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Moosavi A, Huang S, Vahabi M, Motamedivafa B, Tian N, Mahmood R, Liu P, Sun CL. Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review. JACC. ADVANCES 2024; 3:101202. [PMID: 39372457 PMCID: PMC11450923 DOI: 10.1016/j.jacadv.2024.101202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 10/08/2024]
Abstract
Background Despite the potential of artificial intelligence (AI) in enhancing cardiovascular care, its integration into clinical practice is limited by a lack of evidence on its effectiveness with respect to human experts or gold standard practices in real-world settings. Objectives The purpose of this study was to identify AI interventions in cardiology that have been prospectively validated against human expert benchmarks or gold standard practices, assessing their effectiveness, and identifying future research areas. Methods We systematically reviewed Scopus and MEDLINE to identify peer-reviewed publications that involved prospective human validation of AI-based interventions in cardiology from January 2015 to December 2023. Results Of 2,351 initial records, 64 studies were included. Among these studies, 59 (92.2%) were published after 2020. A total of 11 (17.2%) randomized controlled trials were published. AI interventions in 44 articles (68.75%) reported definite clinical or operational improvements over human experts. These interventions were mostly used in imaging (n = 14, 21.9%), ejection fraction (n = 10, 15.6%), arrhythmia (n = 9, 14.1%), and coronary artery disease (n = 12, 18.8%) application areas. Convolutional neural networks were the most common predictive model (n = 44, 69%), and images were the most used data type (n = 38, 54.3%). Only 22 (34.4%) studies made their models or data accessible. Conclusions This review identifies the potential of AI in cardiology, with models often performing equally well as human counterparts for specific and clearly scoped tasks suitable for such models. Nonetheless, the limited number of randomized controlled trials emphasizes the need for continued validation, especially in real-world settings that closely examine joint human AI decision-making.
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Affiliation(s)
- Amirhossein Moosavi
- Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
- University of Ottawa Heart Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Steven Huang
- University of Ottawa Heart Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Maryam Vahabi
- Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
- University of Ottawa Heart Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Bahar Motamedivafa
- Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
- University of Ottawa Heart Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Nelly Tian
- Marshall School of Business, University of Southern California, Los Angeles, California, USA
| | - Rafid Mahmood
- Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
| | - Peter Liu
- University of Ottawa Heart Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Christopher L.F. Sun
- Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
- University of Ottawa Heart Institute, University of Ottawa, Ottawa, Ontario, Canada
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2
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Manini C, Hüllebrand M, Walczak L, Nordmeyer S, Jarmatz L, Kuehne T, Stern H, Meierhofer C, Harloff A, Erley J, Kelle S, Bannas P, Trauzeddel RF, Schulz-Menger J, Hennemuth A. Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging. J Cardiovasc Magn Reson 2024; 26:101081. [PMID: 39127260 PMCID: PMC11422555 DOI: 10.1016/j.jocmr.2024.101081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 07/08/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow CMR segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies. METHODS The study population consists of 260 4D flow CMR datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics, such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced three-dimensional U-net convolutional neural network (CNN) architecture with residual units was trained for time-resolved two-dimensional aortic cross-sectional segmentation. Model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation. RESULTS The representation of technical factors, such as scanner vendor and field strength, in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients' datasets performed well on datasets of healthy subjects but not vice versa. CONCLUSION This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow CMR, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.
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Affiliation(s)
- Chiara Manini
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
| | - Markus Hüllebrand
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Fraunhofer MEVIS, Berlin, Germany
| | - Lars Walczak
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Fraunhofer MEVIS, Berlin, Germany
| | - Sarah Nordmeyer
- Department of Diagnostic and Interventional Radiology, Tübingen University Hospital, Tübingen, Germany
| | - Lina Jarmatz
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
| | - Titus Kuehne
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; German Center for Cardiovascular Research (DZHK), Berlin, Germany
| | - Heiko Stern
- Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Munich, Germany
| | - Christian Meierhofer
- Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Munich, Germany
| | - Andreas Harloff
- Department of Neurology and Neurophysiology, University Medical Center Freiburg - Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jennifer Erley
- German Center for Cardiovascular Research (DZHK), Berlin, Germany; Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Sebastian Kelle
- German Center for Cardiovascular Research (DZHK), Berlin, Germany; Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Peter Bannas
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ralf Felix Trauzeddel
- German Center for Cardiovascular Research (DZHK), Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany; Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité - Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine, Charité Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Jeanette Schulz-Menger
- German Center for Cardiovascular Research (DZHK), Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany; Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité - Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany; Department of Cardiology and Nephrology, Helios Hospital Berlin-Buch, Berlin, Germany
| | - Anja Hennemuth
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Fraunhofer MEVIS, Berlin, Germany; German Center for Cardiovascular Research (DZHK), Berlin, Germany; Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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3
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Aromiwura AA, Cavalcante JL, Kwong RY, Ghazipour A, Amini A, Bax J, Raman S, Pontone G, Kalra DK. The role of artificial intelligence in cardiovascular magnetic resonance imaging. Prog Cardiovasc Dis 2024:S0033-0620(24)00092-6. [PMID: 38925255 DOI: 10.1016/j.pcad.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.
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Affiliation(s)
| | | | - Raymond Y Kwong
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aryan Ghazipour
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Jeroen Bax
- Department of Cardiology, Leiden University, Leiden, the Netherlands
| | - Subha Raman
- Division of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Gianluca Pontone
- Department of Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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4
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Zhang Q, Fotaki A, Ghadimi S, Wang Y, Doneva M, Wetzl J, Delfino JG, O'Regan DP, Prieto C, Epstein FH. Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation. J Cardiovasc Magn Reson 2024; 26:101051. [PMID: 38909656 PMCID: PMC11331970 DOI: 10.1016/j.jocmr.2024.101051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. METHODS Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. RESULTS These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. CONCLUSIONS Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
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Affiliation(s)
- Qiang Zhang
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK.
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | | | - Jens Wetzl
- Siemens Healthineers AG, Erlangen, Germany.
| | - Jana G Delfino
- US Food and Drug Administration, Center for Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), Silver Spring, MD, USA.
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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5
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Onnis C, van Assen M, Muscogiuri E, Muscogiuri G, Gershon G, Saba L, De Cecco CN. The Role of Artificial Intelligence in Cardiac Imaging. Radiol Clin North Am 2024; 62:473-488. [PMID: 38553181 DOI: 10.1016/j.rcl.2024.01.002] [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] [Indexed: 04/02/2024]
Abstract
Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.
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Affiliation(s)
- Carlotta Onnis
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/CarlottaOnnis
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/marly_van_assen
| | - Emanuele Muscogiuri
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Thoracic Imaging, Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Giuseppe Muscogiuri
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Piazza OMS, 1, Bergamo BG 24127, Italy. https://twitter.com/GiuseppeMuscog
| | - Gabrielle Gershon
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/gabbygershon
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/lucasabaITA
| | - Carlo N De Cecco
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Emory University Hospital, 1365 Clifton Road Northeast, Suite AT503, Atlanta, GA 30322, USA.
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6
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Wang X, Pu J. Recent Advances in Cardiac Magnetic Resonance for Imaging of Acute Myocardial Infarction. SMALL METHODS 2024; 8:e2301170. [PMID: 37992241 DOI: 10.1002/smtd.202301170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/14/2023] [Indexed: 11/24/2023]
Abstract
Acute myocardial infarction (AMI) is one of the primary causes of death worldwide, with a high incidence and mortality rate. Assessment of the infarcted and surviving myocardium, along with microvascular obstruction, is crucial for risk stratification, treatment, and prognosis in patients with AMI. Nonionizing radiation, excellent soft tissue contrast resolution, a large field of view, and multiplane imaging make cardiac magnetic resonance (CMR) a "one-stop" method for assessing cardiac structure, function, perfusion, and metabolism. Hence, this imaging technology is considered the "gold standard" for evaluating myocardial function and viability in AMI. This review critically compares the advantages and disadvantages of CMR with other cardiac imaging technologies, and relates the imaging findings to the underlying pathophysiological processes in AMI. A more thorough understanding of CMR technology will clarify their advanced clinical diagnosis and prognostic assessment applications, and assess the future approaches and challenges of CMR in the setting of AMI.
