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Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024; 13:267-279. [PMID: 38703292 PMCID: PMC11093957 DOI: 10.1007/s40119-024-00368-3] [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: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
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Medhi D, Kamidi SR, Mamatha Sree KP, Shaikh S, Rasheed S, Thengu Murichathil AH, Nazir Z. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review. Cureus 2024; 16:e59661. [PMID: 38836155 PMCID: PMC11148729 DOI: 10.7759/cureus.59661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
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Affiliation(s)
- Diptiman Medhi
- Internal Medicine, Gauhati Medical College and Hospital, Guwahati, Guwahati, IND
| | | | | | - Shifa Shaikh
- Cardiology, SMBT Institute of Medical Sciences and Research Centre, Igatpuri, IND
| | - Shanida Rasheed
- Emergency Medicine, East Sussex Healthcare NHS Trust, Eastbourne, GBR
| | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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3
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Khachatoorian Y, Fuisz A, Frishman WH, Aronow WS, Ranjan P. The Significance of Parametric Mapping in Advanced Cardiac Imaging. Cardiol Rev 2024:00045415-990000000-00243. [PMID: 38595125 DOI: 10.1097/crd.0000000000000695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Cardiac magnetic resonance imaging has witnessed a transformative shift with the integration of parametric mapping techniques, such as T1 and T2 mapping and extracellular volume fraction. These techniques play a crucial role in advancing our understanding of cardiac function and structure, providing unique insights into myocardial tissue properties. Native T1 mapping is particularly valuable, correlating with histopathological fibrosis and serving as a marker for various cardiac pathologies. Extracellular volume fraction, an early indicator of myocardial remodeling, predicts adverse outcomes in heart failure. Elevated T2 relaxation time in cardiac MRI indicates myocardial edema, enabling noninvasive and early detection in conditions like myocarditis. These techniques offer precise insights into myocardial properties, enhancing the accuracy of diagnosis and prognosis across a spectrum of cardiac conditions, including myocardial infarction, autoimmune diseases, myocarditis, and sarcoidosis. Emphasizing the significance of these techniques in myocardial tissue analysis, the review provides a comprehensive overview of their applications and contributions to our understanding of cardiac diseases.
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Affiliation(s)
- Yeraz Khachatoorian
- From the Departments of Cardiology and Medicine, Westchester Medical Center and New York Medical College, Valhalla, NY
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Palermi S, Vecchiato M, Saglietto A, Niederseer D, Oxborough D, Ortega-Martorell S, Olier I, Castelletti S, Baggish A, Maffessanti F, Biffi A, D'Andrea A, Zorzi A, Cavarretta E, D'Ascenzi F. Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart? Eur J Prev Cardiol 2024; 31:470-482. [PMID: 38198776 DOI: 10.1093/eurjpc/zwae008] [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] [Received: 06/29/2023] [Revised: 01/01/2024] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.
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Affiliation(s)
- Stefano Palermi
- Public Health Department, University of Naples Federico II, via Pansini 5, 80131 Naples, Italy
| | - Marco Vecchiato
- Sports and Exercise Medicine Division, Department of Medicine, University of Padova, 35128 Padova, Italy
| | - Andrea Saglietto
- Division of Cardiology, Cardiovascular and Thoracic Department, 'Citta della Salute e della Scienza' Hospital, 10129 Turin, Italy
- Department of Medical Sciences, University of Turin, 10129 Turin, Italy
| | - David Niederseer
- Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - David Oxborough
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Ivan Olier
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Silvia Castelletti
- Cardiology Department, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Aaron Baggish
- Cardiovascular Performance Program, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Alessandro Biffi
- Med-Ex, Medicine & Exercise, Medical Partner Scuderia Ferrari, 00187 Rome, Italy
| | - Antonello D'Andrea
- Department of Cardiology, Umberto I Hospital, 84014 Nocera Inferiore, Italy
| | - Alessandro Zorzi
- Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
| | - Elena Cavarretta
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Naples, Italy
| | - Flavio D'Ascenzi
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
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Hernández-Lemus E, Miramontes P, Martínez-García M. Topological Data Analysis in Cardiovascular Signals: An Overview. ENTROPY (BASEL, SWITZERLAND) 2024; 26:67. [PMID: 38248193 PMCID: PMC10814033 DOI: 10.3390/e26010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024]
Abstract
Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.
