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Morris DM, Wang C, Papanastasiou G, Gray CD, Xu W, Sjöström S, Badr S, Paccou J, Semple SIK, MacGillivray T, Cawthorn WP. A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data. Comput Struct Biotechnol J 2024; 24:89-104. [PMID: 38268780 PMCID: PMC10806280 DOI: 10.1016/j.csbj.2023.12.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/26/2024] Open
Abstract
Background Bone marrow adipose tissue (BMAT) represents > 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT's true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances. Objective To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data. Materials and methods We studied males and females aged 60-69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61-64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10-12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis. Results Model accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics. Conclusions We have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.
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Affiliation(s)
- David M. Morris
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Chengjia Wang
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- School of Mathematics and Computer Sciences, Heriot-Watt University, Edinburgh EH14 1AS, UK
| | - Giorgos Papanastasiou
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- School of Computer Science and Electronic Engineering, Wivenhoe Park, The University of Essex, Colchester CO4 3SQ, UK
| | - Calum D. Gray
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Wei Xu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Samuel Sjöström
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Sammy Badr
- University of Lille, Marrow Adiposity and Bone Laboratory (MABlab) ULR 4490, F-59000 Lille, France
- CHU Lille, Department of Radiology and Musculoskeletal Imaging, F-59000 Lille, France
| | - Julien Paccou
- University of Lille, Marrow Adiposity and Bone Laboratory (MABlab) ULR 4490, F-59000 Lille, France
- CHU Lille, Department of Rheumatology, F-59000 Lille, France
| | - Scott IK Semple
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Tom MacGillivray
- Centre for Clinical Brain Sciences, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - William P. Cawthorn
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
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Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes NA, Vora AN, Velazquez EJ, Li F, Menon V, Kapadia SR, Gill TM, Nadkarni GN, Krumholz HM, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiol 2024; 9:534-544. [PMID: 38581644 PMCID: PMC10999005 DOI: 10.1001/jamacardio.2024.0595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/27/2024] [Indexed: 04/08/2024]
Abstract
Importance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Gregory Holste
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - Neal Yuan
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Norrisa A. Haynes
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Amit N. Vora
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Eric J. Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut
| | - Venu Menon
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Samir R. Kapadia
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Thomas M. Gill
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - David Ouyang
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Associate Editor, JAMA
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Baccouch W, Oueslati S, Solaiman B, Lahidheb D, Labidi S. Automatic left ventricle volume and mass quantification from 2D cine-MRI: Investigating papillary muscle influence. Med Eng Phys 2024; 127:104162. [PMID: 38692762 DOI: 10.1016/j.medengphy.2024.104162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 03/01/2024] [Accepted: 03/27/2024] [Indexed: 05/03/2024]
Abstract
OBJECTIVE Early detection of cardiovascular diseases is based on accurate quantification of the left ventricle (LV) function parameters. In this paper, we propose a fully automatic framework for LV volume and mass quantification from 2D-cine MR images already segmented using U-Net. METHODS The general framework consists of three main steps: Data preparation including automatic LV localization using a convolution neural network (CNN) and application of morphological operations to exclude papillary muscles from the LV cavity. The second step consists in automatically extracting the LV contours using U-Net architecture. Finally, by integrating temporal information which is manifested by a spatial motion of myocytes as a third dimension, we calculated LV volume, LV ejection fraction (LVEF) and left ventricle mass (LVM). Based on these parameters, we detected and quantified cardiac contraction abnormalities using Python software. RESULTS CNN was trained with 35 patients and tested on 15 patients from the ACDC database with an accuracy of 99,15 %. U-Net architecture was trained using ACDC database and evaluated using local dataset with a Dice similarity coefficient (DSC) of 99,78 % and a Hausdorff Distance (HD) of 4.468 mm (p < 0,001). Quantification results showed a strong correlation with physiological measures with a Pearson correlation coefficient (PCC) of 0,991 for LV volume, 0.962 for LVEF, 0.98 for stroke volume (SV) and 0.923 for LVM after pillars' elimination. Clinically, our method allows regional and accurate identification of pathological myocardial segments and can serve as a diagnostic aid tool of cardiac contraction abnormalities. CONCLUSION Experimental results prove the usefulness of the proposed method for LV volume and function quantification and verify its potential clinical applicability.
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Affiliation(s)
- Wafa Baccouch
- University of Tunis El Manar, Higher institute of Medical Technologies of Tunis, Research laboratory of Biophysics and Medical Technologies LR13ES07, Tunis, 1006, Tunisia.
| | - Sameh Oueslati
- University of Tunis El Manar, Higher institute of Medical Technologies of Tunis, Research laboratory of Biophysics and Medical Technologies LR13ES07, Tunis, 1006, Tunisia
| | - Basel Solaiman
- Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, 29238, Brest Cedex, France
| | - Dhaker Lahidheb
- University of Tunis El Manar, Faculty of Medicine of Tunis, Tunis, Tunisia; Department of Cardiology, Military Hospital of Tunis, Tunis, Tunisia
| | - Salam Labidi
- University of Tunis El Manar, Higher institute of Medical Technologies of Tunis, Research laboratory of Biophysics and Medical Technologies LR13ES07, Tunis, 1006, Tunisia
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Zhao D, Mauger CA, Gilbert K, Wang VY, Quill GM, Sutton TM, Lowe BS, Legget ME, Ruygrok PN, Doughty RN, Pedrosa J, D'hooge J, Young AA, Nash MP. Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping. Sci Rep 2023; 13:8118. [PMID: 37208380 PMCID: PMC10199025 DOI: 10.1038/s41598-023-33968-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/21/2023] [Indexed: 05/21/2023] Open
Abstract
Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.