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Affiliation(s)
- Xu Wang
- Shanghai Jiao Tong University, School of Medicine Affiliated Renji Hospital, Shanghai, 200127, China
| | - Jun Pu
- Shanghai Jiao Tong University, School of Medicine Affiliated Renji Hospital, Shanghai, 200127, China
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7
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Kamal NM, Salih AF, Ali BM. Speckle tracking echocardiography for diagnosis of right ventricular failure in children with totally corrected tetralogy of Fallot in Sulaimani, Iraq. J Taibah Univ Med Sci 2024; 19:198-208. [PMID: 38124989 PMCID: PMC10730916 DOI: 10.1016/j.jtumed.2023.11.005] [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: 07/05/2023] [Revised: 09/21/2023] [Accepted: 11/09/2023] [Indexed: 12/23/2023] Open
Abstract
Objectives The study was aimed at using speckle tracking echocardiography as a novel technique to diagnose right ventricular failure (RVF) in children with total correction of tetralogy of Fallot (TOF) through surgery. Methods A quasi-experimental study was performed at the Children's Heart Hospital of Sulaimani for 9 months. A total of 150 children with completely repaired TOF were enrolled to investigate RVF. Conventional echocardiographic data were recorded, including right ventricular (RV) ejection fraction (EF), tricuspid annular plane systolic excursion (TAPSE), myocardial performance index (MPI), and RV end-systolic and diastolic volume (RVESV and RVEDV). Additionally, speckle tracking was performed for the regional and longitudinal strain and strain rate in four-chamber apical view. RVF diagnosis was determined on the basis of electrocardiography measurement of P-wave dispersion, T-wave dispersion, and QRS duration. Results Children with repaired TOF who were diagnosed with RVF through conventional echocardiography exhibited abnormalities with respect to children with normal RV function, including a TAPSE of 1.3 ± 0.11 cm, RVEF of 35.5 ± 6.72, RVESV of 69.8 ± 15.13 ml, RVEDV of 110.1 ± 14.13 ml, MPI of 0.60 ± 0.12, and Pmax of 52.4 ± 14.08. The use of speckle tracking in RVF diagnosis revealed a relatively lower longitudinal strain and strain rate (-12.1 ± 2.3 and -0.9 ± 0.3, respectively) in the children with RVF. Moreover, longitudinal right ventricular strain was positively correlated with TAPSE (r = 0.656) and EF (r = 0.675), and negatively correlated with RVEDV (r = -0.684), RVESV (r = -0.718), MPI (r = -0.735), and Pmax (r = -0.767). Conclusions The application of speckle tracking with the longitudinal RV strain and strain rate to estimate RV function in children with repaired TOF is a new advanced method that, compared with conventional echo, significantly improves the diagnosis of regional myocardial deformations and cardiac muscle motion velocity.
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Affiliation(s)
- Niaz M. Kamal
- Pediatrics Department, Technical Institute, Sulaymaniyah Polytechnic University, Sulaymaniyah, Iraq
| | - Aso F. Salih
- Pediatrics Department, Medicine College, Sulaymaniyah University, Sulaymaniyah, Iraq
| | - Bushra M. Ali
- Family and Community Medicine Department, Medicine College, Sulaymaniyah University, Sulaymaniyah, Iraq
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Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F. Artificial Intelligence in Heart Failure: Friend or Foe? Life (Basel) 2024; 14:145. [PMID: 38276274 PMCID: PMC10817517 DOI: 10.3390/life14010145] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
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Affiliation(s)
- Angeliki Bourazana
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Alexandros Briasoulis
- Division of Cardiovascular Medicine, Section of Heart Failure and Transplantation, University of Iowa, Iowa City, IA 52242, USA
| | - Dimitrios Magouliotis
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London W2 1NY, UK
| | - George Vassilopoulos
- Department of Hematology, University Hospital of Larissa, University of Thessaly Medical School, 41110 Larissa, Greece
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
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9
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Zhang Y, Wang M, Zhang E, Wu Y. Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis. Rev Cardiovasc Med 2024; 25:31. [PMID: 39077660 PMCID: PMC11262349 DOI: 10.31083/j.rcm2501031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 07/31/2024] Open
Abstract
The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI's promising role in the AS clinical pathway.
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Affiliation(s)
- Yuxuan Zhang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Moyang Wang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Erli Zhang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Yongjian Wu
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
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Ferrer-Sistach E, Teis A, Escabia C, Delgado V. Assessment of the Severity of Aortic Regurgitation by Noninvasive Imaging : Non-invasive MMI for AR. Curr Cardiol Rep 2024; 26:1-14. [PMID: 38091195 DOI: 10.1007/s11886-023-02011-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/04/2023] [Indexed: 01/27/2024]
Abstract
PURPOSE OF THE REVIEW The role of multimodality imaging in the evaluation of patients with aortic regurgitation is summarized in this review. RECENT FINDINGS The etiology (mechanism) of the aortic regurgitation and the severity of aortic regurgitation and hemodynamic consequences are key in the decision making of patients with severe aortic regurgitation. While echocardiography remains as the leading technique to assess all these parameters, other imaging techniques have become essential for the accurate assessment of aortic regurgitation severity and the timing of aortic intervention. The anatomic suitability of transcatheter aortic valve implantation in inoperable patients with severe aortic regurgitation is usually assessed with computed tomography. Aortic regurgitation is a prevalent disease with various pathophysiological mechanisms that need a personalized treatment. The evaluation of the mechanism and severity of aortic regurgitation can be initially performed with echocardiography. Three-dimensional techniques, including echocardiography, have become very relevant for accurate assessment of the regurgitation severity and its hemodynamic consequences. Assessment of myocardial tissue characteristics with cardiac magnetic resonance is key in the risk stratification of patients and in the timing of aortic intervention. Computed tomography is important in the assessment of aortic dimensions and selection of patients for transcatheter aortic valve implantation.
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Affiliation(s)
- Elena Ferrer-Sistach
- Heart Institute, University Hospital Germans Trias I Pujol, Carretera de Canyet S/N, 08916, Badalona, Spain
| | - Albert Teis
- Heart Institute, University Hospital Germans Trias I Pujol, Carretera de Canyet S/N, 08916, Badalona, Spain
| | - Claudia Escabia
- Heart Institute, University Hospital Germans Trias I Pujol, Carretera de Canyet S/N, 08916, Badalona, Spain
| | - Victoria Delgado
- Heart Institute, University Hospital Germans Trias I Pujol, Carretera de Canyet S/N, 08916, Badalona, Spain.
- Center for Comparative Medicine and Bioimaging (CMCIB), Germans Trias I Pujol Research Institute (IGTP), Badalona, Spain.
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11
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Wehbe RM, Katsaggelos AK, Hammond KJ, Hong H, Ahmad FS, Ouyang D, Shah SJ, McCarthy PM, Thomas JD. Deep Learning for Cardiovascular Imaging: A Review. JAMA Cardiol 2023; 8:1089-1098. [PMID: 37728933 DOI: 10.1001/jamacardio.2023.3142] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Importance Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology. Observations At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI. Conclusions and Relevance Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.
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Affiliation(s)
- Ramsey M Wehbe
- Division of Cardiology, Department of Medicine & Biomedical Informatics Center, Medical University of South Carolina, Charleston
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Aggelos K Katsaggelos
- Department of Computer and Electrical Engineering, Northwestern University, Evanston, Illinois
| | - Kristian J Hammond
- Department of Computer Science, Northwestern University, Evanston, Illinois
| | - Ha Hong
- Medtronic, Minneapolis, Minnesota
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - David Ouyang
- Division of Cardiology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sanjiv J Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - Patrick M McCarthy
- Division of Cardiac Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - James D Thomas
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
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12
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Lee KCS, Breznen B, Ukhova A, Martin SS, Koehler F. Virtual healthcare solutions in heart failure: a literature review. Front Cardiovasc Med 2023; 10:1231000. [PMID: 37745104 PMCID: PMC10513031 DOI: 10.3389/fcvm.2023.1231000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
The widespread adoption of mobile technologies offers an opportunity for a new approach to post-discharge care for patients with heart failure (HF). By enabling non-invasive remote monitoring and two-way, real-time communication between the clinic and home-based patients, as well as a host of other capabilities, mobile technologies have a potential to significantly improve remote patient care. This literature review summarizes clinical evidence related to virtual healthcare (VHC), defined as a care team + connected devices + a digital solution in post-release care of patients with HF. Searches were conducted on Embase (06/12/2020). A total of 171 studies were included for data extraction and evidence synthesis: 96 studies related to VHC efficacy, and 75 studies related to AI in HF. In addition, 15 publications were included from the search on studies scaling up VHC solutions in HF within the real-world setting. The most successful VHC interventions, as measured by the number of reported significant results, were those targeting reduction in rehospitalization rates. In terms of relative success rate, the two most effective interventions targeted patient self-care and all-cause hospital visits in their primary endpoint. Among the three categories of VHC identified in this review (telemonitoring, remote patient management, and patient self-empowerment) the integrated approach in remote patient management solutions performs the best in decreasing HF patients' re-admission rates and overall hospital visits. Given the increased amount of data generated by VHC technologies, artificial intelligence (AI) is being investigated as a tool to aid decision making in the context of primary diagnostics, identifying disease phenotypes, and predicting treatment outcomes. Currently, most AI algorithms are developed using data gathered in clinic and only a few studies deploy AI in the context of VHC. Most successes have been reported in predicting HF outcomes. Since the field of VHC in HF is relatively new and still in flux, this is not a typical systematic review capturing all published studies within this domain. Although the standard methodology for this type of reviews was followed, the nature of this review is qualitative. The main objective was to summarize the most promising results and identify potential research directions.
