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Hrabak Paar M, Muršić M, Bremerich J, Heye T. Cardiovascular Aging and Risk Assessment: How Multimodality Imaging Can Help. Diagnostics (Basel) 2024; 14:1947. [PMID: 39272731 PMCID: PMC11393882 DOI: 10.3390/diagnostics14171947] [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: 07/17/2024] [Revised: 08/28/2024] [Accepted: 08/29/2024] [Indexed: 09/15/2024] Open
Abstract
Aging affects the cardiovascular system, and this process may be accelerated in individuals with cardiovascular risk factors. The main vascular changes include arterial wall thickening, calcification, and stiffening, together with aortic dilatation and elongation. With aging, we can observe left ventricular hypertrophy with myocardial fibrosis and left atrial dilatation. These changes may lead to heart failure and atrial fibrillation. Using multimodality imaging, including ultrasound, computed tomography (CT), and magnetic resonance imaging, it is possible to detect these changes. Additionally, multimodality imaging, mainly via CT measurements of coronary artery calcium or ultrasound carotid intima-media thickness, enables advanced cardiovascular risk stratification and helps in decision-making about preventive strategies. The focus of this manuscript is to briefly review cardiovascular changes that occur with aging, as well as to describe how multimodality imaging may be used for the assessment of these changes and risk stratification of asymptomatic individuals.
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Affiliation(s)
- Maja Hrabak Paar
- Department of Diagnostic and Interventional Radiology, University Hospital Center Zagreb, Kispaticeva 12, HR-10000 Zagreb, Croatia
| | - Miroslav Muršić
- Department of Diagnostic and Interventional Radiology, University Hospital Center Zagreb, Kispaticeva 12, HR-10000 Zagreb, Croatia
| | - Jens Bremerich
- Clinic of Radiology and Nuclear Medicine, University of Basel Hospital, Petersgraben 4, CH-4031 Basel, Switzerland
| | - Tobias Heye
- Clinic of Radiology and Nuclear Medicine, University of Basel Hospital, Petersgraben 4, CH-4031 Basel, Switzerland
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Salih AM, Galazzo IB, Gkontra P, Rauseo E, Lee AM, Lekadir K, Radeva P, Petersen SE, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artif Intell Rev 2024; 57:240. [PMID: 39132011 PMCID: PMC11315784 DOI: 10.1007/s10462-024-10852-w] [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] [Accepted: 07/03/2024] [Indexed: 08/13/2024]
Abstract
Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-024-10852-w.
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Affiliation(s)
- Ahmed M. Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Department of Population Health Sciences, University of Leicester, University Rd, Leicester, LE1 7RH UK
- Department of Computer Science, University of Zakho, Duhok road, Zakho, Kurdistan Iraq
| | - Ilaria Boscolo Galazzo
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
| | - Polyxeni Gkontra
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Elisa Rauseo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Aaron Mark Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
| | - Petia Radeva
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- Health Data Research, London, UK
- Alan Turing Institute, London, UK
| | - Gloria Menegaz
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
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Elias P, Jain SS, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2472-2486. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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Raisi-Estabragh Z, Szabo L, Schuermans A, Salih AM, Chin CWL, Vágó H, Altmann A, Ng FS, Garg P, Pavanello S, Marwick TH, Petersen SE. Noninvasive Techniques for Tracking Biological Aging of the Cardiovascular System: JACC Family Series. JACC Cardiovasc Imaging 2024:S1936-878X(24)00082-2. [PMID: 38597854 DOI: 10.1016/j.jcmg.2024.03.001] [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: 10/04/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 04/11/2024]
Abstract
Population aging is one of the most important demographic transformations of our time. Increasing the "health span"-the proportion of life spent in good health-is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom; Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Art Schuermans
- Faculty of Medicine, KU Leuven, Leuven, Belgium; Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ahmed M Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Department of Population Health Sciences, University of Leicester, Leicester UK; Department of Computer Science, Faculty of Science, University of Zakho, Zakho, Kurdistan Region, Iraq
| | - Calvin W L Chin
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore; Cardiovascular Academic Clinical Programme, Duke National University of Singapore Medical School, Singapore, Singapore
| | - Hajnalka Vágó
- Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Pankaj Garg
- University of East Anglia, Norwich Medical School, Norwich, United Kingdom; Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
| | - Sofia Pavanello
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy; Padua Hospital, Occupational Medicine Unit, Padua, Italy; University Center for Space Studies and Activities "Giuseppe Colombo" - CISAS, University of Padua, Padua, Italy
| | | | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom; Health Data Research UK, London, United Kingdom
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Regnault V, Lacolley P, Laurent S. Arterial Stiffness: From Basic Primers to Integrative Physiology. Annu Rev Physiol 2024; 86:99-121. [PMID: 38345905 DOI: 10.1146/annurev-physiol-042022-031925] [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: 02/15/2024]
Abstract
The elastic properties of conductance arteries are one of the most important hemodynamic functions in the body, and data continue to emerge regarding the importance of their dysfunction in vascular aging and a range of cardiovascular diseases. Here, we provide new insight into the integrative physiology of arterial stiffening and its clinical consequence. We also comprehensively review progress made on pathways/molecules that appear today as important basic determinants of arterial stiffness, particularly those mediating the vascular smooth muscle cell (VSMC) contractility, plasticity and stiffness. We focus on membrane and nuclear mechanotransduction, clearance function of the vascular wall, phenotypic switching of VSMCs, immunoinflammatory stimuli and epigenetic mechanisms. Finally, we discuss the most important advances of the latest clinical studies that revisit the classical therapeutic concepts of arterial stiffness and lead to a patient-by-patient strategy according to cardiovascular risk exposure and underlying disease.
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Arai H, Kawakubo M, Kadokami T. Editorial for "Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers". J Magn Reson Imaging 2023; 58:1813-1814. [PMID: 36946777 DOI: 10.1002/jmri.28687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023] Open
Affiliation(s)
- Hideo Arai
- Fukuokaken Saiseikai Futsukaichi Hospital, Fukuoka, Japan
| | - Masateru Kawakubo
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
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Chambers KH. Radiomic Biomarkers as a Non-Invasive Tool for Assessing Biological Age. Can Geriatr J 2023; 26:410-411. [PMID: 37662058 PMCID: PMC10444525 DOI: 10.5770/cgj.26.685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023] Open
Affiliation(s)
- Kevoyne H Chambers
- School of Medicine, Jiangsu University, Zhenjiang City, Jiangsu Province, China
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