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Affiliation(s)
- Debbie Zhao
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand.
| | - Charlène A Mauger
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Vicky Y Wang
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Gina M Quill
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Timothy M Sutton
- Counties Manukau Health Cardiology, Middlemore Hospital, Auckland, New Zealand
| | - Boris S Lowe
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
| | - Malcolm E Legget
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Peter N Ruygrok
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Robert N Doughty
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - João Pedrosa
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
| | - Jan D'hooge
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King's College London, London, UK
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
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Lin A, Pieszko K, Park C, Ignor K, Williams MC, Slomka P, Dey D. Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification. BJR Open 2023; 5:20220021. [PMID: 37396483 PMCID: PMC10311632 DOI: 10.1259/bjro.20220021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 03/14/2023] [Accepted: 04/03/2023] [Indexed: 07/04/2023] Open
Abstract
In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.
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Affiliation(s)
| | | | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Katarzyna Ignor
- Department of Interventional Cardiology, Collegium Medicum, University of Zielona Góra, Zielona Góra, Poland
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Piotr Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
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6
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Khurshid S, Lazarte J, Pirruccello JP, Weng LC, Choi SH, Hall AW, Wang X, Friedman SF, Nauffal V, Biddinger KJ, Aragam KG, Batra P, Ho JE, Philippakis AA, Ellinor PT, Lubitz SA. Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass. Nat Commun 2023; 14:1558. [PMID: 36944631 PMCID: PMC10030590 DOI: 10.1038/s41467-023-37173-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/04/2023] [Indexed: 03/23/2023] Open
Abstract
Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.
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Affiliation(s)
- Shaan Khurshid
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Julieta Lazarte
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - James P Pirruccello
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Lu-Chen Weng
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Seung Hoan Choi
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Amelia W Hall
- Gene Regulation Observatory, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xin Wang
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Samuel F Friedman
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Victor Nauffal
- Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Kiran J Biddinger
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Krishna G Aragam
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jennifer E Ho
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Anthony A Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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7
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Vukadinovic M, Renjith G, Yuan V, Kwan A, Cheng SC, Li D, Clarke SL, Ouyang D. Impact of Measurement Imprecision on Genetic Association Studies of Cardiac Function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286058. [PMID: 36824841 PMCID: PMC9949184 DOI: 10.1101/2023.02.16.23286058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
Background Recent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses. Methods Left ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses. Results Even within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies. Conclusions Improving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.
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Affiliation(s)
- Milos Vukadinovic
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Gauri Renjith
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Victoria Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA
| | - Alan Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Susan C Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Shoa L Clarke
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
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8
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Shah RA, Asatryan B, Sharaf Dabbagh G, Aung N, Khanji MY, Lopes LR, van Duijvenboden S, Holmes A, Muser D, Landstrom AP, Lee AM, Arora P, Semsarian C, Somers VK, Owens AT, Munroe PB, Petersen SE, Chahal CAA. Frequency, Penetrance, and Variable Expressivity of Dilated Cardiomyopathy-Associated Putative Pathogenic Gene Variants in UK Biobank Participants. Circulation 2022; 146:110-124. [PMID: 35708014 PMCID: PMC9375305 DOI: 10.1161/circulationaha.121.058143] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features. METHODS UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative. RESULTS Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes. CONCLUSIONS In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.
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Affiliation(s)
- Ravi A Shah
- Imperial College Healthcare NHS Trust, London, United Kingdom (R.A.S.)
| | - Babken Asatryan
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Switzerland (B.A.)
| | - Ghaith Sharaf Dabbagh
- Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, PA (G.S.D., C.A.A.C.).,University of Michigan, Division of Cardiovascular Medicine, Ann Arbor (G.S.D.)
| | - Nay Aung
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom (N.A., M.Y.K., L.R.L., A.M.L., S.E.P., C.A.A.C.).,NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Mohammed Y Khanji
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom (N.A., M.Y.K., L.R.L., A.M.L., S.E.P., C.A.A.C.).,NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Luis R Lopes
- Centre for Heart Muscle Disease, Institute of Cardiovascular Science, University College London, United Kingdom (L.R.L.)
| | - Stefan van Duijvenboden
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | | | - Daniele Muser
- Cardiac Electrophysiology, Cardiovascular Division, Hospital of the University of Pennsylvania, Philadelphia (D.M., C.A.A.C.)
| | - Andrew P Landstrom
- Departments of Pediatrics, Division of Cardiology, and Cell Biology, Duke University School of Medicine, Durham, NC (A.P.L.)
| | - Aaron Mark Lee
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Pankaj Arora
- Division of Cardiovascular Disease, University of Alabama at Birmingham (P.A.)
| | - Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute (C.S.), The University of Sydney, New South Wales, Australia.,Sydney Medical School Faculty of Medicine and Health (C.S.), The University of Sydney, New South Wales, Australia.,Department of Cardiology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia (C.S.)
| | - Virend K Somers
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (V.K.S., C.A.A.C.)
| | - Anjali T Owens
- Center for Inherited Cardiovascular Disease, Cardiovascular Division, University of Pennsylvania Perelman School of Medicine, Philadelphia (A.T.O.)
| | - Patricia B Munroe
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Steffen E Petersen
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - C Anwar A Chahal
- Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, PA (G.S.D., C.A.A.C.).,Cardiac Electrophysiology, Cardiovascular Division, Hospital of the University of Pennsylvania, Philadelphia (D.M., C.A.A.C.).,Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (V.K.S., C.A.A.C.)
