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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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Seetharam K, Balla S, Bianco C, Cheung J, Pachulski R, Asti D, Nalluri N, Tejpal A, Mir P, Shah J, Bhat P, Mir T, Hamirani Y. Applications of Machine Learning in Cardiology. Cardiol Ther 2022; 11:355-368. [PMID: 35829916 PMCID: PMC9381660 DOI: 10.1007/s40119-022-00273-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022] Open
Abstract
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
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Affiliation(s)
- Karthik Seetharam
- Medicine Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Weil Cornell Medical Center, New York, NY, USA.
| | - Sudarshan Balla
- Medicine Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Christopher Bianco
- Medicine Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Jim Cheung
- Weil Cornell Medical Center, New York, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital-South Shore, New York, NY, USA
| | - Deepak Asti
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Astha Tejpal
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Premila Bhat
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Tanveer Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
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Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare (Basel) 2022; 10:healthcare10010154. [PMID: 35052317 PMCID: PMC8776229 DOI: 10.3390/healthcare10010154] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 02/04/2023] Open
Abstract
The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.
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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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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: 28] [Impact Index Per Article: 5.6] [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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Advances and New Insights in Post-Transplant Care: From Sequencing to Imaging. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-00828-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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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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