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Affiliation(s)
- Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico;
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Pedro Miramontes
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico;
- Department of Mathematics, Sciences School, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Geng R, Buelo CJ, Sundaresan M, Starekova J, Panagiotopoulos N, Oechtering TH, Lawrence EM, Ignaciuk M, Reeder SB, Hernando D. Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation. J Magn Reson Imaging 2023; 58:429-441. [PMID: 36583550 PMCID: PMC10310884 DOI: 10.1002/jmri.28564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND There is an unmet need for fully automated image prescription of the liver to enable efficient, reproducible MRI. PURPOSE To develop and evaluate artificial intelligence (AI)-based liver image prescription. STUDY TYPE Prospective. POPULATION A total of 570 female/469 male patients (age: 56 ± 17 years) with 72%/8%/20% assigned randomly for training/validation/testing; two female/four male healthy volunteers (age: 31 ± 6 years). FIELD STRENGTH/SEQUENCE 1.5 T, 3.0 T; spin echo, gradient echo, bSSFP. ASSESSMENT A total of 1039 three-plane localizer acquisitions (26,929 slices) from consecutive clinical liver MRI examinations were retrieved retrospectively and annotated by six radiologists. The localizer images and manual annotations were used to train an object-detection convolutional neural network (YOLOv3) to detect multiple object classes (liver, torso, and arms) across localizer image orientations and to output corresponding 2D bounding boxes. Whole-liver image prescription in standard orientations was obtained based on these bounding boxes. 2D detection performance was evaluated on test datasets by calculating intersection over union (IoU) between manual and automated labeling. 3D prescription accuracy was calculated by measuring the boundary mismatch in each dimension and percentage of manual volume covered by AI prescription. The automated prescription was implemented on a 3 T MR system and evaluated prospectively on healthy volunteers. STATISTICAL TESTS Paired t-tests (threshold = 0.05) were conducted to evaluate significance of performance difference between trained networks. RESULTS In 208 testing datasets, the proposed method with full network had excellent agreement with manual annotations, with median IoU > 0.91 (interquartile range < 0.09) across all seven classes. The automated 3D prescription was accurate, with shifts <2.3 cm in superior/inferior dimension for 3D axial prescription for 99.5% of test datasets, comparable to radiologists' interreader reproducibility. The full network had significantly superior performance than the tiny network for 3D axial prescription in patients. Automated prescription performed well across single-shot fast spin-echo, gradient-echo, and balanced steady-state free-precession sequences in the prospective study. DATA CONCLUSION AI-based automated liver image prescription demonstrated promising performance across the patients, pathologies, and field strengths studied. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Ruiqi Geng
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Collin J. Buelo
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Mahalakshmi Sundaresan
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Jitka Starekova
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Nikolaos Panagiotopoulos
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany (Haus A, Ratzeburger Allee 160, 23562 Lübeck, Germany)
| | - Thekla H. Oechtering
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany (Haus A, Ratzeburger Allee 160, 23562 Lübeck, Germany)
| | - Edward M. Lawrence
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Marcin Ignaciuk
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Scott B. Reeder
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medicine, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Emergency Medicine, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Diego Hernando
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
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Garg Y, Seetharam K, Sharma M, Rohita DK, Nabi W. Role of Deep Learning in Computed Tomography. Cureus 2023; 15:e39160. [PMID: 37332431 PMCID: PMC10275744 DOI: 10.7759/cureus.39160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2023] [Indexed: 06/20/2023] Open
Abstract
Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.
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Affiliation(s)
- Yash Garg
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | | | - Manjari Sharma
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | - Dipesh K Rohita
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | - Waseem Nabi
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
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Durr AJ, Korol AS, Hathaway QA, Kunovac A, Taylor AD, Rizwan S, Pinti MV, Hollander JM. Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus. PLoS One 2023; 18:e0285512. [PMID: 37155623 PMCID: PMC10166525 DOI: 10.1371/journal.pone.0285512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023] Open
Abstract
Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets.
