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Mariscal-Harana J, Asher C, Vergani V, Rizvi M, Keehn L, Kim RJ, Judd RM, Petersen SE, Razavi R, King AP, Ruijsink B, Puyol-Antón E. An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:370-383. [PMID: 37794871 PMCID: PMC10545512 DOI: 10.1093/ehjdh/ztad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/05/2023] [Accepted: 07/12/2023] [Indexed: 10/06/2023]
Abstract
Aims Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Methods and results Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n = 414) and five external datasets (n = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4 mL (left ventricle volume), <9.2 mL (right ventricle volume), <13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. Conclusion We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases.
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Affiliation(s)
- Jorge Mariscal-Harana
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Clint Asher
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Vittoria Vergani
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Maleeha Rizvi
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Louise Keehn
- Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital, London, Westminster Bridge Road, London SE1 7EH, UK
| | - Raymond J Kim
- Division of Cardiology, Department of Medicine, Duke University, 40 Duke Medicine Circle, Durham, NC 27710, USA
| | - Robert M Judd
- Division of Cardiology, Department of Medicine, Duke University, 40 Duke Medicine Circle, Durham, NC 27710, USA
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, W Smithfield, London EC1A 7BE, UK
- Health Data Research UK, Gibbs Building, 215 Euston Rd., London NW1 2BE, UK
- Alan Turing Institute, 96 Euston Rd., London NW1 2DB, UK
| | - Reza Razavi
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Bram Ruijsink
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
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Ammann C, Hadler T, Gröschel J, Kolbitsch C, Schulz-Menger J. Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations. Front Cardiovasc Med 2023; 10:1118499. [PMID: 37144061 PMCID: PMC10151814 DOI: 10.3389/fcvm.2023.1118499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
Background Cardiac function quantification in cardiovascular magnetic resonance requires precise contouring of the heart chambers. This time-consuming task is increasingly being addressed by a plethora of ever more complex deep learning methods. However, only a small fraction of these have made their way from academia into clinical practice. In the quality assessment and control of medical artificial intelligence, the opaque reasoning and associated distinctive errors of neural networks meet an extraordinarily low tolerance for failure. Aim The aim of this study is a multilevel analysis and comparison of the performance of three popular convolutional neural network (CNN) models for cardiac function quantification. Methods U-Net, FCN, and MultiResUNet were trained for the segmentation of the left and right ventricles on short-axis cine images of 119 patients from clinical routine. The training pipeline and hyperparameters were kept constant to isolate the influence of network architecture. CNN performance was evaluated against expert segmentations for 29 test cases on contour level and in terms of quantitative clinical parameters. Multilevel analysis included breakdown of results by slice position, as well as visualization of segmentation deviations and linkage of volume differences to segmentation metrics via correlation plots for qualitative analysis. Results All models showed strong correlation to the expert with respect to quantitative clinical parameters (rz ' = 0.978, 0.977, 0.978 for U-Net, FCN, MultiResUNet respectively). The MultiResUNet significantly underestimated ventricular volumes and left ventricular myocardial mass. Segmentation difficulties and failures clustered in basal and apical slices for all CNNs, with the largest volume differences in the basal slices (mean absolute error per slice: 4.2 ± 4.5 ml for basal, 0.9 ± 1.3 ml for midventricular, 0.9 ± 0.9 ml for apical slices). Results for the right ventricle had higher variance and more outliers compared to the left ventricle. Intraclass correlation for clinical parameters was excellent (≥0.91) among the CNNs. Conclusion Modifications to CNN architecture were not critical to the quality of error for our dataset. Despite good overall agreement with the expert, errors accumulated in basal and apical slices for all models.
