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Dhingra LS, Aminorroaya A, Sangha V, Camargos AP, Vasisht Shankar S, Coppi A, Foppa M, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.10.06.24314939. [PMID: 39417095 PMCID: PMC11483021 DOI: 10.1101/2024.10.06.24314939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Background Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility. Objective To leverage 12-lead ECG images for automated detection and prediction of multiple SHDs using a novel deep learning model. Methods We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms (TTEs) performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH). SHDs were defined based on TTEs with LV ejection fraction <40%, moderate-to-severe left-sided valvular disease (aortic/mitral stenosis or regurgitation), or severe left ventricular hypertrophy (IVSd > 1.5cm and diastolic dysfunction). We developed an ensemble XGBoost model, PRESENT-SHD, as a composite screen across all SHDs. We validated PRESENT-SHD at 4 US hospitals and a prospective population-based cohort study, the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), with concurrent protocolized ECGs and TTEs. We also used PRESENT-SHD for risk stratification of new-onset SHD or heart failure (HF) in clinical cohorts and the population-based UK Biobank (UKB). Results The models were developed using 261,228 ECGs from 93,693 YNHH patients and evaluated on a single ECG from 11,023 individuals at YNHH (19% with SHD), 44,591 across external hospitals (20-27% with SHD), and 3,014 in the ELSA-Brasil (3% with SHD). In the held-out test set, PRESENT-SHD demonstrated an AUROC of 0.886 (0.877-894), sensitivity of 89%, and specificity of 66%. At hospital-based sites, PRESENT-SHD had AUROCs ranging from 0.854-0.900, with sensitivities and specificities of 93-96% and 51-56%, respectively. The model generalized well to ELSA-Brasil (AUROC, 0.853 [0.811-0.897], sensitivity 88%, specificity 62%). PRESENT-SHD performance was consistent across demographic subgroups. A positive PRESENT-SHD screen portended a 2- to 4-fold higher risk of new-onset SHD/HF, independent of demographics, comorbidities, and the competing risk of death across clinical sites and UKB, with high predictive discrimination. Conclusion We developed and validated PRESENT-SHD, an AI-ECG tool identifying a range of SHD using images of 12-lead ECGs, representing a robust, scalable, and accessible modality for automated SHD screening and risk stratification. CONDENSED ABSTRACT Screening for structural heart disorders (SHDs) requires cardiac imaging, which has limited accessibility. To leverage 12-lead ECG images for automated detection and prediction of multiple SHDs, we developed PRESENT-SHD, an ensemble deep learning-based model. PRESENT-SHD demonstrated excellent performance for detecting SHDs across 5 US hospitals and a population-based cohort in Brazil. The model successfully predicted the risk of new-onset SHD or heart failure in both US clinical cohorts and the community-based UK Biobank. By using ubiquitous ECG images to predict a composite outcome of multiple SHDs, PRESENT-SHD establishes a scalable paradigm for cardiovascular screening and risk stratification.
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Sanabria M, Tastet L, Pelletier S, Leclercq M, Ohl L, Hermann L, Mattei PA, Precioso F, Coté N, Pibarot P, Droit A. AI-Enhanced Prediction of Aortic Stenosis Progression: Insights From the PROGRESSA Study. JACC. ADVANCES 2024; 3:101234. [PMID: 39309663 PMCID: PMC11416525 DOI: 10.1016/j.jacadv.2024.101234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 07/12/2024] [Accepted: 07/26/2024] [Indexed: 09/25/2024]
Abstract
Background Aortic valve stenosis (AS) is a progressive chronic disease with progression rates that vary in patients and therefore difficult to predict. Objectives The aim of this study was to predict the progression of AS using comprehensive and longitudinal patient data. Methods Machine and deep learning algorithms were trained on a data set of 303 patients enrolled in the PROGRESSA (Metabolic Determinants of the Progression of Aortic Stenosis) study who underwent clinical and echocardiographic follow-up on an annual basis. Performance of the models was measured to predict disease progression over long (next 5 years) and short (next 2 years) terms and was compared to a standard clinical model with usually used features in clinical settings based on logistic regression. Results For each annual follow-up visit including baseline, we trained various supervised learning algorithms in predicting disease progression at 2- and 5-year terms. At both terms, LightGBM consistently outperformed other models with the highest average area under curves across patient visits (0.85 at 2 years, 0.83 at 5 years). Recurrent neural network-based models (Gated Recurrent Unit and Long Short-Term Memory) and XGBoost also demonstrated strong predictive capabilities, while the clinical model showed the lowest performance. Conclusions This study demonstrates how an artificial intelligence-guided approach in clinical routine could help enhance risk stratification of AS. It presents models based on multisource comprehensive data to predict disease progression and clinical outcomes in patients with mild-to-moderate AS at baseline.