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9
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Alandejani F, Alabed S, Garg P, Goh ZM, Karunasaagarar K, Sharkey M, Salehi M, Aldabbagh Z, Dwivedi K, Mamalakis M, Metherall P, Uthoff J, Johns C, Rothman A, Condliffe R, Hameed A, Charalampoplous A, Lu H, Plein S, Greenwood JP, Lawrie A, Wild JM, de Koning PJH, Kiely DG, Van Der Geest R, Swift AJ. Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements. J Cardiovasc Magn Reson 2022; 24:25. [PMID: 35387651 PMCID: PMC8988415 DOI: 10.1186/s12968-022-00855-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 03/19/2022] [Indexed: 12/29/2022] Open
Abstract
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 cm2, 91.2 ± 4.5 cm2 and 93.2 ± 3.2 cm2, 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 cm2, 87.0 ± 5.8 cm2 and 91.8 ± 4.8 cm2. 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.
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Affiliation(s)
- Faisal Alandejani
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Ze Ming Goh
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Kavita Karunasaagarar
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Ziad Aldabbagh
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Michail Mamalakis
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Pete Metherall
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Johanna Uthoff
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Chris Johns
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Alexander Rothman
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Robin Condliffe
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Abdul Hameed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Athanasios Charalampoplous
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Haiping Lu
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Sven Plein
- Multidisciplinary Cardiovascular Research Centre (MCRC) &, Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Clarendon Way, Leeds, UK
| | - John P Greenwood
- Multidisciplinary Cardiovascular Research Centre (MCRC) &, Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Clarendon Way, Leeds, UK
| | - Allan Lawrie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Patrick J H de Koning
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Rob Van Der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK.
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10
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Wang S, Patel H, Miller T, Ameyaw K, Narang A, Chauhan D, Anand S, Anyanwu E, Besser SA, Kawaji K, Liu XP, Lang RM, Mor-Avi V, Patel AR. AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors. JACC Cardiovasc Imaging 2022; 15:413-427. [PMID: 34656471 PMCID: PMC8917993 DOI: 10.1016/j.jcmg.2021.08.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVES The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert. BACKGROUND Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images. METHODS Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and ≥50%. Both ejection fraction values and classifications made by the DL ejection fraction approaches were compared against CLIN ejection fraction reference. Receiver-operating characteristic curve analysis was performed to evaluate the ability of CLIN and each of the DL classifications to predict major adverse cardiovascular events. RESULTS Excellent correlations were seen for each DL-LVEF compared with CLIN-LVEF (r = 0.83-0.93). Good correlations were present between DL-LVM and CLIN-LVM (r = 0.75-0.85). Modest correlations were observed between DL-RVEF and CLIN-RVEF (r = 0.59-0.68). A >10% error between CLIN and DL ejection fraction was present in 5% to 18% of cases for the left ventricle and 23% to 43% for the right ventricle. LVEF classification agreed with CLIN-LVEF classification in 86%, 80%, and 85% cases for the 3 DL-LVEF approaches. There were no differences among the 4 approaches in associations with major adverse cardiovascular events for LVEF, LVM, and RVEF. CONCLUSIONS This study revealed good agreement between automated and expert-derived LVEF and similarly strong associations with outcomes, compared with an expert. However, the ability of these automated measurements to accurately classify left ventricular function for treatment decision remains limited. DL-LVM showed good agreement with CLIN-LVM. DL-RVEF approaches need further refinements.
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Affiliation(s)
- Shuo Wang
- University of Chicago, Chicago, Illinois,Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hena Patel
- University of Chicago, Chicago, Illinois
| | | | | | | | | | | | | | | | - Keigo Kawaji
- University of Chicago, Chicago, Illinois,Illinois Institute of Technology, Chicago, Illinois
| | - Xing-Peng Liu
- Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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11
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Diaz-Pinto A, Ravikumar N, Attar R, Suinesiaputra A, Zhao Y, Levelt E, Dall’Armellina E, Lorenzi M, Chen Q, Keenan TDL, Agrón E, Chew EY, Lu Z, Gale CP, Gale RP, Plein S, Frangi AF. Predicting myocardial infarction through retinal scans and minimal personal information. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-021-00427-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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12
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Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing: Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment. Diagnostics (Basel) 2021; 11:diagnostics11101758. [PMID: 34679457 PMCID: PMC8534796 DOI: 10.3390/diagnostics11101758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/13/2021] [Accepted: 09/22/2021] [Indexed: 11/24/2022] Open
Abstract
Automating cardiac function assessment on cardiac magnetic resonance short-axis cines is faster and more reproducible than manual contour-tracing; however, accurately tracing basal contours remains challenging. Three automated post-processing software packages (Level 1) were compared to manual assessment. Subsequently, automated basal tracings were manually adjusted using a standardized protocol combined with software package-specific relative-to-manual standard error correction (Level 2). All post-processing was performed in 65 healthy subjects. Manual contour-tracing was performed separately from Level 1 and 2 automated analysis. Automated measurements were considered accurate when the difference was equal or less than the maximum manual inter-observer disagreement percentage. Level 1 (2.1 ± 1.0 min) and Level 2 automated (5.2 ± 1.3 min) were faster and more reproducible than manual (21.1 ± 2.9 min) post-processing, the maximum inter-observer disagreement was 6%. Compared to manual, Level 1 automation had wide limits of agreement. The most reliable software package obtained more accurate measurements in Level 2 compared to Level 1 automation: left ventricular end-diastolic volume, 98% and 53%; ejection fraction, 98% and 60%; mass, 70% and 3%; right ventricular end-diastolic volume, 98% and 28%; ejection fraction, 80% and 40%, respectively. Level 1 automated cardiac function post-processing is fast and highly reproducible with varying accuracy. Level 2 automation balances speed and accuracy.