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Affiliation(s)
- Clemens Ammann
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Thomas Hadler
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Jan Gröschel
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jeanette Schulz-Menger
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Department of Cardiology and Nephrology, HELIOS Hospital Berlin-Buch, Berlin, Germany
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3
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Govil S, Crabb BT, Deng Y, Dal Toso L, Puyol-Antón E, Pushparajah K, Hegde S, Perry JC, Omens JH, Hsiao A, Young AA, McCulloch AD. A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot. J Cardiovasc Magn Reson 2023; 25:15. [PMID: 36849960 PMCID: PMC9969707 DOI: 10.1186/s12968-023-00924-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/25/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows. METHODS Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores. RESULTS The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. CONCLUSIONS Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end approach dramatically reduces the manual input required to create shape models, thereby enabling the rapid analysis of large-scale datasets and the potential to deploy statistical atlas-based analyses in point-of-care clinical settings. Training data and networks are available from cardiacatlas.org.
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Affiliation(s)
- Sachin Govil
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Brendan T. Crabb
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Yu Deng
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Laura Dal Toso
- Department of Biomedical Engineering, King’s College London, London, UK
| | | | | | - Sanjeet Hegde
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital San Diego, San Diego, CA USA
| | - James C. Perry
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital San Diego, San Diego, CA USA
| | - Jeffrey H. Omens
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Albert Hsiao
- Department of Radiology, University of California San Diego, La Jolla, CA USA
| | - Alistair A. Young
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Andrew D. McCulloch
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
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Mauger CA, Gilbert K, Suinesiaputra A, Bluemke DA, Wu CO, Lima JAC, Young AA, Ambale-Venkatesh B. Multi-Ethnic Study of Atherosclerosis: Relationship between Left Ventricular Shape at Cardiac MRI and 10-year Outcomes. Radiology 2023; 306:e220122. [PMID: 36125376 PMCID: PMC9870985 DOI: 10.1148/radiol.220122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 02/03/2023]
Abstract
Background Left ventricular (LV) subclinical remodeling is associated with adverse outcomes and indicates mechanisms of disease development. Standard metrics such as LV mass and volumes may not capture the full range of remodeling. Purpose To quantify the relationship between LV three-dimensional shape at MRI and incident cardiovascular events over 10 years. Materials and Methods In this retrospective study, 5098 participants from the Multi-Ethnic Study of Atherosclerosis who were free of clinical cardiovascular disease underwent cardiac MRI from 2000 to 2002. LV shape models were automatically generated using a machine learning workflow. Event-specific remodeling signatures were computed using partial least squares regression, and random survival forests were used to determine which features were most associated with incident heart failure (HF), coronary heart disease (CHD), and cardiovascular disease (CVD) events over a 10-year follow-up period. The discrimination improvement of adding LV shape to traditional cardiovascular risk factors, coronary artery calcium scores, and N-terminal pro-brain natriuretic peptide levels was assessed using the index of prediction accuracy and time-dependent area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival curves were used to illustrate the ability of remodeling signatures to predict the end points. Results Overall, 4618 participants had sufficient three-dimensional MRI information to generate patient-specific LV models (mean age, 60.6 years ± 9.9 [SD]; 2540 women). Among these participants, 147 had HF, 317 had CHD, and 455 had CVD events. The addition of LV remodeling signatures to traditional cardiovascular risk factors improved the mean AUC for 10-year survival prediction and achieved better performance than LV mass and volumes; HF (AUC, 0.83 ± 0.01 and 0.81 ± 0.01, respectively; P < .05), CHD (AUC, 0.77 ± 0.01 and 0.75 ± 0.01, respectively; P < .05), and CVD (AUC, 0.78 ± 0.0 and 0.76 ± 0.0, respectively; P < .05). Kaplan-Meier analysis demonstrated that participants with high-risk HF remodeling signatures had a 10-year survival rate of 56% compared with 95% for those with low-risk scores. Conclusion Left ventricular event-specific remodeling signatures were more predictive of heart failure, coronary heart disease, and cardiovascular disease events over 10 years than standard mass and volume measures and enable an automatic personalized medicine approach to tracking remodeling. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
| | | | - Avan Suinesiaputra
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - David A. Bluemke
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - Colin O. Wu
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - João A. C. Lima
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - Alistair A. Young
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
| | - Bharath Ambale-Venkatesh
- From the Department of Anatomy and Medical Imaging, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton,
Auckland 1023, New Zealand (C.A.M.); Auckland Bioengineering Institute,
University of Auckland, Auckland, New Zealand (C.A.M., K.G.); Department of
Biomedical Engineering, King’s College London, London, UK (A.S., A.A.Y.);
Department of Radiology, University of Wisconsin School of Medicine and Public
Health, Madison, Wis (D.A.B.); and Department of Cardiology, Johns Hopkins
Medical Center, Baltimore, Md (C.O.W., J.A.C.L., B.A.V.)