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Affiliation(s)
| | - Boris Breznen
- Evidence Synthesis, Evidinno Outcomes Research Inc., Vancouver, BC, Canada
| | | | - Seth Shay Martin
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Friedrich Koehler
- Deutsches Herzzentrum der Charité (DHZC), Centre for Cardiovascular Telemedicine, Campus Charité Mitte, Berlin, Germany
- Division of Cardiology and Angiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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13
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Tahir AM, Mutlu O, Bensaali F, Ward R, Ghareeb AN, Helmy SMHA, Othman KT, Al-Hashemi MA, Abujalala S, Chowdhury MEH, Alnabti ARDMH, Yalcin HC. Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes. J Clin Med 2023; 12:4774. [PMID: 37510889 PMCID: PMC10381346 DOI: 10.3390/jcm12144774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/08/2023] [Accepted: 04/10/2023] [Indexed: 07/30/2023] Open
Abstract
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
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Affiliation(s)
- Anas M Tahir
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Onur Mutlu
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Faycal Bensaali
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Rabab Ward
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Abdel Naser Ghareeb
- Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
- Faculty of Medicine, Al Azhar University, Cairo 11884, Egypt
| | - Sherif M H A Helmy
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | - Mohammed A Al-Hashemi
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | | | | | - Huseyin C Yalcin
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar
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14
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Barrera-Naranjo A, Marin-Castrillon DM, Decourselle T, Lin S, Leclerc S, Morgant MC, Bernard C, De Oliveira S, Boucher A, Presles B, Bouchot O, Christophe JJ, Lalande A. Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets. J Imaging 2023; 9:123. [PMID: 37367471 DOI: 10.3390/jimaging9060123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.
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Affiliation(s)
| | | | | | - Siyu Lin
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Sarah Leclerc
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Marie-Catherine Morgant
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, 21078 Dijon, France
| | - Chloé Bernard
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, 21078 Dijon, France
| | | | - Arnaud Boucher
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Benoit Presles
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Olivier Bouchot
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, 21078 Dijon, France
| | | | - Alain Lalande
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Medical Imaging, University Hospital of Dijon, 21078 Dijon, France
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15
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Yang T, Zhu G, Cai L, Yeo JH, Mao Y, Yang J. A benchmark study of convolutional neural networks in fully automatic segmentation of aortic root. Front Bioeng Biotechnol 2023; 11:1171868. [PMID: 37397959 PMCID: PMC10311214 DOI: 10.3389/fbioe.2023.1171868] [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: 02/22/2023] [Accepted: 06/06/2023] [Indexed: 07/04/2023] Open
Abstract
Recent clinical studies have suggested that introducing 3D patient-specific aortic root models into the pre-operative assessment procedure of transcatheter aortic valve replacement (TAVR) would reduce the incident rate of peri-operative complications. Tradition manual segmentation is labor-intensive and low-efficient, which cannot meet the clinical demands of processing large data volumes. Recent developments in machine learning provided a viable way for accurate and efficient medical image segmentation for 3D patient-specific models automatically. This study quantitively evaluated the auto segmentation quality and efficiency of the four popular segmentation-dedicated three-dimensional (3D) convolutional neural network (CNN) architectures, including 3D UNet, VNet, 3D Res-UNet and SegResNet. All the CNNs were implemented in PyTorch platform, and low-dose CTA image sets of 98 anonymized patients were retrospectively selected from the database for training and testing of the CNNs. The results showed that despite all four 3D CNNs having similar recall, Dice similarity coefficient (DSC), and Jaccard index on the segmentation of the aortic root, the Hausdorff distance (HD) of the segmentation results from 3D Res-UNet is 8.56 ± 2.28, which is only 9.8% higher than that of VNet, but 25.5% and 86.4% lower than that of 3D UNet and SegResNet, respectively. In addition, 3D Res-UNet and VNet also performed better in the 3D deviation location of interest analysis focusing on the aortic valve and the bottom of the aortic root. Although 3D Res-UNet and VNet are evenly matched in the aspect of classical segmentation quality evaluation metrics and 3D deviation location of interest analysis, 3D Res-UNet is the most efficient CNN architecture with an average segmentation time of 0.10 ± 0.04 s, which is 91.2%, 95.3% and 64.3% faster than 3D UNet, VNet and SegResNet, respectively. The results from this study suggested that 3D Res-UNet is a suitable candidate for accurate and fast automatic aortic root segmentation for pre-operative assessment of TAVR.
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Affiliation(s)
- Tingting Yang
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Guangyu Zhu
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Li Cai
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, China
| | - Joon Hock Yeo
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yu Mao
- Department of Cardiac Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Jian Yang
- Department of Cardiac Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
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16
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Schäfer M, Carroll A, Carmody KK, Hunter KS, Barker AJ, Aftab M, Reece TB. Aortic shape variation after frozen elephant trunk procedure predicts aortic events: Principal component analysis study. JTCVS OPEN 2023; 14:26-35. [PMID: 37425456 PMCID: PMC10328758 DOI: 10.1016/j.xjon.2023.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/26/2023] [Indexed: 07/11/2023]
Abstract
Objective The frozen elephant trunk procedure is a well-established technique for the repair of type A ascending aortic dissection and complex aortic arch pathology. The ultimate shape created by the repair may have consequences in long-term complications. The purpose of this study was to apply a machine learning technique to comprehensively describe 3-dimensional aortic shape variations after the frozen elephant trunk procedure and associate these variations with aortic events. Methods Computed tomography angiography acquired before discharge of patients (n = 93) who underwent the frozen elephant trunk procedure for type A ascending aortic dissection or ascending aortic arch aneurysm was preprocessed to yield patient-specific aortic models and centerlines. Aortic centerlines were subjected to principal component analysis to describe principal components and aortic shape modulators. Patient-specific shape scores were correlated with outcomes defined by composite aortic event, including aortic rupture, aortic root dissection or pseudoaneurysm, new type B dissection, new thoracic or thoracoabdominal pathologies, residual descending aortic dissection with residual false lumen flow, or thoracic endovascular aortic repair complications. Results The first 3 principal components accounted for 36.4%, 26.4%, and 11.6% of aortic shape variance, respectively, and cumulatively for 74.5% of the total shape variation in all patients. The first principal component described variation in arch height-to-length ratio, the second principal component described angle at the isthmus, and the third principal component described variation in anterior-to-posterior arch tilt. Twenty-one aortic events (22.6%) were encountered. The degree of aortic angle at the isthmus described by the second principal component was associated with aortic events in logistic regression (hazard ratio, 0.98; 95% confidence interval, 0.97-0.99; P = .046). Conclusions The second principal component, describing angulation at the region of the aortic isthmus, was associated with adverse aortic events. Observed shape variation should be evaluated in the context of aortic biomechanical properties and flow hemodynamics.
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Affiliation(s)
- Michal Schäfer
- Division of Cardiology, Heart Institute, Children's Hospital Colorado, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
| | - Adam Carroll
- Department of Surgery, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
| | - Kody K. Carmody
- Division of Cardiology, Heart Institute, Children's Hospital Colorado, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
| | - Kendall S. Hunter
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
| | - Alex J. Barker
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
- Department of Radiology, Children's Hospital Colorado, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
| | - Muhammad Aftab
- Division of Cardiothoracic Surgery, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
| | - T. Brett Reece
- Division of Cardiothoracic Surgery, University of Colorado Denver Anschutz Medical Campus, Denver, Colo
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17
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Ferdian E, Marlevi D, Schollenberger J, Aristova M, Edelman ER, Schnell S, Figueroa CA, Nordsletten DA, Young AA. Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure. Med Image Anal 2023; 88:102831. [PMID: 37244143 DOI: 10.1016/j.media.2023.102831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 04/04/2023] [Accepted: 04/20/2023] [Indexed: 05/29/2023]
Abstract
The development of cerebrovascular disease is tightly coupled to regional changes in intracranial flow and relative pressure. Image-based assessment using phase contrast magnetic resonance imaging has particular promise for non-invasive full-field mapping of cerebrovascular hemodynamics. However, estimations are complicated by the narrow and tortuous intracranial vasculature, with accurate image-based quantification directly dependent on sufficient spatial resolution. Further, extended scan times are required for high-resolution acquisitions, and most clinical acquisitions are performed at comparably low resolution (>1 mm) where biases have been observed with regard to the quantification of both flow and relative pressure. The aim of our study was to develop an approach for quantitative intracranial super-resolution 4D Flow MRI, with effective resolution enhancement achieved by a dedicated deep residual network, and with accurate quantification of functional relative pressures achieved by subsequent physics-informed image processing. To achieve this, our two-step approach was trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 15.0 ± 0.1%, mean absolute error (MAE): 0.07 ± 0.06 m/s, and cosine similarity: 0.99 ± 0.06 at peak velocity) and flow (relative error: 6.6 ± 4.7%, root mean square error (RMSE): 0.56 mL/s at peak flow), and with the coupled physics-informed image analysis allowing for maintained recovery of functional relative pressure throughout the circle of Willis (relative error: 11.0 ± 7.3%, RMSE: 0.3 ± 0.2 mmHg). Furthermore, the quantitative super-resolution approach is applied to an in-vivo volunteer cohort, effectively generating intracranial flow images at <0.5 mm resolution and showing reduced low-resolution bias in relative pressure estimation. Our work thus presents a promising two-step approach to non-invasively quantify cerebrovascular hemodynamics, being applicable to dedicated clinical cohorts in the future.