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Affiliation(s)
- Melissa Sanabria
- Centre hospitalier universitaire de Québec – Université Laval, Québec City, Québec, Canada
- Université Côte d'Azur, Inria, CNRS, I3S, Maasai, Sophia Antipolis, France
| | - Lionel Tastet
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Québec City, Québec, Canada
- Cardiovascular Division, Department of Medicine, University of California, San Francisco, California, USA
| | - Simon Pelletier
- Centre hospitalier universitaire de Québec – Université Laval, Québec City, Québec, Canada
| | - Mickael Leclercq
- Centre hospitalier universitaire de Québec – Université Laval, Québec City, Québec, Canada
| | - Louis Ohl
- Centre hospitalier universitaire de Québec – Université Laval, Québec City, Québec, Canada
- Université Côte d'Azur, Inria, CNRS, I3S, Maasai, Sophia Antipolis, France
| | - Lara Hermann
- Centre hospitalier universitaire de Québec – Université Laval, Québec City, Québec, Canada
| | | | - Frederic Precioso
- Université Côte d'Azur, Inria, CNRS, I3S, Maasai, Sophia Antipolis, France
| | - Nancy Coté
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Québec City, Québec, Canada
| | - Philippe Pibarot
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Québec City, Québec, Canada
| | - Arnaud Droit
- Centre hospitalier universitaire de Québec – Université Laval, Québec City, Québec, Canada
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Thiruganasambandamoorthy V, Probst MA, Poterucha TJ, Sandhu RK, Toarta C, Raj SR, Sheldon R, Rahgozar A, Grant L. Role of Artificial Intelligence in Improving Syncope Management. Can J Cardiol 2024; 40:1852-1864. [PMID: 38838932 DOI: 10.1016/j.cjca.2024.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/25/2024] [Accepted: 05/01/2024] [Indexed: 06/07/2024] Open
Abstract
Syncope is common in the general population and a common presenting symptom in acute care settings. Substantial costs are attributed to the care of patients with syncope. Current challenges include differentiating syncope from its mimickers, identifying serious underlying conditions that caused the syncope, and wide variations in current management. Although validated risk tools exist, especially for short-term prognosis, there is inconsistent application, and the current approach does not meet patient needs and expectations. Artificial intelligence (AI) techniques, such as machine learning methods including natural language processing, can potentially address the current challenges in syncope management. Preliminary evidence from published studies indicates that it is possible to accurately differentiate syncope from its mimickers and predict short-term prognosis and hospitalisation. More recently, AI analysis of electrocardiograms has shown promise in detection of serious structural and functional cardiac abnormalities, which has the potential to improve syncope care. Future AI studies have the potential to address current issues in syncope management. AI can automatically prognosticate risk in real time by accessing traditional and nontraditional data. However, steps to mitigate known problems such as generalisability, patient privacy, data protection, and liability will be needed. In the past AI has had limited impact due to underdeveloped analytical methods, lack of computing power, poor access to powerful computing systems, and availability of reliable high-quality data. All impediments except data have been solved. AI will live up to its promise to transform syncope care if the health care system can satisfy AI requirement of large scale, robust, accurate, and reliable data.
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Affiliation(s)
- Venkatesh Thiruganasambandamoorthy
- Department of Emergency Medicine, University of Ottawa, Ottawa, Ontario, Canada; Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.
| | - Marc A Probst
- Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Timothy J Poterucha
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Roopinder K Sandhu
- Libin Cardiovascular Institute, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Cristian Toarta
- Department of Emergency Medicine, McGill University, Montréal, Québec, Canada; McGill University Health Centre, Montréal, Québec, Canada
| | - Satish R Raj
- Libin Cardiovascular Institute, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Robert Sheldon
- Libin Cardiovascular Institute, Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Arya Rahgozar
- Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada; School of Engineering Design and Teaching Innovation, University of Ottawa, Ottawa, Ontario, Canada
| | - Lars Grant
- Department of Emergency Medicine, McGill University, Montréal, Québec, Canada; Lady Davis Research Institute, Montréal, Québec, Canada; Jewish General Hospital, Montréal, Québec, Canada
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Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024; 40:1788-1803. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
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Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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Adedinsewo DA, Morales-Lara AC, Afolabi BB, Kushimo OA, Mbakwem AC, Ibiyemi KF, Ogunmodede JA, Raji HO, Ringim SH, Habib AA, Hamza SM, Ogah OS, Obajimi G, Saanu OO, Jagun OE, Inofomoh FO, Adeolu T, Karaye KM, Gaya SA, Alfa I, Yohanna C, Venkatachalam KL, Dugan J, Yao X, Sledge HJ, Johnson PW, Wieczorek MA, Attia ZI, Phillips SD, Yamani MH, Tobah YB, Rose CH, Sharpe EE, Lopez-Jimenez F, Friedman PA, Noseworthy PA, Carter RE. Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial. Nat Med 2024; 30:2897-2906. [PMID: 39223284 PMCID: PMC11485252 DOI: 10.1038/s41591-024-03243-9] [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: 06/25/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024]
Abstract
Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05-4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85-3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576.