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Mîra A, Lamata P, Pushparajah K, Abraham G, Mauger CA, McCulloch AD, Omens JH, Bissell MM, Blair Z, Huffaker T, Tandon A, Engelhardt S, Koehler S, Pickardt T, Beerbaum P, Sarikouch S, Latus H, Greil G, Young AA, Hussain T. Le Cœur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot. J Cardiovasc Magn Reson 2022; 24:46. [PMID: 35922806 PMCID: PMC9351245 DOI: 10.1186/s12968-022-00877-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Maladaptive remodelling mechanisms occur in patients with repaired tetralogy of Fallot (rToF) resulting in a cycle of metabolic and structural changes. Biventricular shape analysis may indicate mechanisms associated with adverse events independent of pulmonary regurgitant volume index (PRVI). We aimed to determine novel remodelling patterns associated with adverse events in patients with rToF using shape and function analysis. METHODS Biventricular shape and function were studied in 192 patients with rToF (median time from TOF repair to baseline evaluation 13.5 years). Linear discriminant analysis (LDA) and principal component analysis (PCA) were used to identify shape differences between patients with and without adverse events. Adverse events included death, arrhythmias, and cardiac arrest with median follow-up of 10 years. RESULTS LDA and PCA showed that shape characteristics pertaining to adverse events included a more circular left ventricle (LV) (decreased eccentricity), dilated (increased sphericity) LV base, increased right ventricular (RV) apical sphericity, and decreased RV basal sphericity. Multivariate LDA showed that the optimal discriminative model included only RV apical ejection fraction and one PCA mode associated with a more circular and dilated LV base (AUC = 0.77). PRVI did not add value, and shape changes associated with increased PRVI were not predictive of adverse outcomes. CONCLUSION Pathological remodelling patterns in patients with rToF are significantly associated with adverse events, independent of PRVI. Mechanisms related to incident events include LV basal dilation with a reduced RV apical ejection fraction.
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Affiliation(s)
- Anna Mîra
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Georgina Abraham
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK
| | - Charlène A Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Andrew D McCulloch
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
- Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Jeffrey H Omens
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
- Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Malenka M Bissell
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, England
| | - Zach Blair
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tyler Huffaker
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Animesh Tandon
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Pediatric Cardiology, Cleveland Clinic Children's, Cleveland, OH, USA
| | - Sandy Engelhardt
- Department of Internal Medicine III, Group Artificial Intelligence in Cardiovascular Medicine, Heidelberg University Hospital, 69120, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg/Mannheim, Germany
| | - Sven Koehler
- Department of Internal Medicine III, Group Artificial Intelligence in Cardiovascular Medicine, Heidelberg University Hospital, 69120, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg/Mannheim, Germany
| | - Thomas Pickardt
- German Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Philipp Beerbaum
- German Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Berlin, Germany
- Department for Paediatric Cardiology and Paediatric Intensive Care Medicine, University Children's Hospital, Hannover Medical School, Hannover, Germany
| | - Samir Sarikouch
- German Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Berlin, Germany
- Department of Cardiothoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - Heiner Latus
- Department of Paediatric Cardiology and Congenital Heart Defects, German Heart Centre Munich, Munich, Germany
| | - Gerald Greil
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alistair A Young
- Department of Biomedical Engineering, King's College London, 1 Lambeth Palace Road, London, SE1 7EU, UK.
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
| | - Tarique Hussain
- Department of Pediatrics, Division of Pediatric Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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