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Affiliation(s)
- Andrya J Durr
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Anna S Korol
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Quincy A Hathaway
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Center for Inhalation Toxicology (iTOX), West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Amina Kunovac
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Center for Inhalation Toxicology (iTOX), West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Andrew D Taylor
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Saira Rizwan
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Mark V Pinti
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- West Virginia University School of Pharmacy, Morgantown, West Virginia, United States of America
- Department of Physiology and Pharmacology, West Virginia University School of Pharmacy, Morgantown, West Virginia, United States of America
| | - John M Hollander
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
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Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Front Cardiovasc Med 2022; 9:945726. [PMID: 36312266 PMCID: PMC9608631 DOI: 10.3389/fcvm.2022.945726] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022] Open
Abstract
Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.
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Affiliation(s)
- Loucia Karatzia
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Dunja Aksentijevic
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,*Correspondence: Dunja Aksentijevic,
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11
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Li X, Liu X, Deng X, Fan Y. Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction. Biomedicines 2022; 10:2157. [PMID: 36140258 PMCID: PMC9495955 DOI: 10.3390/biomedicines10092157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/26/2022] [Accepted: 08/28/2022] [Indexed: 11/16/2022] Open
Abstract
Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML-based models to directly predict CVD based on risk factors or medical imaging findings and the ML-based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data-driven models and physics-driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.
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Affiliation(s)
- Xiaoyin Li
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiao Liu
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiaoyan Deng
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Yubo Fan
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
- School of Engineering Medicine, Beihang University, Beijing 100083, China
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12
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Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6348424. [PMID: 35860642 PMCID: PMC9293511 DOI: 10.1155/2022/6348424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/23/2022] [Indexed: 11/26/2022]
Abstract
Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information. On a broad variety of applications such as voice recognition and prediction, the long short-term memory (LSTM) fully connected layer (FCL) and the two convolutional neural networks (CNNs) have shown superior performance over deep learning networks (DLNs). DNNs are suitable for making high points for a more divisible region and CNNs are suitable for reducing recurrence types, LSTMs are appropriate for temporary displays, in the same way as CNNs are appropriate for reducing recurrence types. The CNN, LSTM, and DNN algorithms are acceptable for viewing. The complementarity of DNNs, CNNs, and LSTMs was investigated in this research by bringing them all together under the single architectural company. The researchers got the ECG data from the MIT-BIH arrhythmia database as a result of the investigation. Our results demonstrate that the approach proposed may expressively describe ECG series and identify abnormalities via scores that outperform existing supervised and unsupervised methods in both the short term and long term. The LSTM network and FCL additionally demonstrated that the unbalanced datasets associated with the ECG beat detection problem could be consistently resolved and that they were not susceptible to the accuracy of ECG signals. It is recommended that cardiologists employ the unique technique to aid them in performing reliable and impartial interpretation of ECG data in telemedicine settings.
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13
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Karakuş G, Değirmencioğlu A, Nanda NC. Artificial intelligence in echocardiography: Review and limitations including epistemological concerns. Echocardiography 2022; 39:1044-1053. [PMID: 35808922 DOI: 10.1111/echo.15417] [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: 04/08/2022] [Revised: 06/01/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE In this review we describe the use of artificial intelligence in the field of echocardiography. Various aspects and terminologies used in artificial intelligence are explained in an easy-to-understand manner and supplemented with illustrations related to echocardiography. Limitations of artificial intelligence, including epistemologic concerns from a philosophical standpoint, are also discussed. METHODS A narrative review of relevant papers was conducted. CONCLUSION We provide an overview of the usefulness of artificial intelligence in echocardiography and focus on how it can supplement current day-to-day clinical practice in the assessment of various cardiovascular disease entities. On the other hand, there are significant limitations, including epistemological concerns, which need to be kept in perspective.
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Affiliation(s)
- Gültekin Karakuş
- Department of Cardiology, School of Medicine, Acibadem University, Istanbul, Turkey
| | - Aleks Değirmencioğlu
- Department of Cardiology, School of Medicine, Acibadem University, Istanbul, Turkey
| | - Navin C Nanda
- Division of Cardiology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
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14
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Covas P, De Guzman E, Barrows I, Bradley AJ, Choi BG, Krepp JM, Lewis JF, Katz R, Tracy CM, Zeman RK, Earls JP, Choi AD. Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis. Front Cardiovasc Med 2022; 9:839400. [PMID: 35387447 PMCID: PMC8977643 DOI: 10.3389/fcvm.2022.839400] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice.