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Affiliation(s)
| | | | - Bosede B Afolabi
- Department of Obstetrics and Gynaecology, College of Medicine and Centre for Clinical Trials, Research and Implementation Science, University of Lagos, Lagos, Nigeria
| | - Oyewole A Kushimo
- Cardiology Unit, Department of Medicine, Lagos University Teaching Hospital, Lagos, Nigeria
| | - Amam C Mbakwem
- Cardiology Unit, Department of Medicine, Lagos University Teaching Hospital, Lagos, Nigeria
| | - Kehinde F Ibiyemi
- Department of Obstetrics & Gynaecology, University of Ilorin Teaching Hospital, Ilorin, Nigeria
| | | | - Hadijat Olaide Raji
- Department of Obstetrics & Gynaecology, University of Ilorin Teaching Hospital, Ilorin, Nigeria
| | - Sadiq H Ringim
- Department of Medicine, Rasheed Shekoni Specialist Hospital, Dutse, Nigeria
| | - Abdullahi A Habib
- Department of Obstetrics and Gynaecology, Rasheed Shekoni Specialist Hospital, Dutse, Nigeria
| | - Sabiu M Hamza
- Department of Medicine, Rasheed Shekoni Specialist Hospital, Dutse, Nigeria
| | | | - Gbolahan Obajimi
- Department of Obstetrics and Gynaecology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Olusoji E Jagun
- Department of Obstetrics and Gynaecology, Olabisi Onabanjo University Teaching Hospital, Sagamu, Nigeria
| | - Francisca O Inofomoh
- Cardiology Unit, Department of Medicine, Olabisi Onabanjo University Teaching Hospital, Sagamu, Nigeria
| | - Temitope Adeolu
- Cardiology Unit, Department of Medicine, Olabisi Onabanjo University Teaching Hospital, Sagamu, Nigeria
| | - Kamilu M Karaye
- Department of Medicine, Bayero University and Aminu Kano Teaching Hospital, Kano, Nigeria
| | - Sule A Gaya
- Department of Obstetrics and Gynaecology, Bayero University and Aminu Kano Teaching Hospital, Kano, Nigeria
| | - Isiaka Alfa
- Department of Medicine, Bayero University and Aminu Kano Teaching Hospital, Kano, Nigeria
| | - Cynthia Yohanna
- Lakeside Healthcare at Yaxley, the Health Centre, Peterborough, United Kingdom
| | - K L Venkatachalam
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Jennifer Dugan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Xiaoxi Yao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Hanna J Sledge
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Patrick W Johnson
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Mikolaj A Wieczorek
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sabrina D Phillips
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Mohamad H Yamani
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA
| | | | - Carl H Rose
- Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, MN, USA
| | - Emily E Sharpe
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
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Hou Y, Fan Z, Li J, Zeng Z, Lv G, Lin J, Zhou L, Wu T, Cao Q. Deep Learning-based 12-Lead Electrocardiogram for Low Left Ventricular Ejection Fraction Detection in Patients. Can J Cardiol 2024:S0828-282X(24)00980-2. [PMID: 39343388 DOI: 10.1016/j.cjca.2024.09.018] [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: 06/16/2024] [Revised: 09/12/2024] [Accepted: 09/21/2024] [Indexed: 10/01/2024] Open
Abstract
BACKGROUND Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly identifying low ejection fraction is crucial for managing progression and averting mortality. This study developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to identify patients with low ejection fraction and predict LVEF values. METHODS The ECG data were used as input, and the algorithm generated the probability of the patient suffering a low ejection fraction as well as estimating the LVEF value. A 5-year follow-up study on a group of individuals who initially had normal LVEF values was also performed. Furthermore, external validation of the algorithm performance was conducted using the Medical Information Mart for Intensive Care (MIMIC-IV) database. RESULTS The algorithm's performance on the test set yielded an area under the curve (AUC) of 0.965 for detecting LVEF ≤ 50%. The algorithm had an accuracy of 92.8%, sensitivity of 88.8%, and specificity of 92.9%. For LVEF regression, the method showed a mean absolute error (MAE) of 5.28 (95% CI: 5.23 - 5.33) for the testing set. Additionally, the algorithm obtained an AUC value of 0.848 and an MAE value of 9.56 during external validation. Patients with false-positive results had a significantly greater likelihood of developing a low ejection fraction compared to patients who received true negative results (26.2% vs. 2.0%, P < 0.0001). CONCLUSIONS The AI-ECG algorithm is capable of identifying low ejection fraction in patients with high accuracy. The AI-ECG algorithm is an efficient, prompt, and cost-effective screening tool for early heart failure.
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Affiliation(s)
- Yuxin Hou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Zhiping Fan
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Jiaqi Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Zi Zeng
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Gang Lv
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jingsheng Lin
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Liang Zhou
- Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Tao Wu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China; Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China; China Hospital Development Institute of Medical Intelligence Development, Shanghai Jiao Tong University School, Shanghai, 200025, China
| | - Qing Cao
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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Oikonomou EK, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. Eur Heart J 2024; 45:3204-3218. [PMID: 38976371 PMCID: PMC11400875 DOI: 10.1093/eurheartj/ehae415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/23/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024] Open
Abstract
The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, 195 Church St, 6th Floor, New Haven, CT 06510, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, New Haven, 06511 CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06510 CT, USA
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Pencovich N, Smith BH, Attia ZI, Jimenez FL, Bentall AJ, Schinstock CA, Khamash HA, Jadlowiec CC, Jarmi T, Mao SA, Park WD, Diwan TS, Friedman PA, Stegall MD. Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation. Transplantation 2024; 108:1976-1985. [PMID: 38557657 DOI: 10.1097/tp.0000000000005023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality ( P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.
