1
|
Park H, Kwon OS, Shim J, Kim D, Park JW, Kim YG, Yu HT, Kim TH, Uhm JS, Choi JI, Joung B, Lee MH, Pak HN. Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation. NPJ Digit Med 2024; 7:234. [PMID: 39237703 PMCID: PMC11377779 DOI: 10.1038/s41746-024-01234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 08/22/2024] [Indexed: 09/07/2024] Open
Abstract
The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.
Collapse
Affiliation(s)
- Hanjin Park
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Oh-Seok Kwon
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Jaemin Shim
- Division of Cardiology, Department of Internal Medicine, Korea University Medical Center, Seoul, Republic of Korea.
| | - Daehoon Kim
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Je-Wook Park
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Yun-Gi Kim
- Division of Cardiology, Department of Internal Medicine, Korea University Medical Center, Seoul, Republic of Korea
| | - Hee Tae Yu
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Tae-Hoon Kim
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Jae-Sun Uhm
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Jong-Il Choi
- Division of Cardiology, Department of Internal Medicine, Korea University Medical Center, Seoul, Republic of Korea
| | - Boyoung Joung
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Moon-Hyoung Lee
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Hui-Nam Pak
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea.
| |
Collapse
|
2
|
Monlezun DJ, MacKay K. Artificial Intelligence and Health Inequities in Dietary Interventions on Atherosclerosis: A Narrative Review. Nutrients 2024; 16:2601. [PMID: 39203738 PMCID: PMC11357035 DOI: 10.3390/nu16162601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 07/28/2024] [Accepted: 07/29/2024] [Indexed: 09/03/2024] Open
Abstract
Poor diet is the top modifiable mortality risk factor globally, accounting for 11 million deaths annually with half being from diet-linked atherosclerotic cardiovascular disease (ASCVD). Yet, most of the world cannot afford a healthy diet-as the hidden costs of the inadequate global food system total over USD 13 trillion annually-let alone the much more clinically, financially, and ecologically costly and resource-intensive medical interventions required to address the disease progression and acute complications of ASCVD. Yet, AI is increasingly understood as a force multiplying revolutionary technology which may catalyze multi-sector efforts in medicine and public health to better address these significant health challenges. This novel narrative review seeks to provide the first known overview of the state-of-the-art in clinical interventions and public health policies in healthy diets for ASCVD, accelerated by health equity-focused AI. It is written from the first-hand practitioner perspective to provide greater relevance and applicability for health professionals and data scientists. The review summarizes the emerging trends and leading use cases in population health risk stratification and precision public health, AI democratizing clinical diagnosis, digital twins in precision nutrition, and AI-enabled culinary medicine as medical education and treatment. This review may, therefore, help inform and advance the evidence-based foundation for more clinically effective, financially efficient, and societally equitable dietary and nutrition interventions for ASCVD.
Collapse
Affiliation(s)
- Dominique J. Monlezun
- Department of Hospital Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA;
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Faculty of Bioethics, Ateneo Pontificio Regina Apostolorum, 00163 Rome, Italy
- School of Bioethics, Universidad Anahuac México, Mexico City 52786, Mexico
- Center for Artificial Intelligence and Health Equities, Global System Analytics & Structures, Rochester, MN 55905, USA
| | - Keir MacKay
- Department of Hospital Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| |
Collapse
|
3
|
Parsa S, Somani S, Dudum R, Jain SS, Rodriguez F. Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time? Curr Atheroscler Rep 2024; 26:263-272. [PMID: 38780665 DOI: 10.1007/s11883-024-01210-w] [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] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE OF REVIEW This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. RECENT FINDINGS AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
Collapse
Affiliation(s)
- Shyon Parsa
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Sulaiman Somani
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Ramzi Dudum
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Sneha S Jain
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Center for Digital Health, Stanford University, Stanford, California, USA.
| |
Collapse
|
4
|
Patrascanu OS, Tutunaru D, Musat CL, Dragostin OM, Fulga A, Nechita L, Ciubara AB, Piraianu AI, Stamate E, Poalelungi DG, Dragostin I, Iancu DCE, Ciubara A, Fulga I. Future Horizons: The Potential Role of Artificial Intelligence in Cardiology. J Pers Med 2024; 14:656. [PMID: 38929877 PMCID: PMC11204977 DOI: 10.3390/jpm14060656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/11/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.
Collapse
Affiliation(s)
- Octavian Stefan Patrascanu
- Department of Cardiology, University Emergency Hospital of Bucharest, 169 Splaiul Independentei St, 050098 Bucharest, Romania; (O.S.P.); (E.S.)
| | - Dana Tutunaru
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Carmina Liana Musat
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Oana Maria Dragostin
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Luiza Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Alexandru Bogdan Ciubara
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Alin Ionut Piraianu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Elena Stamate
- Department of Cardiology, University Emergency Hospital of Bucharest, 169 Splaiul Independentei St, 050098 Bucharest, Romania; (O.S.P.); (E.S.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Diana Gina Poalelungi
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Ionut Dragostin
- Emergency County Clinical Hospital, 2 Buzaului St, 810325 Braila, Romania;
| | | | - Anamaria Ciubara
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| |
Collapse
|
5
|
Haverkamp W, Strodthoff N. [Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?]. Herzschrittmacherther Elektrophysiol 2024; 35:104-110. [PMID: 38361131 DOI: 10.1007/s00399-024-00997-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024]
Abstract
The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.
Collapse
Affiliation(s)
- Wilhelm Haverkamp
- Abteilung für Kardiologie und Metabolismus, Medizinische Klinik mit Schwerpunkt Kardiologie, Campus Virchow-Klinikum, Deutsches Herzzentrum der Charité, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
| | - Nils Strodthoff
- Department für Versorgungsforschung, Fakultät VI - Medizin und Gesundheitswissenschaften, Abteilung AI4Health, Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
| |
Collapse
|