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13
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Chen V, Barker AJ, Golan R, Scott MB, Huh H, Wei Q, Sojoudi A, Markl M. Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging. Int J Cardiovasc Imaging 2021; 37:3539-3547. [PMID: 34185211 DOI: 10.1007/s10554-021-02326-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/24/2021] [Indexed: 01/03/2023]
Abstract
Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective analysis of 100 healthy subjects undergoing cardiac MRI from 2012 to 2018, with 10 men and women in the following age groups: 18-30, 31-40, 41-50, 51-60, and 61-80 years old. Subjects underwent 1.5 T, 2D CINE SSFP MRI. 35 pathologic cases from local clinical exams and the SCMR 2015 consensus contours dataset were also analyzed. A fully convolutional network (FCN) similar to U-Net trained on the U.K. Biobank was used to automatically segment LV endocardial and epicardial contours. FCN and manual segmentation were compared using Dice metrics and measurements of end-diastolic volume (EDV), end-systolic volume (ESV), mass (LVM), and ejection fraction (LVEF). Paired t-tests and linear regressions were used to analyze measurement differences with respect to sex and age. Dice metrics (median ± IQR) for n = 135 cases were 0.94 ± 0.04/0.87 ± 0.10 (ED endocardium/ES endocardium). Measurement biases (mean ± SD) among the healthy cohort were - 0.3 ± 10.1 mL for EDV, - 6.7 ± 9.6 mL for ESV, 4.6 ± 6.4% for LVEF, and - 2.2 ± 11.0 g for LVM; biases were independent of sex and age. Biases among the 35 pathologic cases were 0.1 ± 19 mL for EDV, - 4.8 ± 19 mL for ESV, 2.0 ± 7.6% for LVEF, and 1.0 ± 20 g for LVM. In conclusion, automatic segmentation by the Biobank-trained FCN was independent of age and sex. Improvements in end-systolic basal slice detection are needed to decrease bias and improve precision in ESV and LVEF.
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Affiliation(s)
- Vincent Chen
- Department of Internal Medicine, Northwestern University, Chicago, IL, USA.,Department of Radiology, Northwestern University, 737 N. Michigan Avenue, Suite 1600, Chicago, IL, 60611, USA
| | - Alex J Barker
- Department of Radiology, University of Colorado, Denver, CO, USA
| | - Rotem Golan
- Circle Cardiovascular Imaging, Inc., Calgary, Canada
| | - Michael B Scott
- Department of Radiology, Northwestern University, 737 N. Michigan Avenue, Suite 1600, Chicago, IL, 60611, USA
| | - Hyungkyu Huh
- Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, South Korea
| | - Qiao Wei
- Circle Cardiovascular Imaging, Inc., Calgary, Canada
| | | | - Michael Markl
- Department of Radiology, Northwestern University, 737 N. Michigan Avenue, Suite 1600, Chicago, IL, 60611, USA.
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14
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Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell 2021; 4:652669. [PMID: 34056579 PMCID: PMC8160471 DOI: 10.3389/frai.2021.652669] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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15
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Khurshid S, Friedman SF, Pirruccello JP, Di Achille P, Diamant N, Anderson CD, Ellinor PT, Batra P, Ho JE, Philippakis AA, Lubitz SA. Deep learning to estimate cardiac magnetic resonance-derived left ventricular mass. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:109-117. [PMID: 35265898 PMCID: PMC8890333 DOI: 10.1016/j.cvdhj.2021.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Background Cardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (eg, InlineVF), but their accuracy and availability may be limited. Objective To develop an open-source deep learning model to estimate CMR-derived LV mass. Methods Within participants of the UK Biobank prospective cohort undergoing CMR, we trained 2 convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5065 individuals), while the second (ML4Hseg) performed LV segmentation informed by InlineVF (version D13A) contours. We compared ML4Hreg, ML4Hseg, and InlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex. Results We generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r = 0.864, 95% confidence interval [CI] 0.847-0.880; MAE 10.41 g, 95% CI 9.82-10.99) than ML4Hreg (r = 0.843, 95% CI 0.823-0.861; MAE 10.51, 95% CI 9.86-11.15, P = .01) and InlineVF (r = 0.795, 95% CI 0.770-0.818; MAE 14.30, 95% CI 13.46-11.01, P < .01). LVH defined using ML4Hseg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51-3.04), atrial fibrillation (1.75, 95% CI 1.37-2.20), and heart failure (4.67, 95% CI 3.28-6.49). Conclusions ML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.