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Affiliation(s)
- Niv Pencovich
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
- Department of General Surgery and Transplantation, Sheba Medical Center, Tel Hashomer, Tel-Aviv University, Tel-Aviv, Israel
| | - Byron H Smith
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Andrew J Bentall
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Carrie A Schinstock
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | | | | | - Tambi Jarmi
- Department of Transplant, Mayo Clinic Florida, Jacksonville, FL
| | - Shennen A Mao
- Division of Transplant Surgery, Department of Surgery, Mayo Clinic, Phoenix, AZ
| | - Walter D Park
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Tayyab S Diwan
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Mark D Stegall
- Departments of Surgery and Immunology, William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN
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Ribeiro JM, Nuis RJ, de Jaegere PPT. Artificial intelligence-empowered treatment decision-making in patients with aortic stenosis via early detection of cardiac amyloidosis. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:505-506. [PMID: 39318683 PMCID: PMC11417482 DOI: 10.1093/ehjdh/ztae053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Affiliation(s)
- Joana M Ribeiro
- Unidade de Intervenção e Diagnóstico Cardiovascular, Unidade Local de Saúde da Região de Aveiro, Avenida Artur Ravara n° 35, 3810-164 Aveiro, Portugal
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, ‘s-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands
| | - Rutger Jan Nuis
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, ‘s-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands
| | - Peter P T de Jaegere
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, ‘s-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands
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10
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Takase B, Ikeda T, Shimizu W, Abe H, Aiba T, Chinushi M, Koba S, Kusano K, Niwano S, Takahashi N, Takatsuki S, Tanno K, Watanabe E, Yoshioka K, Amino M, Fujino T, Iwasaki YK, Kohno R, Kinoshita T, Kurita Y, Masaki N, Murata H, Shinohara T, Yada H, Yodogawa K, Kimura T, Kurita T, Nogami A, Sumitomo N. JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia. Circ J 2024; 88:1509-1595. [PMID: 37690816 DOI: 10.1253/circj.cj-22-0827] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Affiliation(s)
| | - Takanori Ikeda
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine
| | - Wataru Shimizu
- Department of Cardiovascular Medicine, Nippon Medical School
| | - Haruhiko Abe
- Department of Heart Rhythm Management, University of Occupational and Environmental Health, Japan
| | - Takeshi Aiba
- Department of Clinical Laboratory Medicine and Genetics, National Cerebral and Cardiovascular Center
| | - Masaomi Chinushi
- School of Health Sciences, Niigata University School of Medicine
| | - Shinji Koba
- Division of Cardiology, Department of Medicine, Showa University School of Medicine
| | - Kengo Kusano
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center
| | - Shinichi Niwano
- Department of Cardiovascular Medicine, Kitasato University School of Medicine
| | - Naohiko Takahashi
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Seiji Takatsuki
- Department of Cardiology, Keio University School of Medicine
| | - Kaoru Tanno
- Cardiology Division, Cardiovascular Center, Showa University Koto-Toyosu Hospital
| | - Eiichi Watanabe
- Division of Cardiology, Department of Internal Medicine, Fujita Health University Bantane Hospital
| | | | - Mari Amino
- Department of Cardiology, Tokai University School of Medicine
| | - Tadashi Fujino
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine
| | - Yu-Ki Iwasaki
- Department of Cardiovascular Medicine, Nippon Medical School
| | - Ritsuko Kohno
- Department of Heart Rhythm Management, University of Occupational and Environmental Health, Japan
| | - Toshio Kinoshita
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine
| | - Yasuo Kurita
- Cardiovascular Center, International University of Health and Welfare, Mita Hospital
| | - Nobuyuki Masaki
- Department of Intensive Care Medicine, National Defense Medical College
| | | | - Tetsuji Shinohara
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Hirotaka Yada
- Department of Cardiology, International University of Health and Welfare, Mita Hospital
| | - Kenji Yodogawa
- Department of Cardiovascular Medicine, Nippon Medical School
| | - Takeshi Kimura
- Cardiovascular Medicine, Kyoto University Graduate School of Medicine
| | | | - Akihiko Nogami
- Department of Cardiology, Faculty of Medicine, University of Tsukuba
| | - Naokata Sumitomo
- Department of Pediatric Cardiology, Saitama Medical University International Medical Center
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11
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Lüscher TF, Wenzl FA, D'Ascenzo F, Friedman PA, Antoniades C. Artificial intelligence in cardiovascular medicine: clinical applications. Eur Heart J 2024:ehae465. [PMID: 39158472 DOI: 10.1093/eurheartj/ehae465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/07/2024] [Accepted: 07/03/2024] [Indexed: 08/20/2024] Open
Abstract
Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.