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Affiliation(s)
- E Ferdian
- University of Auckland, Auckland 1142 New Zealand
| | - D Marlevi
- Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | | | - M Aristova
- Northwestern University, Chicago, IL 60611, USA
| | - E R Edelman
- Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - S Schnell
- Northwestern University, Chicago, IL 60611, USA; University of Greifswald, Greifswald 17489, Germany
| | - C A Figueroa
- University of Michigan, Ann Arbor, MI 48109, USA
| | - D A Nordsletten
- University of Michigan, Ann Arbor, MI 48109, USA; King's College London, London, SE1 7EH, UK
| | - A A Young
- University of Auckland, Auckland 1142 New Zealand; King's College London, London, SE1 7EH, UK
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18
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Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T 1 and T 2 Relaxation Times with Application to Cancer Cell Culture. Int J Mol Sci 2023; 24:ijms24021554. [PMID: 36675075 PMCID: PMC9861169 DOI: 10.3390/ijms24021554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.
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19
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Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI. Eur Radiol 2023; 33:1707-1718. [PMID: 36307551 PMCID: PMC9935671 DOI: 10.1007/s00330-022-09179-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) application for fully-automated VCD and flow quantification and corrected semi-automated analysis (corSAA). METHODS We included 97 consecutive patients (age = 52.9 ± 16 years, 41 female) with 2D-CINE-PC-MRI imaging on 1.5T MRI systems at sinotubular junction (STJ), and 28/97 also received 2D-CINE-PC at main pulmonary artery (PA). A cardiovascular radiologist performed MA (reference) and corSAA (built-in tool) in commercial software for all cardiac time frames (median: 20, total contours per analysis: 2358 STJ, 680 PA). DL-analysis automatically performed VCD, followed by net flow (NF) and peak velocity (PV) quantification. Contours were compared using Dice similarity coefficients (DSC). Discrepant cases (> ± 10 mL or > ± 10 cm/s) were reviewed in detail. RESULTS DL was successfully applied to 97% (121/125) of the 2D-CINE-PC-MRI series (STJ: 95/97, 98%, PA: 26/28, 93%). Compared to MA, mean DSC were 0.91 ± 0.02 (DL), 0.94 ± 0.02 (corSAA) at STJ, and 0.85 ± 0.08 (DL), 0.93 ± 0.02 (corSAA) at PA; this indicated good to excellent DL-performance. Flow quantification revealed similar NF at STJ (p = 0.48) and PA (p > 0.05) between methods while PV assessment was significantly different (STJ: p < 0.001, PA: p = 0.04). A detailed review showed noisy voxels in MA and corSAA impacted PV results. Overall, DL analysis compared to human assessments was accurate in 113/121 (93.4%) cases. CONCLUSIONS Fully-automated DL-analysis of 2D-CINE-PC-MRI provided flow quantification at STJ and PA at expert level in > 93% of cases with results being available instantaneously. KEY POINTS • Deep learning performed flow quantification on clinical 2D-CINE-PC series at the sinotubular junction and pulmonary artery at the expert level in > 93% of cases. • Location detection and contouring of the vessel boundaries were performed fully-automatic with results being available instantaneously compared to human assessments which approximately takes three minutes per location. • The evaluated tool indicates usability in daily practice.
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Peper ES, van Ooij P, Jung B, Huber A, Gräni C, Bastiaansen JAM. Advances in machine learning applications for cardiovascular 4D flow MRI. Front Cardiovasc Med 2022; 9:1052068. [PMID: 36568555 PMCID: PMC9780299 DOI: 10.3389/fcvm.2022.1052068] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow.
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Affiliation(s)
- Eva S. Peper
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland,*Correspondence: Eva S. Peper,
| | - Pim van Ooij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands,Department of Pediatric Cardiology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bernd Jung
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Adrian Huber
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jessica A. M. Bastiaansen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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21
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Yan J, Tian J, Yang H, Han G, Liu Y, He H, Han Q, Zhang Y. A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease. Comput Biol Med 2022; 151:106300. [PMID: 36410096 DOI: 10.1016/j.compbiomed.2022.106300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/16/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
Invasive coronary angiography imposes risks and high medical costs. Therefore, accurate, reliable, non-invasive, and cost-effective methods for diagnosing coronary stenosis are required. We designed a machine learning-based risk-prediction system as an accurate, noninvasive, and cost-effective alternative method for evaluating suspected coronary heart disease (CHD) patients. Electronic medical record data were collected from suspected CHD patients undergoing coronary angiography between May 1, 2017, and December 31, 2019. Multi-Class XGBoost, LightGBM, Random Forest, NGBoost, logistic models and MLP were constructed to identify patients with normal coronary arteries (class 0: no coronary artery stenosis), minimum coronary artery stenosis (class 1: 0 < stenosis <50%), and CHD (class 2: stenosis ≥50%). Model stability was verified externally. A risk-assessment and management system was established for patient-specific intervention guidance. Of 1577 suspected CHD patients, 81 (5.14%) had normal coronary arteries. The XGBoost model demonstrated the best overall classification performance (micro-average receiver operating characteristic [ROC] curve: 0.92, macro-average ROC curve: 0.89, class 0 ROC curve: 0.88, class 1 ROC curve: 0.90, class 2 ROC curve: 0.89), with good external verification. In class-specific classification, the XGBoost model yielded F1 values of 0.636, 0.850, and 0.858, for Classes 0, 1, and 2, respectively. The visualization system allowed disease diagnosis and probability estimation, and identified the intervention focus for individual patients. Thus, the system distinguished coronary artery stenosis well in suspected CHD patients. Personalized probability curves provide individualized intervention guidance. This may reduce the number of invasive inspections in negative patients, while facilitating decision-making regarding appropriate medical intervention, improving patient prognosis.
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Affiliation(s)
- Jingjing Yan
- Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China
| | - Jing Tian
- Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China; Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Hong Yang
- Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China
| | - Gangfei Han
- Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China
| | - Yanling Liu
- Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China
| | - Hangzhi He
- Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China
| | - Qinghua Han
- Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China.
| | - Yanbo Zhang
- Department of Health Statistics, School of Public Health, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Taiyuan, China; Shanxi University of Chinese Medicine, Taiyuan, China.
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22
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Dirix P, Buoso S, Peper ES, Kozerke S. Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves. Sci Rep 2022; 12:16004. [PMID: 36163357 PMCID: PMC9513106 DOI: 10.1038/s41598-022-20121-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/08/2022] [Indexed: 11/09/2022] Open
Abstract
We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier-Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.
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Affiliation(s)
- Pietro Dirix
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
| | - Stefano Buoso
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Eva S Peper
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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23
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Popa IP, Haba MȘC, Mărănducă MA, Tănase DM, Șerban DN, Șerban LI, Iliescu R, Tudorancea I. Modern Approaches for the Treatment of Heart Failure: Recent Advances and Future Perspectives. Pharmaceutics 2022; 14:1964. [PMID: 36145711 PMCID: PMC9503448 DOI: 10.3390/pharmaceutics14091964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
Heart failure (HF) is a progressively deteriorating medical condition that significantly reduces both the patients' life expectancy and quality of life. Even though real progress was made in the past decades in the discovery of novel pharmacological treatments for HF, the prevention of premature deaths has only been marginally alleviated. Despite the availability of a plethora of pharmaceutical approaches, proper management of HF is still challenging. Thus, a myriad of experimental and clinical studies focusing on the discovery of new and provocative underlying mechanisms of HF physiopathology pave the way for the development of novel HF therapeutic approaches. Furthermore, recent technological advances made possible the development of various interventional techniques and device-based approaches for the treatment of HF. Since many of these modern approaches interfere with various well-known pathological mechanisms in HF, they have a real ability to complement and or increase the efficiency of existing medications and thus improve the prognosis and survival rate of HF patients. Their promising and encouraging results reported to date compel the extension of heart failure treatment beyond the classical view. The aim of this review was to summarize modern approaches, new perspectives, and future directions for the treatment of HF.