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Affiliation(s)
- Pedro Covas
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Eison De Guzman
- Department of Internal Medicine, The George Washington University School of Medicine, Washington, DC, United States
| | - Ian Barrows
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Andrew J. Bradley
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Brian G. Choi
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Joseph M. Krepp
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Jannet F. Lewis
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Richard Katz
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Cynthia M. Tracy
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Robert K. Zeman
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
| | - James P. Earls
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Andrew D. Choi
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
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15
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Seetharam K, Shrestha S, Sengupta PP. Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence. Interv Cardiol 2021; 16:e31. [PMID: 34754333 PMCID: PMC8559149 DOI: 10.15420/icr.2020.04] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.
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Affiliation(s)
- Karthik Seetharam
- West Virginia University Medicine Heart and Vascular Institute Morgantown, WV, US
| | - Sirish Shrestha
- West Virginia University Medicine Heart and Vascular Institute Morgantown, WV, US
| | - Partho P Sengupta
- West Virginia University Medicine Heart and Vascular Institute Morgantown, WV, US
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16
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Stevens BR, Pepine CJ. Emerging role of machine learning in cardiovascular disease investigation and translations. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2021; 11:100050. [PMID: 38559318 PMCID: PMC10978128 DOI: 10.1016/j.ahjo.2021.100050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/02/2021] [Accepted: 09/01/2021] [Indexed: 04/04/2024]
Abstract
Unexpected insights and practical advances in cardiovascular disease (CVD) are being discovered by rapidly advancing developments in supercomputers and machine learning (ML) software algorithms. These have been accelerated during the COVID-19 pandemic, and the resulting CVD translational implications of ML are steering new measures of prevention and treatment, new tools for objective clinical diagnosis, and even opportunities for rethinking basic foundations of CVD nosology. As the usual cardiovascular specialist may not be familiar with these tools, the editor has invited this brief overview.
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Affiliation(s)
- Bruce R. Stevens
- Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Carl J. Pepine
- Division of Cardiovascular Medicine, University of Florida College of Medicine, Gainesville, FL, USA
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17
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Seetharam K, Bhat P, Orris M, Prabhu H, Shah J, Asti D, Chawla P, Mir T. Artificial intelligence and machine learning in cardiovascular computed tomography. World J Cardiol 2021; 13:546-555. [PMID: 34754399 PMCID: PMC8554359 DOI: 10.4330/wjc.v13.i10.546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 07/10/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virgina University, Morgan Town, NY 26501, United States
| | - Premila Bhat
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Maxine Orris
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Hejmadi Prabhu
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Jilan Shah
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Deepak Asti
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Preety Chawla
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Tanveer Mir
- Department of Internal Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
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18
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Sermesant M, Delingette H, Cochet H, Jaïs P, Ayache N. Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol 2021; 18:600-609. [PMID: 33712806 DOI: 10.1038/s41569-021-00527-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.
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Affiliation(s)
| | | | - Hubert Cochet
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
| | - Pierre Jaïs
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
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19
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Friedrich S, Groß S, König IR, Engelhardt S, Bahls M, Heinz J, Huber C, Kaderali L, Kelm M, Leha A, Rühl J, Schaller J, Scherer C, Vollmer M, Seidler T, Friede T. Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:424-436. [PMID: 36713608 PMCID: PMC9707954 DOI: 10.1093/ehjdh/ztab054] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/21/2021] [Accepted: 06/07/2021] [Indexed: 02/01/2023]
Abstract
Aims Artificial intelligence (AI) and machine learning (ML) promise vast advances in medicine. The current state of AI/ML applications in cardiovascular medicine is largely unknown. This systematic review aims to close this gap and provides recommendations for future applications. Methods and results Pubmed and EMBASE were searched for applied publications using AI/ML approaches in cardiovascular medicine without limitations regarding study design or study population. The PRISMA statement was followed in this review. A total of 215 studies were identified and included in the final analysis. The majority (87%) of methods applied belong to the context of supervised learning. Within this group, tree-based methods were most commonly used, followed by network and regression analyses as well as boosting approaches. Concerning the areas of application, the most common disease context was coronary artery disease followed by heart failure and heart rhythm disorders. Often, different input types such as electronic health records and images were combined in one AI/ML application. Only a minority of publications investigated reproducibility and generalizability or provided a clinical trial registration. Conclusions A major finding is that methodology may overlap even with similar data. Since we observed marked variation in quality, reporting of the evaluation and transparency of data and methods urgently need to be improved.