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Affiliation(s)
- Thomas F Lüscher
- Royal Brompton and Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College London, UK
- Cardiovascular Academic Group, King's College, London, UK
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
| | - Florian A Wenzl
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
- National Disease Registration and Analysis Service, NHS, London, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Department of Clinical Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza Hospital, Turin, Italy
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic and Mayo Foundation, Rochester, MN, USA
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
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12
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Harmon DM, Liu K, Dugan J, Jentzer JC, Attia ZI, Friedman PA, Dillon JJ. Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings. Clin J Am Soc Nephrol 2024; 19:952-958. [PMID: 39116276 PMCID: PMC11321728 DOI: 10.2215/cjn.0000000000000483] [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: 10/12/2023] [Accepted: 06/11/2024] [Indexed: 06/23/2024]
Abstract
Background Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.
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Affiliation(s)
- David M. Harmon
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Kan Liu
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Jennifer Dugan
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Jacob C. Jentzer
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Zachi I. Attia
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - Paul A. Friedman
- Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota
| | - John J. Dillon
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
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13
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Takase B, Ikeda T, Shimizu W, Abe H, Aiba T, Chinushi M, Koba S, Kusano K, Niwano S, Takahashi N, Takatsuki S, Tanno K, Watanabe E, Yoshioka K, Amino M, Fujino T, Iwasaki Y, Kohno R, Kinoshita T, Kurita Y, Masaki N, Murata H, Shinohara T, Yada H, Yodogawa K, Kimura T, Kurita T, Nogami A, Sumitomo N. JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia. J Arrhythm 2024; 40:655-752. [PMID: 39139890 PMCID: PMC11317726 DOI: 10.1002/joa3.13052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 04/22/2024] [Indexed: 08/15/2024] Open
Affiliation(s)
| | - Takanori Ikeda
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Wataru Shimizu
- Department of Cardiovascular MedicineNippon Medical School
| | - Haruhiko Abe
- Department of Heart Rhythm ManagementUniversity of Occupational and Environmental HealthJapan
| | - Takeshi Aiba
- Department of Clinical Laboratory Medicine and GeneticsNational Cerebral and Cardiovascular Center
| | | | - Shinji Koba
- Division of Cardiology, Department of MedicineShowa University School of Medicine
| | - Kengo Kusano
- Department of Cardiovascular MedicineNational Cerebral and Cardiovascular Center
| | - Shinichi Niwano
- Department of Cardiovascular MedicineKitasato University School of Medicine
| | - Naohiko Takahashi
- Department of Cardiology and Clinical Examination, Faculty of MedicineOita University
| | | | - Kaoru Tanno
- Cardiovascular Center, Cardiology DivisionShowa University Koto‐Toyosu Hospital
| | - Eiichi Watanabe
- Division of Cardiology, Department of Internal MedicineFujita Health University Bantane Hospital
| | | | - Mari Amino
- Department of CardiologyTokai University School of Medicine
| | - Tadashi Fujino
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Yu‐ki Iwasaki
- Department of Cardiovascular MedicineNippon Medical School
| | - Ritsuko Kohno
- Department of Heart Rhythm ManagementUniversity of Occupational and Environmental HealthJapan
| | - Toshio Kinoshita
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Yasuo Kurita
- Cardiovascular Center, Mita HospitalInternational University of Health and Welfare
| | - Nobuyuki Masaki
- Department of Intensive Care MedicineNational Defense Medical College
| | | | - Tetsuji Shinohara
- Department of Cardiology and Clinical Examination, Faculty of MedicineOita University
| | - Hirotaka Yada
- Department of CardiologyInternational University of Health and Welfare Mita Hospital
| | - Kenji Yodogawa
- Department of Cardiovascular MedicineNippon Medical School
| | - Takeshi Kimura
- Cardiovascular MedicineKyoto University Graduate School of Medicine
| | | | - Akihiko Nogami
- Department of Cardiology, Faculty of MedicineUniversity of Tsukuba
| | - Naokata Sumitomo
- Department of Pediatric CardiologySaitama Medical University International Medical Center
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14
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Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.2024.05.003] [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: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
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15
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Hadida Barzilai D, Cohen-Shelly M, Sorin V, Zimlichman E, Massalha E, Allison TG, Klang E. Machine learning in cardiac stress test interpretation: a systematic review. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:401-408. [PMID: 39081945 PMCID: PMC11284008 DOI: 10.1093/ehjdh/ztae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 03/13/2024] [Accepted: 04/01/2024] [Indexed: 08/02/2024]
Abstract
Coronary artery disease (CAD) is a leading health challenge worldwide. Exercise stress testing is a foundational non-invasive diagnostic tool. Nonetheless, its variable accuracy prompts the exploration of more reliable methods. Recent advancements in machine learning (ML), including deep learning and natural language processing, have shown potential in refining the interpretation of stress testing data. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of ML applications in stress electrocardiogram (ECG) and stress echocardiography for CAD prognosis. Medical Literature Analysis and Retrieval System Online, Web of Science, and the Cochrane Library were used as databases. We analysed the ML models, outcomes, and performance metrics. Overall, seven relevant studies were identified. Machine-learning applications in stress ECGs resulted in sensitivity and specificity improvements. Some models achieved rates of above 96% in both metrics and reduced false positives by up to 21%. In stress echocardiography, ML models demonstrated an increase in diagnostic precision. Some models achieved specificity and sensitivity rates of up to 92.7 and 84.4%, respectively. Natural language processing applications enabled the categorization of stress echocardiography reports, with accuracy rates nearing 98%. Limitations include a small, retrospective study pool and the exclusion of nuclear stress testing, due to its well-documented status. This review indicates the potential of artificial intelligence applications in refining CAD stress testing assessment. Further development for real-world use is warranted.