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Affiliation(s)
- Irene Paula Popa
- Cardiology Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700111 Iași, Romania
| | - Mihai Ștefan Cristian Haba
- Cardiology Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700111 Iași, Romania
- Department of Internal Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Minela Aida Mărănducă
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Daniela Maria Tănase
- Department of Internal Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
- Internal Medicine Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700115 Iași, Romania
| | - Dragomir N. Șerban
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Lăcrămioara Ionela Șerban
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Radu Iliescu
- Department of Pharmacology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Ionuț Tudorancea
- Cardiology Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700111 Iași, Romania
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
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24
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Zuin G, Araujo D, Ribeiro V, Seiler MG, Prieto WH, Pintão MC, dos Santos Lazari C, Granato CFH, Veloso A. Prediction of SARS-CoV-2-positivity from million-scale complete blood counts using machine learning. COMMUNICATIONS MEDICINE 2022; 2:72. [PMID: 35721829 PMCID: PMC9199341 DOI: 10.1038/s43856-022-00129-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 05/23/2022] [Indexed: 01/10/2023] Open
Abstract
Background The Complete Blood Count (CBC) is a commonly used low-cost test that measures white blood cells, red blood cells, and platelets in a person's blood. It is a useful tool to support medical decisions, as intrinsic variations of each analyte bring relevant insights regarding potential diseases. In this study, we aimed at developing machine learning models for COVID-19 diagnosis through CBCs, unlocking the predictive power of non-linear relationships between multiple blood analytes. Methods We collected 809,254 CBCs and 1,088,385 RT-PCR tests for SARS-Cov-2, of which 21% (234,466) were positive, from 900,220 unique individuals. To properly screen COVID-19, we also collected 120,807 CBCs of 16,940 individuals who tested positive for other respiratory viruses. We proposed an ensemble procedure that combines machine learning models for different respiratory infections and analyzed the results in both the first and second waves of COVID-19 cases in Brazil. Results We obtain a high-performance AUROC of 90 + % for validations in both scenarios. We show that models built solely of SARS-Cov-2 data are biased, performing poorly in the presence of infections due to other RNA respiratory viruses. Conclusions We demonstrate the potential of a novel machine learning approach for COVID-19 diagnosis based on a CBC and show that aggregating information about other respiratory diseases was essential to guarantee robustness in the results. Given its versatile nature, low cost, and speed, we believe that our tool can be particularly useful in a variety of scenarios-both during the pandemic and after.
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Affiliation(s)
- Gianlucca Zuin
- Universidade Federal de Minas Gerais, CS Dept., Belo Horizonte, Brazil
- Kunumi, Belo Horizonte, Brazil
| | - Daniella Araujo
- Universidade Federal de Minas Gerais, CS Dept., Belo Horizonte, Brazil
- Huna, São Paulo, Brazil
| | | | | | | | | | | | | | - Adriano Veloso
- Universidade Federal de Minas Gerais, CS Dept., Belo Horizonte, Brazil
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25
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Algarni M, Al-Rezqi A, Saeed F, Alsaeedi A, Ghabban F. Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images. PeerJ Comput Sci 2022; 8:e993. [PMID: 35721418 PMCID: PMC9202622 DOI: 10.7717/peerj-cs.993] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. METHODS In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. RESULTS The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. DISCUSSION The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.
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Affiliation(s)
- Mona Algarni
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
- Computer Science and Artificial Intelligence Department, University of Prince Mugrin, Medina, Saudi Arabia
| | - Abdulkader Al-Rezqi
- College of Medicine, King Saud bin Abdulaziz University, Jeddah, Saudi Arabia
| | - Faisal Saeed
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
- School of Computing and Digital Technology, University of Birmingham, Birmingham, United Kingdom
| | - Abdullah Alsaeedi
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
| | - Fahad Ghabban
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
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26
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Guglielmo M, Rovera C, Rabbat MG, Pontone G. The Role of Cardiac Magnetic Resonance in Aortic Stenosis and Regurgitation. J Cardiovasc Dev Dis 2022; 9:108. [PMID: 35448084 PMCID: PMC9030119 DOI: 10.3390/jcdd9040108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 02/06/2023] Open
Abstract
Cardiac magnetic resonance (CMR) imaging is a well-set diagnostic technique for assessment of valvular heart diseases and is gaining ground in current clinical practice. It provides high-quality images without the administration of ionizing radiation and occasionally without the need of contrast agents. It offers the unique possibility of a comprehensive stand-alone assessment of the heart including biventricular function, left ventricle remodeling, myocardial fibrosis, and associated valvulopathies. CMR is the recognized reference for the quantification of ventricular volumes, mass, and function. A particular strength is the ability to quantify flow, especially with new techniques which allow accurate measurement of stenosis and regurgitation. Furthermore, tissue mapping enables the visualization and quantification of structural changes in the myocardium. In this way, CMR has the potential to yield important prognostic information predicting those patients who will progress to surgery and impact outcomes. In this review, the fundamentals of CMR in assessment of aortic valve diseases (AVD) are described, together with its strengths and weaknesses. This state-of-the-art review provides an updated overview of CMR potentials in all AVD issues, including valve anatomy, flow quantification, ventricular volumes and function, and tissue characterization.
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Affiliation(s)
- Marco Guglielmo
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (M.G.); (C.R.)
| | - Chiara Rovera
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (M.G.); (C.R.)
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60611, USA;
- Edward Hines Jr. VA Hospital, Hines, IL 60141, USA
| | - Gianluca Pontone
- Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (M.G.); (C.R.)
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27
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Xu L, Zhou S, Wang L, Yao Y, Hao L, Qi L, Yao Y, Han H, Mukkamala R, Greenwald SE. Improving the accuracy and robustness of carotid-femoral pulse wave velocity measurement using a simplified tube-load model. Sci Rep 2022; 12:5147. [PMID: 35338246 PMCID: PMC8956634 DOI: 10.1038/s41598-022-09256-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 03/21/2022] [Indexed: 11/09/2022] Open
Abstract
Arterial stiffness, as measured by pulse wave velocity, for the early non-invasive screening of cardiovascular disease is becoming ever more widely used and is an independent prognostic indicator for a variety of pathologies including arteriosclerosis. Carotid-femoral pulse wave velocity (cfPWV) is regarded as the gold standard for aortic stiffness. Existing algorithms for cfPWV estimation have been shown to have good repeatability and accuracy, however, further assessment is needed, especially when signal quality is compromised. We propose a method for calculating cfPWV based on a simplified tube-load model, which allows for the propagation and reflection of the pulse wave. In-vivo cfPWV measurements from 57 subjects and numerical cfPWV data based on a one-dimensional model were used to assess the method and its performance was compared to three other existing approaches (waveform matching, intersecting tangent, and cross-correlation). The cfPWV calculated using the simplified tube-load model had better repeatability than the other methods (Intra-group Correlation Coefficient, ICC = 0.985). The model was also more accurate than other methods (deviation, 0.13 ms−1) and was more robust when dealing with noisy signals. We conclude that the determination of cfPWV based on the proposed model can accurately and robustly evaluate arterial stiffness.
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Affiliation(s)
- Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China. .,Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, China. .,Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China.
| | - Shuran Zhou
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Lu Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Yang Yao
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Liling Hao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Lin Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Hongguang Han
- General Hospital of Northern Theater Command, Shenyang, China.
| | - Ramakrishna Mukkamala
- Department of Bioengineering, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, USA
| | - Stephen E Greenwald
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
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28
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Jani VP, Kachenoura N, Redheuil A, Teixido-Tura G, Bouaou K, Bollache E, Mousseaux E, De Cesare A, Kutty S, Wu CO, Bluemke DA, Lima JAC, Ambale-Venkatesh B. Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA). J Digit Imaging 2022; 35:594-604. [PMID: 35233722 DOI: 10.1007/s10278-021-00529-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 09/21/2021] [Accepted: 10/15/2021] [Indexed: 10/19/2022] Open
Abstract
This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging for automatic aortic analysis on the large MESA cohort with assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. This study includes images and corresponding analysis of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study. Train, validation, and internal test sets consisted of 1123 studies (24,282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. The external test set of TAAs consisted of 37 studies (3224 images). CNN performance was evaluated utilizing a dice coefficient and concordance correlation coefficients (CCC) of geometric parameters. Dice coefficients were as high as 97.55% (CI: 97.47-97.62%) and 93.56% (CI: 84.63-96.68%) on the internal and external test of TAAs, respectively. CCC for maximum and minimum and ascending aortic area were 0.969 and 0.950, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. The absolute differences between manual and deep learning segmentations for ascending and descending aortic distensibility were 0.0194 × 10-4 ± 9.67 × 10-4 and 0.002 ± 0.001 mmHg-1, respectively, on the internal test set and 0.44 × 10-4 ± 20.4 × 10-4 and 0.002 ± 0.001 mmHg-1, respectively, on the external test set. We successfully developed a U-Net-based aortic segmentation and analysis algorithm in both MESA and in external cases of TAA.
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Affiliation(s)
- Vivek P Jani
- Department of Radiology, Johns Hopkins University, 600 North Wolfe St, Baltimore, MD, 21287, USA
| | - Nadjia Kachenoura
- Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France
| | - Alban Redheuil
- Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France
| | | | - Kevin Bouaou
- Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France
| | - Emilie Bollache
- Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France
| | - Elie Mousseaux
- Université de Paris, Hôpital Européen Georges Pompidou, APHP, INSERM PARCC, Paris, France
| | - Alain De Cesare
- Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France
| | - Shelby Kutty
- Department of Radiology, Johns Hopkins University, 600 North Wolfe St, Baltimore, MD, 21287, USA
| | - Colin O Wu
- Office of Biostatistics Research, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - David A Bluemke
- University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Joao A C Lima
- Department of Radiology, Johns Hopkins University, 600 North Wolfe St, Baltimore, MD, 21287, USA
| | - Bharath Ambale-Venkatesh
- Department of Radiology, Johns Hopkins University, 600 North Wolfe St, Baltimore, MD, 21287, USA.