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Affiliation(s)
- Shaan Khurshid
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Samuel Freesun Friedman
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - James P. Pirruccello
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christopher D. Anderson
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Jennifer E. Ho
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Anthony A. Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts
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16
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Raisi-Estabragh Z, Harvey NC, Neubauer S, Petersen SE. Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource. Eur Heart J Cardiovasc Imaging 2021; 22:251-258. [PMID: 33164079 PMCID: PMC7899275 DOI: 10.1093/ehjci/jeaa297] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/12/2020] [Indexed: 12/12/2022] Open
Abstract
The UK Biobank (UKB) is a health research resource of major international importance, incorporating comprehensive characterization of >500 000 men and women recruited between 2006 and 2010 from across the UK. There is prospective tracking of health outcomes for all participants through linkages with national cohorts (death registers, cancer registers, electronic hospital records, and primary care records). The dataset has been enhanced with the UKB imaging study, which aims to scan a subset of 100 000 participants. The imaging protocol includes magnetic resonance imaging of the brain, heart, and abdomen, carotid ultrasound, and whole-body dual X-ray absorptiometry. Since its launch in 2015, over 48 000 participants have completed the imaging study with scheduled completion in 2023. Repeat imaging of 10 000 participants has been approved and commenced in 2019. The cardiovascular magnetic resonance (CMR) scan provides detailed assessment of cardiac structure and function comprising bright blood anatomic assessment (sagittal, coronal, and axial), left and right ventricular cine images (long and short axes), myocardial tagging, native T1 mapping, aortic flow, and imaging of the thoracic aorta. The UKB is an open access resource available to health researchers across all scientific disciplines from both academia and industry with no preferential access or exclusivity. In this paper, we consider how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Department of Cardiac Imaging, St. Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, OX3 9DU, UK
| | - Steffen E Petersen
- William Harvey Research Institute, Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Department of Cardiac Imaging, St. Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
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17
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Tandon A, Mohan N, Jensen C, Burkhardt BEU, Gooty V, Castellanos DA, McKenzie PL, Zahr RA, Bhattaru A, Abdulkarim M, Amir-Khalili A, Sojoudi A, Rodriguez SM, Dillenbeck J, Greil GF, Hussain T. Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot. Pediatr Cardiol 2021; 42:578-589. [PMID: 33394116 PMCID: PMC7990832 DOI: 10.1007/s00246-020-02518-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 12/03/2020] [Indexed: 12/19/2022]
Abstract
Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm's performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.
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Affiliation(s)
- Animesh Tandon
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Navina Mohan
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Cory Jensen
- Circle Cardiovascular Imaging, Calgary, AB Canada
| | - Barbara E. U. Burkhardt
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
- Pediatric Cardiology, Department of Surgery, Pediatric Heart Center, University Children’s- Hospital Zurich, Zurich, Switzerland
| | - Vasu Gooty
- Department of Pediatrics, LeBonheur Children’s Hospital and University of Tennessee, Memphis, TN USA
| | - Daniel A. Castellanos
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Paige L. McKenzie
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Riad Abou Zahr
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
- King Faisal Specialist Hospital and Research Centre, Jeddah, Saudi Arabia
| | - Abhijit Bhattaru
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Mubeena Abdulkarim
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | | | | | - Stephen M. Rodriguez
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Jeanne Dillenbeck
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
| | - Gerald F. Greil
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
| | - Tarique Hussain
- Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Department of Radiology, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX 75235 USA
- Division of Cardiology, Children’s Health Children’s Medical Center Dallas, Dallas, TX USA
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18
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Zheng Q, Delingette H, Fung K, Petersen SE, Ayache N. Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs. Front Cardiovasc Med 2020; 7:539788. [PMID: 33313075 PMCID: PMC7701336 DOI: 10.3389/fcvm.2020.539788] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022] Open
Abstract
We perform unsupervised analysis of image-derived shape and motion features extracted from 3,822 cardiac Magnetic resonance imaging (MRIs) of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify 2 small clusters that probably correspond to 2 pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this finding. Moreover, we examine the differences between the other large clusters and compare our measures with the ground truth.
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Affiliation(s)
- Qiao Zheng
- Université Côte d'Azur, Inria, Sophia Antipolis, Valbonne, France
| | - Hervé Delingette
- Université Côte d'Azur, Inria, Sophia Antipolis, Valbonne, France
| | - Kenneth Fung
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
| | - Steffen E. Petersen
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
| | - Nicholas Ayache
- Université Côte d'Azur, Inria, Sophia Antipolis, Valbonne, France
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19
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Kamrul Hasan SM, Linte CA. L-CO-Net: Learned Condensation-Optimization Network for Segmentation and Clinical Parameter Estimation from Cardiac Cine MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1217-1220. [PMID: 33018206 DOI: 10.1109/embc44109.2020.9176491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology patient groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LV blood-pool), 93.3% (RV blood-pool), and 90.0% (LV Myocardium) with five-fold cross-validation and yielded similar clinical parameters as those estimated from the ground-truth segmentation data. Based on these results, this technique has the potential to become an efficient and competitive cardiac image segmentation tool that may be used for cardiac computer-aided diagnosis, planning, and guidance applications.