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Affiliation(s)
- Dor Hadida Barzilai
- Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
| | - Michal Cohen-Shelly
- Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
- Leviev Heart Center, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
| | - Vera Sorin
- School of Medicine, Tel Aviv University, Tel Aviv, Ramat Aviv 69978, Israel
- Department of Diagnostic Radiology, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
| | - Eyal Zimlichman
- Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
- School of Medicine, Tel Aviv University, Tel Aviv, Ramat Aviv 69978, Israel
- Sheba Medical Center, The Sheba Talpiot Medical Leadership Program, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
| | - Eias Massalha
- Leviev Heart Center, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
| | - Thomas G Allison
- Department of Cardiovascular Medicine, Mayo Clinic, 21 2nd St SW Suite 30, Rochester, MN 55905, USA
- Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA
| | - Eyal Klang
- Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
- School of Medicine, Tel Aviv University, Tel Aviv, Ramat Aviv 69978, Israel
- Department of Diagnostic Radiology, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
- Sheba Medical Center, The Sheba Talpiot Medical Leadership Program, 31 Emek Ha'ela, Ramat Gan 5262000, Israel
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029-5674, USA
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16
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Bauer A, Dlaska C. Implantable cardiac monitors: the digital future of risk prediction? EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:397-398. [PMID: 39081935 PMCID: PMC11284001 DOI: 10.1093/ehjdh/ztae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Affiliation(s)
- Axel Bauer
- Department of Cardiology, Medical University of Innsbruck, Anichstr. 35, 6020 Innsbruck, Austria
| | - Clemens Dlaska
- Department of Cardiology, Medical University of Innsbruck, Anichstr. 35, 6020 Innsbruck, Austria
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17
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Posan E, Richie R. Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG). J Insur Med 2024; 51:64-76. [PMID: 39266002 DOI: 10.17849/insm-51-2-64-76.1] [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: 05/02/2024] [Accepted: 07/22/2024] [Indexed: 09/14/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific conditions and findings will be discussed, followed by reviewing associated terminology and methodology. In the appendix, definitions of AUC versus accuracy are explained. The application of deep learning models enables detecting diseases from normal electrocardiograms at accuracy not previously achieved by technology or human experts. Results with AI enabled ECG are encouraging as they considerably exceeded current screening models for specific conditions (i.e., atrial fibrillation, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy). This could potentially lead to a revitalization of the utilization of the ECG in the insurance domain. While we are embracing the findings with this rapidly evolving technology, but cautious optimism is still necessary at this point.
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Affiliation(s)
- Emoke Posan
- Chief Medical Officer, Life North America, PartnerRe Reinsurance
| | - Rod Richie
- Editor-in-Chief, Journal of Insurance Medicine
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18
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Zhang J, Fang J, Xu Y, Si G. How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives. Diagnostics (Basel) 2024; 14:1393. [PMID: 39001283 PMCID: PMC11241154 DOI: 10.3390/diagnostics14131393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.
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Affiliation(s)
- Jiaming Zhang
- Department of Radiology, Clinical Medical College, Southwest Medical University, Luzhou 646699, China; (J.Z.); (J.F.)
| | - Jiayi Fang
- Department of Radiology, Clinical Medical College, Southwest Medical University, Luzhou 646699, China; (J.Z.); (J.F.)
| | - Yanneng Xu
- Department of Radiology, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou 646699, China;
| | - Guangyan Si
- Department of Radiology, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou 646699, China;
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19
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Elias P, Jain SS, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2472-2486. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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Bhave S, Rodriguez V, Poterucha T, Mutasa S, Aberle D, Capaccione KM, Chen Y, Dsouza B, Dumeer S, Goldstein J, Hodes A, Leb J, Lungren M, Miller M, Monoky D, Navot B, Wattamwar K, Wattamwar A, Clerkin K, Ouyang D, Ashley E, Topkara VK, Maurer M, Einstein AJ, Uriel N, Homma S, Schwartz A, Jaramillo D, Perotte AJ, Elias P. Deep learning to detect left ventricular structural abnormalities in chest X-rays. Eur Heart J 2024; 45:2002-2012. [PMID: 38503537 PMCID: PMC11156488 DOI: 10.1093/eurheartj/ehad782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/24/2023] [Accepted: 11/14/2023] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND AND AIMS Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. METHODS A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. RESULTS The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. CONCLUSIONS Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.