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29
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Fotaki A, Puyol-Antón E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? Front Cardiovasc Med 2022; 8:818765. [PMID: 35083303 PMCID: PMC8785419 DOI: 10.3389/fcvm.2021.818765] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/15/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.
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Affiliation(s)
- Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Esther Puyol-Antón
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Amedeo Chiribiri
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - René Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Taylor AM. The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging. Pediatr Radiol 2022; 52:2131-2138. [PMID: 34936019 PMCID: PMC9537201 DOI: 10.1007/s00247-021-05218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/13/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022]
Abstract
Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.
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Affiliation(s)
- Andrew M. Taylor
- Great Ormond Street Hospital for Children, Zayed Centre for Research, 20 Guildford St., Room 3.7, London, WC1N 1DZ UK ,Cardiovascular Imaging, UCL Institute of Cardiovascular Science, London, UK
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31
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Bikia V, Fong T, Climie RE, Bruno RM, Hametner B, Mayer C, Terentes-Printzios D, Charlton PH. Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:676-690. [PMID: 35316972 PMCID: PMC7612526 DOI: 10.1093/ehjdh/ztab089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology (LHTC), Swiss Federal Institute of Technology, CH-1015 Lausanne, Vaud, Switzerland
| | - Terence Fong
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Grattan Street, Parkville, Victoria, 3010 Australia
| | - Rachel E Climie
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Rosa-Maria Bruno
- Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Bernhard Hametner
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Christopher Mayer
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Dimitrios Terentes-Printzios
- First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, 114 Vasilissis Sofias Avenue, 11527, Athens, Greece
| | - Peter H Charlton
- Department of Public Health and Primary Care, Strangeways Research Laboratory, 2 Worts' Causeway, Cambridge, CB1 8RN, UK,Research Centre for Biomedical Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK,Corresponding author.
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Romero P, Lozano M, Martínez-Gil F, Serra D, Sebastián R, Lamata P, García-Fernández I. Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta. Front Physiol 2021; 12:713118. [PMID: 34539438 PMCID: PMC8440937 DOI: 10.3389/fphys.2021.713118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.
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Affiliation(s)
- Pau Romero
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Miguel Lozano
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Francisco Martínez-Gil
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Dolors Serra
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Rafael Sebastián
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom
| | - Ignacio García-Fernández
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
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Abstract
PURPOSE OF REVIEW Discuss foundational concepts for artificial intelligence (AI) and review recent literature on its application to aortic disease. RECENT FINDINGS Machine learning (ML) techniques are rapidly evolving for the evaluation of aortic disease - broadly categorized as algorithms for aortic segmentation, detection of pathology, and risk stratification. Advances in deep learning, particularly U-Net architectures, have revolutionized segmentation of the aorta and show potential for monitoring the size of aortic aneurysm and characterizing aortic dissection. These algorithms also facilitate application of more complex technologies including analysis of flow dynamics with 4D Flow magnetic resonance imaging (MRI) and computational simulation of fluid dynamics for aortic coarctation. In addition, AI algorithms have been proposed to assist in 'opportunistic' screening from routine imaging exams, including automated aortic calcification score, which has emerged as a strong predictor of cardiovascular risk. Finally, several ML algorithms are being explored for risk stratification of patients with aortic aneurysm and dissection, in addition to prediction of postprocedural complications. SUMMARY Multiple ML techniques have potential for characterization and risk prediction of aortic aneurysm, dissection, coarctation, and atherosclerotic disease on computed tomography and MRI. This nascent field shows considerable promise with many applications in development and in early preclinical evaluation.
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Danilov VV, Klyshnikov KY, Gerget OM, Skirnevsky IP, Kutikhin AG, Shilov AA, Ganyukov VI, Ovcharenko EA. Aortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning. Front Cardiovasc Med 2021; 8:697737. [PMID: 34350220 PMCID: PMC8326378 DOI: 10.3389/fcvm.2021.697737] [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: 04/20/2021] [Accepted: 06/10/2021] [Indexed: 11/15/2022] Open
Abstract
Currently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI.
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Affiliation(s)
- Viacheslav V. Danilov
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Kirill Yu. Klyshnikov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Olga M. Gerget
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Igor P. Skirnevsky
- Research Laboratory for Processing and Analysis of Big Data, Tomsk Polytechnic University, Tomsk, Russia
| | - Anton G. Kutikhin
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Aleksandr A. Shilov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Vladimir I. Ganyukov
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Evgeny A. Ovcharenko
- Department of Experimental Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
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35
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Fadil H, Totman JJ, Hausenloy DJ, Ho HH, Joseph P, Low AFH, Richards AM, Chan MY, Marchesseau S. A deep learning pipeline for automatic analysis of multi-scan cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2021; 23:47. [PMID: 33896419 PMCID: PMC8074440 DOI: 10.1186/s12968-020-00695-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 12/09/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) sequences are commonly used to obtain a complete description of the function and structure of the heart, provided that accurate measurements are extracted from images. New methods of extraction of information are being developed, among them, deep neural networks are powerful tools that showed the ability to perform fast and accurate segmentation. Iq1n order to reduce the time spent by reading physicians to process data and minimize intra- and inter-observer variability, we propose a fully automatic multi-scan CMR image analysis pipeline. METHODS Sequence specific U-Net 2D models were trained to perform the segmentation of the left ventricle (LV), right ventricle (RV) and aorta in cine short-axis, late gadolinium enhancement (LGE), native T1 map, post-contrast T1, native T2 map and aortic flow sequences depending on the need. The models were trained and tested on a set of data manually segmented by experts using semi-automatic and manual tools. A set of parameters were computed from the resulting segmentations such as the left ventricular and right ventricular ejection fraction (EF), LGE scar percentage, the mean T1, T1 post, T2 values within the myocardium, and aortic flow. The Dice similarity coefficient, Hausdorff distance, mean surface distance, and Pearson correlation coefficient R were used to assess and compare the results of the U-Net based pipeline with intra-observer variability. Additionally, the pipeline was validated on two clinical studies. RESULTS The sequence specific U-Net 2D models trained achieved fast (≤ 0.2 s/image on GPU) and precise segmentation over all the targeted region of interest with high Dice scores (= 0.91 for LV, = 0.92 for RV, = 0.93 for Aorta in average) comparable to intra-observer Dice scores (= 0.86 for LV, = 0.87 for RV, = 0.95 for aorta flow in average). The automatically and manually computed parameters were highly correlated (R = 0.91 in average) showing results superior to the intra-observer variability (R = 0.85 in average) for every sequence presented here. CONCLUSION The proposed pipeline allows for fast and robust analysis of large CMR studies while guaranteeing reproducibility, hence potentially improving patient's diagnosis as well as clinical studies outcome.
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Affiliation(s)
- Hakim Fadil
- Centre for Translational MR Research (TMR), National University of Singapore, Singapore, 117549, Singapore.
| | - John J Totman
- Centre for Translational MR Research (TMR), National University of Singapore, Singapore, 117549, Singapore
| | - Derek J Hausenloy
- Cardiovascular & Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, 169857, Singapore
- National Heart Research Institute Singapore, National Heart Centre, Singapore, Singapore
- Department of Medicine, Yong Loo Lin SoM, National University of Singapore, Singapore, 117597, Singapore
- The Hatter Cardiovascular Institute, University College London, London, UK
- Cardiovascular Research Center, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
| | - Hee-Hwa Ho
- Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | | | | | - A Mark Richards
- Cardiovascular Research Institute, National University of Singapore, Singapore, 119228, Singapore
- Christchurch Heart Institute, University of Otago, 8140, Christchurch, New Zealand
| | - Mark Y Chan
- Department of Medicine, Yong Loo Lin SoM, National University of Singapore, Singapore, 117597, Singapore
| | - Stephanie Marchesseau
- Centre for Translational MR Research (TMR), National University of Singapore, Singapore, 117549, Singapore
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Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging: Can It Help Clinicians in Making a Diagnosis? J Thorac Imaging 2021; 36:142-148. [PMID: 33769416 DOI: 10.1097/rti.0000000000000584] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In the era of modern medicine, artificial intelligence (AI) is a growing field of interest which is experiencing a steady development. Several applications of AI have been applied to various aspects of cardiac magnetic resonance to assist clinicians and engineers in reducing the costs of exams and, at the same time, to improve image acquisition and reconstruction, thus simplifying their analysis, interpretation, and decision-making process as well. In fact, the role of AI and machine learning in cardiovascular imaging relies on evaluating images more quickly, improving their quality, nulling intraobserver and interobserver variability in their interpretation, upgrading the understanding of the stage of the disease, and providing with a personalized approach to cardiovascular care. In addition, AI algorithm could be directed toward workflow management. This article presents an overview of the existing AI literature in cardiac magnetic resonance, with its strengths and limitations, recent applications, and promising developments. We conclude that AI is very likely be used in all the various process of diagnosis routine mode for cardiac care of patients.