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Affiliation(s)
- Sarah Friedrich
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany,Corresponding author. Tel: +0049-551-3964064,
| | - Stefan Groß
- Department of Internal Medicine B, University Medicine Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Inke R König
- Institute of Medical Biometry and Statistics, University of Lübeck, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
| | - Sandy Engelhardt
- Department of Internal Medicine III, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Mannheim/Heidelberg, Heidelberg, Germany,Informatics for Life, Heidelberg, Germany
| | - Martin Bahls
- Department of Internal Medicine B, University Medicine Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Judith Heinz
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany
| | - Cynthia Huber
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany
| | - Lars Kaderali
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany,Institute of Bioinformatics, University Medicine Greifswald, Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany
| | - Marcus Kelm
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany,Department of Congenital Heart Disease, Deutsches Herzzentrum Berlin (DHZB), Berlin, Germany,Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178 Berlin, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Andreas Leha
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
| | - Jasmin Rühl
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany
| | - Jens Schaller
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Clemens Scherer
- Department of Medicine I, University Hospital, LMU Munich, Marchioninistr. 15, 81377 München, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich, Munich, Germany
| | - Marcus Vollmer
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany,Institute of Bioinformatics, University Medicine Greifswald, Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany
| | - Tim Seidler
- DZHK (German Centre for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany,Clinic for Cardiology and Pneumology, University Medical Center Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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20
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Seetharam K, Min JK. Artificial Intelligence and Machine Learning in Cardiovascular Imaging. Methodist Debakey Cardiovasc J 2021; 16:263-271. [PMID: 33500754 DOI: 10.14797/mdcj-16-4-263] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular disease is the leading cause of mortality in Western countries and leads to a spectrum of complications that can complicate patient management. The emergence of artificial intelligence (AI) has garnered significant interest in many industries, and the field of cardiovascular imaging is no exception. Machine learning (ML) especially is showing significant promise in various diagnostic imaging modalities. As conventional statistics are reaching their apex in computational capabilities, ML can explore new possibilities and unravel hidden relationships. This will have a positive impact on diagnosis and prognosis for cardiovascular imaging. In this in-depth review, we highlight the role of AI and ML for various cardiovascular imaging modalities.
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21
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Seetharam K, Min JK. Artificial intelligence in cardiovascular imaging. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00019-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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22
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Seetharam K, Brito D, Farjo PD, Sengupta PP. The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review. Front Cardiovasc Med 2020; 7:618849. [PMID: 33426010 PMCID: PMC7786371 DOI: 10.3389/fcvm.2020.618849] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Daniel Brito
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Peter D Farjo
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Partho P Sengupta
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
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23
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Senbekov M, Saliev T, Bukeyeva Z, Almabayeva A, Zhanaliyeva M, Aitenova N, Toishibekov Y, Fakhradiyev I. The Recent Progress and Applications of Digital Technologies in Healthcare: A Review. Int J Telemed Appl 2020; 2020:8830200. [PMID: 33343657 PMCID: PMC7732404 DOI: 10.1155/2020/8830200] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects. PURPOSE The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.