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20
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Hansen KB, Staehr C, Rohde PD, Homilius C, Kim S, Nyegaard M, Matchkov VV, Boedtkjer E. PTPRG is an ischemia risk locus essential for HCO 3--dependent regulation of endothelial function and tissue perfusion. eLife 2020; 9:e57553. [PMID: 32955439 PMCID: PMC7541084 DOI: 10.7554/elife.57553] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 09/18/2020] [Indexed: 12/22/2022] Open
Abstract
Acid-base conditions modify artery tone and tissue perfusion but the involved vascular-sensing mechanisms and disease consequences remain unclear. We experimentally investigated transgenic mice and performed genetic studies in a UK-based human cohort. We show that endothelial cells express the putative HCO3--sensor receptor-type tyrosine-protein phosphatase RPTPγ, which enhances endothelial intracellular Ca2+-responses in resistance arteries and facilitates endothelium-dependent vasorelaxation only when CO2/HCO3- is present. Consistent with waning RPTPγ-dependent vasorelaxation at low [HCO3-], RPTPγ limits increases in cerebral perfusion during neuronal activity and augments decreases in cerebral perfusion during hyperventilation. RPTPγ does not influence resting blood pressure but amplifies hyperventilation-induced blood pressure elevations. Loss-of-function variants in PTPRG, encoding RPTPγ, are associated with increased risk of cerebral infarction, heart attack, and reduced cardiac ejection fraction. We conclude that PTPRG is an ischemia susceptibility locus; and RPTPγ-dependent sensing of HCO3- adjusts endothelium-mediated vasorelaxation, microvascular perfusion, and blood pressure during acid-base disturbances and altered tissue metabolism.
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Affiliation(s)
| | | | - Palle D Rohde
- Department of Chemistry and Bioscience, Aalborg UniversityAalborgDenmark
| | | | - Sukhan Kim
- Department of Biomedicine, Aarhus UniversityAarhusDenmark
| | - Mette Nyegaard
- Department of Biomedicine, Aarhus UniversityAarhusDenmark
| | | | - Ebbe Boedtkjer
- Department of Biomedicine, Aarhus UniversityAarhusDenmark
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21
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Böttcher B, Beller E, Busse A, Cantré D, Yücel S, Öner A, Ince H, Weber MA, Meinel FG. Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm. Int J Cardiovasc Imaging 2020; 36:2239-2247. [PMID: 32677023 PMCID: PMC7568707 DOI: 10.1007/s10554-020-01935-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 07/06/2020] [Indexed: 12/18/2022]
Abstract
To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 years). The most common indications were known or suspected ischemic heart disease, cardiomyopathies or myocarditis. Fully automated analysis of LV volumes and function was performed using a deep learning-based algorithm. The analysis was subsequently corrected by a senior cardiovascular radiologist. Manual volumetric analysis was performed by two radiology trainees. Volumetric results were compared using Bland–Altman statistics and intra-class correlation coefficient. The frequency of clinically relevant differences was analysed using re-classification rates. The fully automated volumetric analysis was completed in a median of 8 s. With expert review and corrections, the analysis required a median of 110 s. Median time required for manual analysis was 3.5 min for a cardiovascular imaging fellow and 9 min for a radiology resident (p < 0.0001 for all comparisons). The correlation between fully automated results and expert-corrected results was very strong with intra-class correlation coefficients of 0.998 for end-diastolic volume, 0.997 for end-systolic volume, 0.899 for stroke volume, 0.972 for ejection fraction and 0.991 for myocardial mass (all p < 0.001). Clinically meaningful differences between fully automated and expert corrected results occurred in 18% of cases, comparable to the rate between the two manual readers (20%). Deep learning-based fully automated analysis of LV volumes and function is feasible, time-efficient and highly accurate. Clinically relevant corrections are required in a minority of cases.
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Affiliation(s)
- Benjamin Böttcher
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Ebba Beller
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Anke Busse
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Daniel Cantré
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Seyrani Yücel
- Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany
| | - Alper Öner
- Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany
| | - Hüseyin Ince
- Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany
| | - Felix G Meinel
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
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22
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Lin A, Kolossváry M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence: improving the efficiency of cardiovascular imaging. Expert Rev Med Devices 2020; 17:565-577. [PMID: 32510252 PMCID: PMC7382901 DOI: 10.1080/17434440.2020.1777855] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. AREAS COVERED In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool. EXPERT OPINION Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Piotr J Slomka
- Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
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23
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Littlejohns TJ, Holliday J, Gibson LM, Garratt S, Oesingmann N, Alfaro-Almagro F, Bell JD, Boultwood C, Collins R, Conroy MC, Crabtree N, Doherty N, Frangi AF, Harvey NC, Leeson P, Miller KL, Neubauer S, Petersen SE, Sellors J, Sheard S, Smith SM, Sudlow CLM, Matthews PM, Allen NE. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun 2020; 11:2624. [PMID: 32457287 PMCID: PMC7250878 DOI: 10.1038/s41467-020-15948-9] [Citation(s) in RCA: 262] [Impact Index Per Article: 65.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 04/03/2020] [Indexed: 01/18/2023] Open
Abstract
UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.
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Affiliation(s)
| | - Jo Holliday
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lorna M Gibson
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Department of Clinical Radiology, New Royal Infirmary of Edinburgh, Edinburgh, UK
| | | | | | - Fidel Alfaro-Almagro
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Megan C Conroy
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Crabtree
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | | | - Alejandro F Frangi
- Department of Cardiovascular Sciences and Electrical Engineering, KU Leuven, Leuven, Belgium
- CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine, Schools of Computing and Medicine, University of Leeds, Leeds, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Paul Leeson
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Karla L Miller
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, Queen Mary University of Medicine, London, UK
| | - Jonathan Sellors
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank Coordinating Centre, Stockport, UK
| | | | - Stephen M Smith
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Cathie L M Sudlow
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London and UK Dementia Research Institute, London, UK
| | - Naomi E Allen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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24
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Abdeltawab H, Khalifa F, Taher F, Alghamdi NS, Ghazal M, Beache G, Mohamed T, Keynton R, El-Baz A. A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images. Comput Med Imaging Graph 2020; 81:101717. [PMID: 32222684 PMCID: PMC7232687 DOI: 10.1016/j.compmedimag.2020.101717] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 02/14/2020] [Accepted: 03/10/2020] [Indexed: 12/15/2022]
Abstract
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis.