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37
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Ghadimi S, Auger DA, Feng X, Sun C, Meyer CH, Bilchick KC, Cao JJ, Scott AD, Oshinski JN, Ennis DB, Epstein FH. Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping. J Cardiovasc Magn Reson 2021; 23:20. [PMID: 33691739 PMCID: PMC7949250 DOI: 10.1186/s12968-021-00712-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 01/26/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis. METHODS Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain. RESULTS LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p < 0.05), respectively. Bland-Altman analyses showed biases of 0.00 ± 0.03 and limits of agreement of - 0.04 to 0.05 or better for deep learning-based fully-automatic global and segmental end-systolic circumferential strain vs. conventional user-assisted methods. CONCLUSIONS Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.
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Affiliation(s)
- Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA 22908 USA
| | - Daniel A. Auger
- Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA 22908 USA
| | - Xue Feng
- Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA 22908 USA
| | - Changyu Sun
- Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA 22908 USA
| | - Craig H. Meyer
- Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA 22908 USA
| | - Kenneth C. Bilchick
- Department of Medicine, University of Virginia Health System, Charlottesville, VA USA
| | - Jie Jane Cao
- Department of Cardiology, St. Francis Hospital, New York, NY USA
| | - Andrew D. Scott
- Cardiovascular Magnetic Resonance Unit, The Royal Brompton Hospital, London, United Kingdom
| | - John N. Oshinski
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA USA
| | - Daniel B. Ennis
- Department of Radiology, Stanford University, Stanford, CA USA
| | - Frederick H. Epstein
- Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA 22908 USA
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Ribeiro JM, Astudillo P, de Backer O, Budde R, Nuis RJ, Goudzwaard J, Van Mieghem NM, Lumens J, Mortier P, Mattace-Raso F, Boersma E, Cummins P, Bruining N, de Jaegere PP. Artificial Intelligence and Transcatheter Interventions for Structural Heart Disease: A glance at the (near) future. Trends Cardiovasc Med 2021; 32:153-159. [PMID: 33581255 DOI: 10.1016/j.tcm.2021.02.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 01/16/2023]
Abstract
With innovations in therapeutic technologies and changes in population demographics, transcatheter interventions for structural heart disease have become the preferred treatment and will keep growing. Yet, a thorough clinical selection and efficient pathway from diagnosis to treatment and follow-up are mandatory. In this review we reflect on how artificial intelligence may help to improve patient selection, pre-procedural planning, procedure execution and follow-up so to establish efficient and high quality health care in an increasing number of patients.
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Affiliation(s)
- Joana Maria Ribeiro
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Cardiology, Centro Hospitalar de Entre o Douro e Vouga, Santa Maria da Feira, Portugal; Department of Cardiology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | | | - Ole de Backer
- Department of Cardiology, Rigshospitalet University Hospital, Copenhagen, Denmark
| | - Ricardo Budde
- Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Rutger Jan Nuis
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jeanette Goudzwaard
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Nicolas M Van Mieghem
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Joost Lumens
- CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Eric Boersma
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Paul Cummins
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Nico Bruining
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Peter Pt de Jaegere
- Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, the Netherlands.
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La integración de la inteligencia artificial en el abordaje clínico del paciente: enfoque en la imagen cardiaca. Rev Esp Cardiol 2021. [DOI: 10.1016/j.recesp.2020.07.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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40
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Kawel-Boehm N, Hetzel SJ, Ambale-Venkatesh B, Captur G, Francois CJ, Jerosch-Herold M, Salerno M, Teague SD, Valsangiacomo-Buechel E, van der Geest RJ, Bluemke DA. Reference ranges ("normal values") for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update. J Cardiovasc Magn Reson 2020; 22:87. [PMID: 33308262 PMCID: PMC7734766 DOI: 10.1186/s12968-020-00683-3] [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: 01/09/2020] [Accepted: 10/26/2020] [Indexed: 01/06/2023] Open
Abstract
Cardiovascular magnetic resonance (CMR) enables assessment and quantification of morphological and functional parameters of the heart, including chamber size and function, diameters of the aorta and pulmonary arteries, flow and myocardial relaxation times. Knowledge of reference ranges ("normal values") for quantitative CMR is crucial to interpretation of results and to distinguish normal from disease. Compared to the previous version of this review published in 2015, we present updated and expanded reference values for morphological and functional CMR parameters of the cardiovascular system based on the peer-reviewed literature and current CMR techniques. Further, databases and references for deep learning methods are included.
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Affiliation(s)
- Nadine Kawel-Boehm
- Department of Radiology, Kantonsspital Graubuenden, Loestrasse 170, 7000, Chur, Switzerland
- Institute for Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, InselspitalBern, Switzerland
| | - Scott J Hetzel
- Department of Biostatistics and Medical Informatics, University of Wisconsin, 610 Walnut St, Madison, WI, 53726, USA
| | - Bharath Ambale-Venkatesh
- Department of Radiology, Johns Hopkins University, 600 N Wolfe Street, Baltimore, MD, 21287, USA
| | - Gabriella Captur
- MRC Unit of Lifelong Health and Ageing At UCL, 5-19 Torrington Place, Fitzrovia, London, WC1E 7HB, UK
- Inherited Heart Muscle Conditions Clinic, Royal Free Hospital NHS Foundation Trust, Hampstead, London, NW3 2QG, UK
| | - Christopher J Francois
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53792, USA
| | - Michael Jerosch-Herold
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Michael Salerno
- Cardiovascular Division, University of Virginia Health System, 1215 Lee Street, Charlottesville, VA, 22908, USA
| | - Shawn D Teague
- Department of Radiology, National Jewish Health, 1400 Jackson St, Denver, CO, 80206, USA
| | - Emanuela Valsangiacomo-Buechel
- Division of Paediatric Cardiology, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032, Zurich, Switzerland
| | - Rob J van der Geest
- Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands
| | - David A Bluemke
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53792, USA.
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Xue H, Tseng E, Knott KD, Kotecha T, Brown L, Plein S, Fontana M, Moon JC, Kellman P. Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients. Magn Reson Med 2020; 84:2788-2800. [PMID: 32378776 PMCID: PMC9373024 DOI: 10.1002/mrm.28291] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 03/30/2020] [Accepted: 03/30/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE Quantification of myocardial perfusion has the potential to improve the detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF) extraction. Failure to accurately identify the left ventricle (LV) prevents AIF estimation required for quantification, therefore high detection accuracy is required. This study presents a robust LV detection method using the convolutional neural network (CNN). METHODS CNN models were trained by assembling 25,027 scans (N = 12,984 patients) from three hospitals, seven scanners. Performance was evaluated using a hold-out test set of 5721 scans (N = 2805 patients). Model inputs were a time series of AIF images (2D+T). Two variations were investigated: (1) two classes (2CS) for background and foreground (LV mask), and (2) three classes (3CS) for background, LV, and RV. The final model was deployed on MRI scanners using the Gadgetron reconstruction software framework. RESULTS Model loading on the MRI scanner took ~340 ms and applying the model took ~180 ms. The 3CS model successfully detected the LV in 99.98% of all test cases (1 failure out of 5721). The mean Dice ratio for 3CS was 0.87 ± 0.08 with 92.0% of all cases having Dice >0.75. The 2CS model gave a lower Dice ratio of 0.82 ± 0.22 (P < 1e-5). There was no significant difference in foot-time, peak-time, first-pass duration, peak value, and area-under-curve (P > .2) comparing automatically extracted AIF signals with signals from manually drawn contours. CONCLUSIONS A CNN-based solution to detect the LV blood pool from the arterial input function image series was developed, validated, and deployed. A high LV detection accuracy of 99.98% was achieved.
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Affiliation(s)
- Hui Xue
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ethan Tseng
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Tushar Kotecha
- National Amyloidosis Centre, Royal Free Hospital, London, UK
| | - Louise Brown
- Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Sven Plein
- Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | | | | | - Peter Kellman
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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42
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Clinical Implications of Machine Learning, Artificial Intelligence, and Radiomics in Cardiac Imaging. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-00838-6] [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: 10/23/2022]
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43
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Kim J, Yum B, Palumbo MC, Sultana R, Wright N, Das M, You C, Moskowitz CS, Levine RA, Devereux RB, Weinsaft JW. Left Atrial Strain Impairment Precedes Geometric Remodeling as a Marker of Post-Myocardial Infarction Diastolic Dysfunction. JACC Cardiovasc Imaging 2020; 13:2099-2113. [PMID: 32828776 PMCID: PMC7554167 DOI: 10.1016/j.jcmg.2020.05.041] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 05/12/2020] [Accepted: 05/22/2020] [Indexed: 01/20/2023]
Abstract
OBJECTIVES The aims of this study were to test the magnitude of agreement between echocardiography (echo)- and cardiac magnetic resonance (CMR)-derived left atrial (LA) strain and to study their relative diagnostic performance in discriminating diastolic dysfunction (DD) and predicting atrial fibrillation (AF). BACKGROUNDS Peak atrial longitudinal strain (PALS) is a novel performance index. Utility of echo-quantified LA strain has yet to be prospectively tested in relation to current DD guidelines or compared to CMR. METHODS The study population comprised 257 post-myocardial infarction (MI) patients undergoing echo and CMR, including prospective derivation (n = 157) and clinical validation (n = 100) cohorts. DD was graded on echo using established consensus guidelines blinded to strain results. RESULTS PALS on both echo and CMR was nearly 2-fold lower among patients with versus no DD (p < 0.001) and was significantly different in those with mild versus no DD (p < 0.01). In contrast, LA geometric parameters including echo- and CMR-derived volumes were significantly different between advanced versus no DD groups (p < 0.001) but not between groups with mild versus no DD (all p > 0.05). Echo and CMR PALS yielded small differences irrespective of orientation and similar diagnostic performance for DD in the derivation (area under the curve [AUC]: 0.70 to 0.78) and validation (AUC: 0.75 to 0.78) cohorts. Impaired PALS on both modalities was independently associated with MI size (p < 0.001). During 4.4 ± 3.8 years of follow-up in the derivation cohort, 8% developed AF. Both 2-chamber echo- and CMR-derived PALS stratified arrhythmic risk (p = 0.004 and p = 0.02, respectively), including a 4-fold difference among patients in the lowest versus remainder of quartiles of echo-derived PALS (24% vs. 6%). Similarly, echo and CMR PALS were lower (both p < 0.05) among patients with subsequent heart failure hospitalizations. CONCLUSIONS Echo-derived PALS parallels results of CMR, yields incremental diagnostic utility versus LA geometry for stratifying presence and severity of DD, and improves prediction of AF and congestive heart failure after MI.