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Affiliation(s)
- Maksut Senbekov
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | - Timur Saliev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | | | | | | | - Nazym Aitenova
- NJSC “Astana Medical University”, Nur-Sultan, Kazakhstan
| | | | - Ildar Fakhradiyev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
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24
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Wang Y, Zhu K, Li Y, Lv Q, Fu G, Zhang W. A machine learning-based approach for the prediction of periprocedural myocardial infarction by using routine data. Cardiovasc Diagn Ther 2020; 10:1313-1324. [PMID: 33224755 PMCID: PMC7666922 DOI: 10.21037/cdt-20-551] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 09/28/2020] [Indexed: 01/14/2023]
Abstract
BACKGROUND Periprocedural myocardial infarction (PMI) after percutaneous coronary intervention (PCI) is associated with the bad prognosis in patients. Current approaches to predict PMI fail to identify many people who would benefit from preventive treatment, and machine learning (ML) offers opportunity to improve the performance of ML models for PMI based on the big routine data. METHODS By using electronic medical records, we retrospectively extracted all records of patients from 2007 to 2019 in our cardiovascular center. The main enrollment criterion was that inpatients with one single coronary stenosis with stents implantation this time. The primary outcome was PMI [PMI3: cTnI >3-fold upper reference limit (URL); PMI5: cTnI >5-fold URL]. Four different ML algorithms [Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Networks (ANN)] were evaluated and their diagnostic accuracy measures were compared. RESULTS A total of (10,886) patients who were admitted in our hospital. PMI3 and PMI5 results were analyzed respectively. The incidence of PMI3 and PMI5 was 20.9% and 13.7%. In PMI3 Drop group, ANN (accuracy: 0.72; AUC: 0.77) showed the best power to predict the presence of PMI; In PMI3 Mean Group, RF (accuracy: 0.72; AUC: 0.77) showed the best power; In PMI5 Drop group, RF (accuracy: 0.67; AUC: 0.67) showed the best power; In PMI5 Mean group, RF (accuracy: 0.61; AUC: 0.67) showed the best power. CONCLUSIONS ML methods may provide accurate prediction of PMI in CAD patients, and could be used as a precise model in the preventive treatment of PMI.
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Affiliation(s)
- Yao Wang
- Department of Cardiology, Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kangjun Zhu
- College of Computer Science and Technology, Zhejiang University of Technology, Zhejiang University, Hangzhou, China
| | - Ya Li
- Department of Cardiology, Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qingbo Lv
- Department of Cardiology, Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Guosheng Fu
- Department of Cardiology, Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wenbin Zhang
- Department of Cardiology, Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8894694. [PMID: 32952992 PMCID: PMC7481991 DOI: 10.1155/2020/8894694] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/09/2020] [Accepted: 08/19/2020] [Indexed: 12/31/2022]
Abstract
Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.
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26
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Lapadula P, Mecca G, Santoro D, Solimando L, Veltri E. Greg, ML – Machine Learning for Healthcare at a Scale. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-020-00468-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
AbstractThis paper introduces the Greg, ML platform, a machine-learning engine and toolset conceived to generate automatic diagnostic suggestions based on patient profiles. Greg, ML departs from many other experiences in machine learning for healthcare in the fact that it was designed to handle a large number of different diagnoses, in the order of the hundreds. We discuss the architecture that stands at the core of Greg, ML, designed to handle the complex challenges posed by this ambitious goal, and confirm its effectiveness with experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned while developing the tool. In this respect, we underline that Greg, ML should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.
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Classification of cervical neoplasms on colposcopic photography using deep learning. Sci Rep 2020; 10:13652. [PMID: 32788635 PMCID: PMC7423899 DOI: 10.1038/s41598-020-70490-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 06/17/2020] [Indexed: 01/07/2023] Open
Abstract
Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and validated deep learning models to automatically classify cervical neoplasms on colposcopic photographs. Pre-trained convolutional neural networks were fine-tuned for two grading systems: the cervical intraepithelial neoplasia (CIN) system and the lower anogenital squamous terminology (LAST) system. The multi-class classification accuracies of the networks for the CIN system in the test dataset were 48.6 ± 1.3% by Inception-Resnet-v2 and 51.7 ± 5.2% by Resnet-152. The accuracies for the LAST system were 71.8 ± 1.8% and 74.7 ± 1.8%, respectively. The area under the curve (AUC) for discriminating high-risk lesions from low-risk lesions by Resnet-152 was 0.781 ± 0.020 for the CIN system and 0.708 ± 0.024 for the LAST system. The lesions requiring biopsy were also detected efficiently (AUC, 0.947 ± 0.030 by Resnet-152), and presented meaningfully on attention maps. These results may indicate the potential of the application of AI for automated reading of colposcopic photographs.