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Affiliation(s)
- Hisham Abdeltawab
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Fahmi Khalifa
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Fatma Taher
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Norah Saleh Alghamdi
- College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Saudi Arabia
| | - Mohammed Ghazal
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Garth Beache
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Tamer Mohamed
- Institute of Molecular Cardiology, University of Louisville, Louisville, KY 40202, USA
| | - Robert Keynton
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA.
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25
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de Marvao A, Dawes TJW, O'Regan DP. Artificial Intelligence for Cardiac Imaging-Genetics Research. Front Cardiovasc Med 2020; 6:195. [PMID: 32039240 PMCID: PMC6985036 DOI: 10.3389/fcvm.2019.00195] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/27/2019] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
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Affiliation(s)
| | | | - Declan P. O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
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Haddad SMH, Scott CJM, Ozzoude M, Holmes MF, Arnott SR, Nanayakkara ND, Ramirez J, Black SE, Dowlatshahi D, Strother SC, Swartz RH, Symons S, Montero-Odasso M, Bartha R. Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines. PLoS One 2019; 14:e0226715. [PMID: 31860686 PMCID: PMC6924651 DOI: 10.1371/journal.pone.0226715] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 12/02/2019] [Indexed: 12/29/2022] Open
Abstract
The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.
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Affiliation(s)
- Seyyed M. H. Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Miracle Ozzoude
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Melissa F. Holmes
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Nuwan D. Nanayakkara
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Joel Ramirez
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sandra E. Black
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada
| | | | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Richard H. Swartz
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Ontario, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, University of Western Ontario, London, Ontario, Canada
| | | | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
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Berger L, Mumtaz F. Will three-dimensional models change the way nephrometric scoring is carried out? BJU Int 2019; 124:898-899. [PMID: 31769141 DOI: 10.1111/bju.14907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Faiz Mumtaz
- Royal Free London NHS Foundation Trust, London, UK
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Standardized image post-processing of cardiovascular magnetic resonance T1-mapping reduces variability and improves accuracy and consistency in myocardial tissue characterization. Int J Cardiol 2019; 298:128-134. [PMID: 31500864 DOI: 10.1016/j.ijcard.2019.08.058] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/26/2019] [Accepted: 08/30/2019] [Indexed: 12/23/2022]
Abstract
BACKGROUND Myocardial T1-mapping is increasingly used in multicentre studies and trials. Inconsistent image analysis introduces variability, hinders differentiation of diseases, and results in larger sample sizes. We present a systematic approach to standardize T1-map analysis by human operators to improve accuracy and consistency. METHODS We developed a multi-step training program for T1-map post-processing. The training dataset contained 42 left ventricular (LV) short-axis T1-maps (normal and diseases; 1.5 and 3 Tesla). Contours drawn by two experienced human operators served as reference for myocardial T1 and wall thickness (WT). Trainees (n = 26) underwent training and were evaluated by: (a) qualitative review of contours; (b) quantitative comparison with reference T1 and WT. RESULTS The mean absolute difference between reference operators was 8.4 ± 6.3 ms (T1) and 1.2 ± 0.7 pixels (WT). Trainees' mean discrepancy from reference in T1 improved significantly post-training (from 8.1 ± 2.4 to 6.7 ± 1.4 ms; p < 0.001), with a 43% reduction in standard deviation (SD) (p = 0.035). WT also improved significantly post-training (from 0.9 ± 0.4 to 0.7 ± 0.2 pixels, p = 0.036), with 47% reduction in SD (p = 0.04). These experimentally-derived thresholds served to guide the training process: T1 (±8 ms) and WT (±1 pixel) from reference. CONCLUSION A standardized approach to CMR T1-map image post-processing leads to significant improvements in the accuracy and consistency of LV myocardial T1 values and wall thickness. Improving consistency between operators can translate into 33-72% reduction in clinical trial sample-sizes. This work may: (a) serve as a basis for re-certification for core-lab operators; (b) translate to sample-size reductions for clinical studies; (c) produce better-quality training datasets for machine learning.
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Siegersma KR, Leiner T, Chew DP, Appelman Y, Hofstra L, Verjans JW. Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist. Neth Heart J 2019; 27:403-413. [PMID: 31399886 PMCID: PMC6712136 DOI: 10.1007/s12471-019-01311-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician's perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.
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Affiliation(s)
- K R Siegersma
- Department of Cardiology, location VUmc, Amsterdam University Medical Centres, Amsterdam, The Netherlands.,Department of Experimental Cardiology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - T Leiner
- Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - D P Chew
- Department of Cardiovascular Medicine, Flinders Medical Centre, Bedford Park, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Y Appelman
- Department of Cardiology, location VUmc, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - L Hofstra
- Department of Cardiology, location VUmc, Amsterdam University Medical Centres, Amsterdam, The Netherlands.,Cardiologie Centra Nederland, Amsterdam, The Netherlands
| | - J W Verjans
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia. .,Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia. .,Dept of Cardiology, Royal Adelaide Hospital, Adelaide, SA, Australia.
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30
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Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King AP. Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. JACC Cardiovasc Imaging 2019; 13:684-695. [PMID: 31326477 PMCID: PMC7060799 DOI: 10.1016/j.jcmg.2019.05.030] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/26/2019] [Accepted: 05/16/2019] [Indexed: 12/13/2022]
Abstract
Objectives This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output. Background Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. Methods The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps’ ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. Results Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. Conclusions The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.