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Affiliation(s)
- Jiwon Kim
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York.
| | - Brian Yum
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Maria C Palumbo
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Razia Sultana
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Nathaniel Wright
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Mukund Das
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Cindy You
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Robert A Levine
- Department of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Richard B Devereux
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Jonathan W Weinsaft
- Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York, New York; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
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44
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Loncaric F, Camara O, Piella G, Bijnens B. Integration of artificial intelligence into clinical patient management: focus on cardiac imaging. ACTA ACUST UNITED AC 2020; 74:72-80. [PMID: 32819849 DOI: 10.1016/j.rec.2020.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 07/01/2020] [Indexed: 10/23/2022]
Abstract
Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve-through automation, standardization and data integration-all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management-with a focus on cardiac imaging, but applicable to all information handling-and to discuss current barriers that remain to be overcome before its widespread implementation and integration.
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Affiliation(s)
- Filip Loncaric
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - Oscar Camara
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; ICREA, Barcelona, Spain
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45
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Abstract
4D Flow is an emerging MR technique enabling three-dimensional and cardiac phase-resolved flowmetry with ECG-gated phase-contrast MRI that increased the speed of data acquisitions, accuracy and robustness. The method is promoting researches in areas that have not been fully addressed before in the cardiovascular system, such as flowmetry of the bloodstream across the valves, within the heart chambers, complexed flow dynamics such as vortex, helical or retrograde. Wall shear stress and other potential biomarkers derived from 4D Flow are known to be related to vascular wall diseases such as atherosclerosis. In this review, fundamental concepts of 4D Flow technique and post-processing, benefits and limitations as well as its clinical applications are discussed, and the importance of quality control and validation of the method is emphasized. New ideas inspired by 4D Flow can help clinicians and MR scientists further understand the role of flow dynamics in health sciences, diseases and various aspects of cardiovascular physiology.
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Affiliation(s)
- Yasuo Takehara
- Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
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46
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Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review. Prog Cardiovasc Dis 2020; 63:367-376. [DOI: 10.1016/j.pcad.2020.03.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 03/08/2020] [Indexed: 02/06/2023]
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47
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Berhane H, Scott M, Elbaz M, Jarvis K, McCarthy P, Carr J, Malaisrie C, Avery R, Barker AJ, Robinson JD, Rigsby CK, Markl M. Fully automated 3D aortic segmentation of 4D flow MRI for hemodynamic analysis using deep learning. Magn Reson Med 2020; 84:2204-2218. [PMID: 32167203 DOI: 10.1002/mrm.28257] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/18/2020] [Accepted: 02/24/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE To generate fully automated and fast 4D-flow MRI-based 3D segmentations of the aorta using deep learning for reproducible quantification of aortic flow, peak velocity, and dimensions. METHODS A total of 1018 subjects with aortic 4D-flow MRI (528 with bicuspid aortic valve, 376 with tricuspid aortic valve and aortic dilation, 114 healthy controls) comprised the data set. A convolutional neural network was trained to generate 3D aortic segmentations from 4D-flow data. Manual segmentations served as the ground truth (N = 499 training, N = 101 validation, N = 418 testing). Dice scores, Hausdorff distance, and average symmetrical surface distance were calculated to assess performance. Aortic flow, peak velocity, and lumen dimensions were quantified at the ascending, arch, and descending aorta and compared using Bland-Altman analysis. Interobserver variability of manual analysis was assessed on a subset of 40. RESULTS Convolutional neural network segmentation required 0.438 ± 0.355 seconds versus 630 ± 254 seconds for manual analysis and demonstrated excellent performance with a median Dice score of 0.951 (0.930-0.966), Hausdorff distance of 2.80 (2.13-4.35), and average symmetrical surface distance of 0.176 (0.119-0.290). Excellent agreement was found for flow, peak velocity, and dimensions with low bias and limits of agreement less than 10% difference versus manual analysis. For aortic volume, limits of agreement were moderate within 16.3%. Interobserver variability (median Dice score: 0.950; Hausdorff distance: 2.45; and average symmetrical surface distance: 0.145) and convolutional neural network-based analysis (median Dice score: 0.953-0.959; Hausdorff distance: 2.24-2.91; and average symmetrical surface distance: 0.145-1.98 to observers) demonstrated similar reproducibility. CONCLUSIONS Deep learning enabled fast and automated 3D aortic segmentation from 4D-flow MRI, demonstrating its potential for efficient clinical workflows. Future studies should investigate its utility for other vasculature and multivendor applications.
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Affiliation(s)
- Haben Berhane
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Michael Scott
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois.,Department of Radiology, Northwestern University, Chicago, Illinois
| | - Mohammed Elbaz
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois.,Department of Radiology, Northwestern University, Chicago, Illinois
| | - Kelly Jarvis
- Department of Radiology, Northwestern University, Chicago, Illinois
| | - Patrick McCarthy
- Divison of Cardiac Surgery, Northwestern University, Chicago, Illinois
| | - James Carr
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois
| | - Chris Malaisrie
- Department of Radiology, Northwestern University, Chicago, Illinois
| | - Ryan Avery
- Department of Radiology, Northwestern University, Chicago, Illinois
| | - Alex J Barker
- Anschutz Medical Campus, University of Colorado, Aurora, Colorado
| | - Joshua D Robinson
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Cynthia K Rigsby
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Michael Markl
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois.,Department of Radiology, Northwestern University, Chicago, Illinois
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48
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Callaghan FM, Burkhardt B, Geiger J, Valsangiacomo Buechel ER, Kellenberger CJ. Flow quantification dependency on background phase correction techniques in 4D‐flow MRI. Magn Reson Med 2019; 83:2264-2275. [DOI: 10.1002/mrm.28085] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/09/2019] [Accepted: 10/24/2019] [Indexed: 12/23/2022]
Affiliation(s)
- Fraser M. Callaghan
- Center for MR Research University Children's Hospital Zurich Switzerland
- Children's Research Center University Children's Hospital Zurich Switzerland
| | - Barbara Burkhardt
- Children's Research Center University Children's Hospital Zurich Switzerland
- Division of Pediatric Cardiology University Children's Hospital Zurich Switzerland
| | - Julia Geiger
- Children's Research Center University Children's Hospital Zurich Switzerland
- Department of Diagnostic Imaging University Children's Hospital Zurich Switzerland
| | - Emanuela R. Valsangiacomo Buechel
- Children's Research Center University Children's Hospital Zurich Switzerland
- Division of Pediatric Cardiology University Children's Hospital Zurich Switzerland
| | - Christian J. Kellenberger
- Children's Research Center University Children's Hospital Zurich Switzerland
- Department of Diagnostic Imaging University Children's Hospital Zurich Switzerland
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49
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Petersen SE, Abdulkareem M, Leiner T. Artificial Intelligence Will Transform Cardiac Imaging-Opportunities and Challenges. Front Cardiovasc Med 2019; 6:133. [PMID: 31552275 PMCID: PMC6746883 DOI: 10.3389/fcvm.2019.00133] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 08/27/2019] [Indexed: 01/31/2023] Open
Abstract
Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.
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Affiliation(s)
- Steffen E Petersen
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Musa Abdulkareem
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.,NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Tim Leiner
- Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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50
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Hwang M, Leem CH, Shim EB. Toward a grey box approach for cardiovascular physiome. THE KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY : OFFICIAL JOURNAL OF THE KOREAN PHYSIOLOGICAL SOCIETY AND THE KOREAN SOCIETY OF PHARMACOLOGY 2019; 23:305-310. [PMID: 31496867 PMCID: PMC6717786 DOI: 10.4196/kjpp.2019.23.5.305] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 01/17/2023]
Abstract
The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.
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Affiliation(s)
| | - Chae Hun Leem
- Department of Physiology, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Eun Bo Shim
- SiliconSapiens Inc., Seoul 06097, Korea
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, Korea
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