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Abstract
PURPOSE OF REVIEW Echocardiography is an indispensable tool in diagnostic cardiology and is fundamental to clinical care. Significant advances in cardiovascular imaging technology paralleled by rapid growth in electronic medical records, miniaturized devices, real-time monitoring, and wearable devices using body sensor network technology have led to the development of complex data. RECENT FINDINGS The intricate nature of these data can be overwhelming and exceed the capabilities of current statistical software. Machine learning (ML), a branch of artificial intelligence (AI), can help health care providers navigate through this complex labyrinth of information and unravel hidden discoveries. Furthermore, ML algorithms can help automate several tasks in echocardiography and clinical care. ML can serve as a valuable diagnostic tool for physicians in the field of echocardiography. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management. In this review article, we describe the role of AI and ML in echocardiography.
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Affiliation(s)
- Karthik Seetharam
- West Virginia University Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Sameer Raina
- West Virginia University Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Partho P Sengupta
- West Virginia University Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
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29
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Pieszko K, Hiczkiewicz J, Budzianowski J, Musielak B, Hiczkiewicz D, Faron W, Rzeźniczak J, Burchardt P. Clinical applications of artificial intelligence in cardiology on the verge of the decade. Cardiol J 2020; 28:460-472. [PMID: 32648252 DOI: 10.5603/cj.a2020.0093] [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: 02/18/2020] [Revised: 04/29/2020] [Accepted: 05/25/2020] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.
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Affiliation(s)
- Konrad Pieszko
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland. .,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland.
| | - Jarosław Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Jan Budzianowski
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Bogdan Musielak
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Dariusz Hiczkiewicz
- University of Zielona Góra, Department of Medicine and Medical Sciences, ul. Licealna 9, 65-417 Zielona Góra, Poland.,Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Wojciech Faron
- Nowa Sól Multidisciplinary Hospital, Clinical Department of Cardiology,, ul. Chałubińskiego 7, 67-100 Nowa Sól, Poland
| | - Janusz Rzeźniczak
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland
| | - Paweł Burchardt
- Józefa Strusia Hospital, Cardiology Clinic, Szwajcarska 3,, 61-285 Poznań, Poland.,Department of Biology and Environmental Protection, Poznań University of Medical Sciences, ul. Rokietnicka 8, 60-806 Poznań, Poland
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30
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Miyoshi T, Higaki A, Kawakami H, Yamaguchi O. Automated interpretation of the coronary angioscopy with deep convolutional neural networks. Open Heart 2020; 7:e001177. [PMID: 32404485 PMCID: PMC7228653 DOI: 10.1136/openhrt-2019-001177] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 02/28/2020] [Accepted: 04/16/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Coronary angioscopy (CAS) is a useful modality to assess atherosclerotic changes, but interpretation of the images requires expert knowledge. Deep convolutional neural networks (DCNN) can be used for diagnostic prediction and image synthesis. METHODS 107 images from 47 patients, who underwent CAS in our hospital between 2014 and 2017, and 864 images, selected from 142 MEDLINE-indexed articles published between 2000 and 2019, were analysed. First, we developed a prediction model for the angioscopic findings. Next, we made a generative adversarial networks (GAN) model to simulate the CAS images. Finally, we tried to control the output images according to the angioscopic findings with conditional GAN architecture. RESULTS For both yellow colour (YC) grade and neointimal coverage (NC) grade, we could observe strong correlations between the true grades and the predicted values (YC grade, average r=0.80±0.02, p<0.001; NC grade, average r=0.73±0.02, p<0.001). The binary classification model for the red thrombus yielded 0.71±0.03 F1-score and the area under the receiver operator characteristic curve was 0.91±0.02. The standard GAN model could generate realistic CAS images (average Inception score=3.57±0.06). GAN-based data augmentation improved the performance of the prediction models. In the conditional GAN model, there were significant correlations between given values and the expert's diagnosis in YC grade but not in NC grade. CONCLUSION DCNN is useful in both predictive and generative modelling that can help develop the diagnostic support system for CAS.