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Affiliation(s)
- Shreyas Bhave
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
| | - Victor Rodriguez
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
| | - Timothy Poterucha
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Simukayi Mutasa
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Dwight Aberle
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Kathleen M Capaccione
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Yibo Chen
- Inova Fairfax Hospital Imaging Center, Inova Fairfax Medical Campus, Falls Church, VA, USA
| | - Belinda Dsouza
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Shifali Dumeer
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Jonathan Goldstein
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Aaron Hodes
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - Jay Leb
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Matthew Lungren
- Department of Radiology, University of California, SanFrancisco, CA, USA
| | - Mitchell Miller
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - David Monoky
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - Benjamin Navot
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Kapil Wattamwar
- Division of Vascular and Interventional Radiology, Department of Radiology, Montefiore Medical Center, Bronx, NY, USA
| | - Anoop Wattamwar
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - Kevin Clerkin
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - David Ouyang
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Euan Ashley
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Veli K Topkara
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Mathew Maurer
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Andrew J Einstein
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Nir Uriel
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Shunichi Homma
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Allan Schwartz
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Diego Jaramillo
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Adler J Perotte
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
| | - Pierre Elias
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
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21
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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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22
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Jain H, Goyal A, Khan AT, Khan NU, Jain J, Chopra S, Sulaiman SA, Reddy MM, Patel K, Khullar K, Daoud M, Sohail AH. Insights into calcific aortic valve stenosis: a comprehensive overview of the disease and advancing treatment strategies. Ann Med Surg (Lond) 2024; 86:3577-3590. [PMID: 38846838 PMCID: PMC11152847 DOI: 10.1097/ms9.0000000000002106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 04/14/2024] [Indexed: 06/09/2024] Open
Abstract
Aortic valve stenosis is a disease characterized by thickening and narrowing of the aortic valve (AV), most commonly due to calcification, which leads to left ventricular outflow obstruction called calcific aortic valve disease (CAVD). CAVD presents as a progressive clinical syndrome with cardiorespiratory symptoms, often with rapid deterioration. The modern-day pathophysiology of CAVD involves a complex interplay of genetic factors, chronic inflammation, lipid deposition, and valve calcification, with early CAVD stages resembling atherosclerosis. Various imaging modalities have been used to evaluate CAVD, with a recent trend of using advanced imaging to measure numerous AV parameters, such as peak jet velocity. Significant improvements in mortality have been achieved with transcatheter AV repair, but numerous therapeutics and modalities are being researched to delay the progression of CAVD. This article aims to provide a comprehensive review of CAVD, explore recent developments, and provide insights into future treatments with various novel modalities.
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Affiliation(s)
- Hritvik Jain
- Department of Internal Medicine, All India Institute of Medical Sciences (AIIMS), Jodhpur
| | - Aman Goyal
- Department of Internal Medicine, Seth Gordhandas Sunderdas (GS) Medical College and King Edward Memorial (KEM) Hospital, Mumbai
| | | | - Noor U. Khan
- Department of Public Health, Health Services Academy, Islamabad, Pakistan
| | - Jyoti Jain
- Department of Internal Medicine, All India Institute of Medical Sciences (AIIMS), Jodhpur
| | - Shrey Chopra
- Department of Internal Medicine, University College of Medical Sciences and Guru Teg Bahadur Hospital, Delhi
| | | | | | - Kush Patel
- Department of Internal Medicine, Baroda Medical College, Gujarat
| | - Kaarvi Khullar
- Department of Internal Medicine, Government Medical College and Hospital, Gondia, Maharashtra, India
| | - Mohamed Daoud
- Department of Internal Medicine, Bogomolets National Medical University, Kyiv, Ukraine
| | - Amir H. Sohail
- Department of Surgery, University of New Mexico Health Sciences, Albuquerque, New Mexico, USA
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23
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Kalmady SV, Salimi A, Sun W, Sepehrvand N, Nademi Y, Bainey K, Ezekowitz J, Hindle A, McAlister F, Greiner R, Sandhu R, Kaul P. Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level. NPJ Digit Med 2024; 7:133. [PMID: 38762623 PMCID: PMC11102430 DOI: 10.1038/s41746-024-01130-8] [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: 08/16/2023] [Accepted: 04/26/2024] [Indexed: 05/20/2024] Open
Abstract
Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80-90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.
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Affiliation(s)
- Sunil Vasu Kalmady
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Amir Salimi
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yousef Nademi
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Kevin Bainey
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Justin Ezekowitz
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Finlay McAlister
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Russel Greiner
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, CA, USA
| | - Padma Kaul
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.
- Department of Medicine, University of Alberta, Edmonton, AB, Canada.
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24
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Lin YT, Lin CS, Tsai CS, Tsai DJ, Lou YS, Fang WH, Lee YT, Lin C. Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases. Aging (Albany NY) 2024; 16:8717-8731. [PMID: 38761181 PMCID: PMC11164519 DOI: 10.18632/aging.205835] [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: 12/26/2023] [Accepted: 04/16/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.
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Affiliation(s)
- Yu-Ting Lin
- Department of Surgery, Division of Cardiovascular Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chin-Sheng Lin
- Department of Internal Medicine, Division of Cardiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chien-Sung Tsai
- Department of Surgery, Division of Cardiovascular Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Dung-Jang Tsai
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Yu-Sheng Lou
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Yung-Tsai Lee
- Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei, Taiwan
- Department of Exercise and Healthy Science, National Taipei University of Nursing and Healthy Science, Taipei, Taiwan
| | - Chin Lin
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan
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25
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Jia Y, Li Y, Luosang G, Wang J, Peng G, Pu X, Jiang W, Li W, Zhao Z, Peng Y, Feng Y, Wei J, Xu Y, Liu X, Yi Z, Chen M. Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:219-228. [PMID: 38774374 PMCID: PMC11104474 DOI: 10.1093/ehjdh/ztae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/21/2023] [Accepted: 01/06/2024] [Indexed: 05/24/2024]
Abstract
Aims Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.
