Uthoff, Johanna Johns, Chris Rothman, Alexander Condliffe, Robin Hameed, Abdul Charalampoplous, Athanasios Lu, Haiping Plein, Sven Greenwood, John P. Lawrie, Allan Wild, Jim M. de Koning, Patrick J. H. Kiely, David G. Van Der Geest, Rob Swift, Andrew J. |
author_sort | Alandejani, Faisal |
collection | PubMed |
description | BACKGROUND: Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. METHODS: A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). RESULTS: All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm(2), 91.2 ± 4.5 cm(2) and 93.2 ± 3.2 cm(2), respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm(2), 87.0 ± 5.8 cm(2) and 91.8 ± 4.8 cm(2). Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84. CONCLUSION: Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-022-00855-3. |
format | Online Article Text |
id | pubmed-8988415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89884152022-04-08 Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements Alandejani, Faisal Alabed, Samer Garg, Pankaj Goh, Ze Ming Karunasaagarar, Kavita Sharkey, Michael Salehi, Mahan Aldabbagh, Ziad Dwivedi, Krit Mamalakis, Michail Metherall, Pete Uthoff, Johanna Johns, Chris Rothman, Alexander Condliffe, Robin Hameed, Abdul Charalampoplous, Athanasios Lu, Haiping Plein, Sven Greenwood, John P. Lawrie, Allan Wild, Jim M. de Koning, Patrick J. H. Kiely, David G. Van Der Geest, Rob Swift, Andrew J. J Cardiovasc Magn Reson Research BACKGROUND: Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. METHODS: A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). RESULTS: All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm(2), 91.2 ± 4.5 cm(2) and 93.2 ± 3.2 cm(2), respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm(2), 87.0 ± 5.8 cm(2) and 91.8 ± 4.8 cm(2). Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84. CONCLUSION: Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-022-00855-3. BioMed Central 2022-04-07 /pmc/articles/PMC8988415/ /pubmed/35387651 http://dx.doi.org/10.1186/s12968-022-00855-3 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Alandejani, Faisal Alabed, Samer Garg, Pankaj Goh, Ze Ming Karunasaagarar, Kavita Sharkey, Michael Salehi, Mahan Aldabbagh, Ziad Dwivedi, Krit Mamalakis, Michail Metherall, Pete Uthoff, Johanna Johns, Chris Rothman, Alexander Condliffe, Robin Hameed, Abdul Charalampoplous, Athanasios Lu, Haiping Plein, Sven Greenwood, John P. Lawrie, Allan Wild, Jim M. de Koning, Patrick J. H. Kiely, David G. Van Der Geest, Rob Swift, Andrew J. Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements |
title | Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements |
title_full | Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements |
title_fullStr | Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements |
title_full_unstemmed | Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements |
title_short | Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements |
title_sort | training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8988415/ https://www.ncbi.nlm.nih.gov/pubmed/35387651 http://dx.doi.org/10.1186/s12968-022-00855-3 |
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