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Affiliation(s)
- Bram Ruijsink
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom.
| | - Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ilkay Oksuz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Matthew Sinclair
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Department of Medicine, Imperial College London, London, United Kingdom
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom
| | - Andrew P King
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Reiber JHC, Pereira GTR, Bezerra HG, De Sutter J, Schoenhagen P, Stillman AE, Van de Veire NRL. Cardiovascular imaging 2018 in the International Journal of Cardiovascular Imaging. Int J Cardiovasc Imaging 2019; 35:1175-1188. [DOI: 10.1007/s10554-019-01579-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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32
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Curiale AH, Colavecchia FD, Mato G. Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 169:37-50. [PMID: 30638590 DOI: 10.1016/j.cmpb.2018.12.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 11/16/2018] [Accepted: 12/10/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVE This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN). METHODS The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance as optimization objective function. RESULTS The CNNs were trained and evaluated with 140 patients from two public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas Project) by using a 5-fold cross-validation strategy. Our results demonstrate a suitable accuracy for myocardial segmentation ( ∼ 0.9 Dice's coefficient), and a strong correlation with the most relevant physiological measures: 0.99 for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass, 0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac output. CONCLUSION Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN to estimate different structural and functional features such as LV mass and EF which are commonly used for both diagnosis and treatment of different pathologies. SIGNIFICANCE This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring.
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Affiliation(s)
- Ariel H Curiale
- CONICET - Departamento de Física Médica, Centro Atómico Bariloche, Av. Bustillo 9500, S. C. de Bariloche, Río Negro, 8400 Argentina. http://www.curiale.com.ar
| | - Flavio D Colavecchia
- CONICET - Centro Integral de Medicina Nuclear y Radioterapia, Centro Atómico Bariloche, Av. Bustillo 9500, S. C. de Bariloche, Río Negro, 8400 Argentina; Comisión Nacional de Energía Atómica (CNEA) Argentina
| | - German Mato
- CONICET - Departamento de Física Médica, Centro Atómico Bariloche, Av. Bustillo 9500, S. C. de Bariloche, Río Negro, 8400 Argentina; Comisión Nacional de Energía Atómica (CNEA) Argentina
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34
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Kerkhof DL, Lucas C, Corrado GD. Monitoring Morphologic Changes in Male Rowers Using Limited Portable Echocardiography Performed by a Frontline Physician. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2018; 37:2451-2455. [PMID: 29575042 DOI: 10.1002/jum.14596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 01/13/2018] [Accepted: 01/17/2018] [Indexed: 06/08/2023]
Abstract
Athletes' hearts have been studied for adaptive changes in response to exercise. Physiologic changes are normal responses to intense athletic training regimens, whereas pathologic changes predispose athletes to sudden cardiac death. The two alterations overlap in clinical presentation. Research continues to investigate the upper limits of cardiac remodeling to aid clinical decision making. Studying normal changes that occur in response to exercise will advance physicians' understanding of physiologic responses to exercise and potentially improve clinical distinction. To expand this body of knowledge, we present an observational case series that describes morphologic changes in athlete's hearts concurrent with performance measurements.
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Affiliation(s)
| | | | - Gianmichel D Corrado
- Northeastern University, Boston, Massachusetts, USA
- Division of Sports Medicine, Department of Orthopedics, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Micheli Center for Sports Injury Prevention, Waltham, Massachusetts, USA
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35
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Panayiotou M, Housden RJ, Ishak A, Brost A, Rinaldi CA, Sieniewicz B, Behar JM, Kurzendorfer T, Rhode KS. LV function validation of computer-assisted interventional system for cardiac resyncronisation therapy. Int J Comput Assist Radiol Surg 2018; 13:777-786. [PMID: 29603064 PMCID: PMC5974009 DOI: 10.1007/s11548-018-1748-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 03/21/2018] [Indexed: 12/01/2022]
Abstract
PURPOSE Cardiac resynchronisation therapy (CRT) is an established treatment for symptomatic patients with heart failure, a prolonged QRS duration, and impaired left ventricular (LV) function; however, non-response rates remain high. Recently proposed computer-assisted interventional platforms for CRT provide new routes to improving outcomes. Interventional systems must process information in an accurate, fast and highly automated way that is easy for the interventional cardiologists to use. In this paper, an interventional CRT platform is validated against two offline diagnostic tools to demonstrate that accurate information processing is possible in the time critical interventional setting. METHODS The study consisted of 3 healthy volunteers and 16 patients with heart failure and conventional criteria for CRT. Data analysis included the calculation of end-diastolic volume, end-systolic volume, stroke volume and ejection fraction; computation of global volume over the cardiac cycle as well as time to maximal contraction expressed as a percentage of the total cardiac cycle. RESULTS The results showed excellent correlation ([Formula: see text] values of [Formula: see text] and Pearson correlation coefficient of [Formula: see text]) with comparable offline diagnostic tools. CONCLUSION Results confirm that our interventional system has good accuracy in everyday clinical practice and can be of clinical utility in identification of CRT responders and LV function assessment.
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Affiliation(s)
- Maria Panayiotou
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
| | - R James Housden
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Athanasius Ishak
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | - Christopher A Rinaldi
- Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Benjamin Sieniewicz
- Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Jonathan M Behar
- Department of Cardiology, Guy's and St. Thomas' Hospitals NHS Foundation Trust, London, UK
| | | | - Kawal S Rhode
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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