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Affiliation(s)
- Toru Miyoshi
- Department of Cardiology, Ehime Prefectural Imabari Hospital, Imabari, Japan
- Department of Cardiology, Pulmonology, Hypertension and Nephrology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Akinori Higaki
- Department of Cardiology, Pulmonology, Hypertension and Nephrology, Ehime University Graduate School of Medicine, Toon, Japan
- Hypertension and Vascular Research Unit, Lady Davis Institute for Medical Research, Montreal, Quebec, Canada
| | - Hideo Kawakami
- Department of Cardiology, Ehime Prefectural Imabari Hospital, Imabari, Japan
| | - Osamu Yamaguchi
- Department of Cardiology, Pulmonology, Hypertension and Nephrology, Ehime University Graduate School of Medicine, Toon, Japan
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Ng AC, Delgado V, Bax JJ. Individualized Patient Risk Stratification Using Machine Learning and Topological Data Analysis. JACC Cardiovasc Imaging 2020; 13:1133-1134. [DOI: 10.1016/j.jcmg.2020.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 02/12/2020] [Indexed: 12/31/2022]
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32
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Clinical Inference From Cardiovascular Imaging: Paradigm Shift Towards Machine-Based Intelligent Platform. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-0805-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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33
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Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiac Imaging. US CARDIOLOGY REVIEW 2020. [DOI: 10.15420/usc.2019.19.2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, WV
| | - Sirish Shrestha
- Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, WV
| | - Partho P Sengupta
- Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, WV
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34
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Akoumianakis I. Highlights of AHA Scientific Sessions 2019: novel approaches in cardiovascular risk reduction. Cardiovasc Res 2020; 116:e16-e18. [PMID: 31850503 DOI: 10.1093/cvr/cvz319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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35
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Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford) 2020; 2020:baaa010. [PMID: 32185396 PMCID: PMC7078068 DOI: 10.1093/database/baaa010] [Citation(s) in RCA: 167] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.
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Affiliation(s)
- Zeeshan Ahmed
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, 67 North Eagleville Road, Storrs, CT, USA
| | - Khalid Mohamed
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
| | - Saman Zeeshan
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - XinQi Dong
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
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36
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Killu AM, Gersh BJ. Response to: Risk stratification for stroke in atrial fibrillation: incorporating neurologists in the comprehensive management. Eur Heart J 2019; 40:3060. [PMID: 31302678 DOI: 10.1093/eurheartj/ehz500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Ammar M Killu
- Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN, USA
| | - Bernard J Gersh
- Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN, USA
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37
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Abstract
Over the last 15 years, cardiovascular magnetic resonance (CMR) imaging has progressively evolved to become an indispensable tool in cardiology. It is a non-invasive technique that enables objective and functional assessment of myocardial tissue. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of T1 and T2 parametric mapping to explore tissue characteristics. The emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. Significant progress in computer processing capabilities and cloud infrastructure has supported the growth of artificial intelligence in CMR imaging. In this review article, we describe recent advances in T1/T2 mapping, myocardial strain, and artificial intelligence in CMR imaging.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center, New York, USA
| | - Stamatios Lerakis
- Division of Cardiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center, New York, USA
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38
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Abstract
As the cost of healthcare in the United States increases at an unsustainable rate, health-policy leaders are looking towards innovative ways to maximize value in delivery of care. Incorporating technology, such as artificial intelligence/machine-learning, to assist physicians in decision-making and predicting outcomes, on a real-time basis, is a major topic of discussion. While machine learning is gradually pulling traction in the medical community, it still remains a nascent field in the realm of spine surgery. The current review aims to gather current literature discussing the validity and applicability of machine-learning models in spine surgery.
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Affiliation(s)
- Azeem Tariq Malik
- Department of Orthopaedics, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Safdar N Khan
- Department of Orthopaedics, The Ohio State University Wexner Medical Center, Columbus, OH, USA
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39
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Kagiyama N, Shrestha S, Farjo PD, Sengupta PP. Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease. J Am Heart Assoc 2019; 8:e012788. [PMID: 31450991 PMCID: PMC6755846 DOI: 10.1161/jaha.119.012788] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
| | - Sirish Shrestha
- West Virginia University Heart and Vascular Institute Morgantown WV
| | - Peter D Farjo
- West Virginia University Heart and Vascular Institute Morgantown WV
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40
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Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol 2019; 16:601-607. [PMID: 31555327 PMCID: PMC6748901 DOI: 10.11909/j.issn.1671-5411.2019.08.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 07/24/2019] [Accepted: 07/26/2019] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.
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Affiliation(s)
- Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Teresa Perillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Eliana De Rosa
- Department of Translational Medical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Mario Petretta
- Department of Translational Medical Sciences, University of Naples “Federico II”, Naples, Italy
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