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Affiliation(s)
- Yuheng Jia
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yiming Li
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Gaden Luosang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
- Department of Information Science and Technology, Tibet University, No.10 Zangda East Road, Lhasa 850000, Tibet, P. R. China
| | - Jianyong Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Gang Peng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Xingzhou Pu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Weili Jiang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Wenjian Li
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Zhengang Zhao
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yong Peng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yuan Feng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Jiafu Wei
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yuanning Xu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Xingbin Liu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Mao Chen
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
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26
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Sengupta PP, Kluin J, Lee SP, Oh JK, Smits AIPM. The future of valvular heart disease assessment and therapy. Lancet 2024; 403:1590-1602. [PMID: 38554727 DOI: 10.1016/s0140-6736(23)02754-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 08/15/2023] [Accepted: 12/06/2023] [Indexed: 04/02/2024]
Abstract
Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.
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Affiliation(s)
- Partho P Sengupta
- Division of Cardiovascular Diseases and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA; Cardiovascular Services, Robert Wood Johnson University Hospital, New Brunswick, NJ, USA.
| | - Jolanda Kluin
- Department of Cardiothoracic Surgery, Erasmus MC Rotterdam, Thorax Center, Rotterdam, Netherlands
| | - Seung-Pyo Lee
- Department of Internal Medicine, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Jae K Oh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthal I P M Smits
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
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27
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Baqal O, Habib EA, Hasabo EA, Galasso F, Barry T, Arsanjani R, Sweeney JP, Noseworthy P, David Fortuin F. Artificial intelligence-enabled electrocardiogram (AI-ECG) does not predict atrial fibrillation following patent foramen ovale closure. IJC HEART & VASCULATURE 2024; 51:101361. [PMID: 38379633 PMCID: PMC10877678 DOI: 10.1016/j.ijcha.2024.101361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/22/2024]
Abstract
Background Atrial fibrillation (AF) is a known complication following patent foramen ovale (PFO) closure. AI-enabled ECG (AI-ECG) acquired during normal sinus rhythm has been shown to identify individuals with AF by noting high-risk ECG features invisible to the human eye. We sought to characterize the value of AI-ECG in predicting AF development following PFO closure and investigate key clinical and procedural characteristics possibly associated with post-procedural AF. Methods We performed a retrospective analysis of patients who underwent PFO closure at our hospital from January 2011 to December 2022. We recorded the probability (%) of AF using the Mayo Clinic AI-ECG dashboard from pre- and post-procedure ECGs. The cut-off point of ≥ 11 %, which was found to optimally balance sensitivity and specificity in the original derivation paper (the Youden index) was used to label an AI-ECG "positive" for AF. Pre-procedural transesophageal echocardiography (TEE) and pre- and post-procedure transcranial doppler (TCD) data was also recorded. Results Out of 93 patients, 49 (53 %) were male, mean age was 55 ± 15 years with mean post-procedure follow up of 29 ± 3 months. Indication for PFO closure in 69 (74 %) patients was for secondary prevention of transient ischemic attack (TIA) and/or stroke. Twenty patients (22 %) developed paroxysmal AF post-procedure, with the majority within the first month post-procedure (15 patients, 75 %). Patients who developed AF were not significantly more likely to have a positive post-procedure AI-ECG than those who did not develop AF (30 % AF vs 27 % no AF, p = 0.8).Based on the PFO-Associated Stroke Causal Likelihood (PASCAL) classification, patients who had PFO closure for secondary prevention of TIA and/or stroke in the "possible" group were significantly more likely to develop AF than patients in "probable" and "unlikely" groups (p = 0.034). AF-developing patients were more likely to have post-procedure implantable loop recorder (ILR) (55 % vs 9.6 %, p < 0.001), and longer duration of ILR monitoring (121 vs 92.5 weeks, p = 0.035). There were no significant differences in TCD and TEE characteristics, device type, or device size between those who developed AF vs those who did not. Conclusions In this small, retrospective study, AI-ECG did not accurately distinguish patients who developed AF post-PFO closure from those who did not. Although AI-ECG has emerged as a valuable tool for risk prediction of AF, extrapolation of its performance to procedural settings such as PFO closure requires further investigation.
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Affiliation(s)
- Omar Baqal
- Department of Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Eiad A. Habib
- Department of Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Elfatih A. Hasabo
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, Clinical Science Institute, University of Galway, Galway, Ireland
- Discipline of Cardiology, Saolta Healthcare Group, Health Service Executive, Galway University Hospital, Galway, Ireland
| | - Francesca Galasso
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Timothy Barry
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - John P. Sweeney
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Peter Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - F. David Fortuin
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ, USA
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Linz D, Andrade JG, Arbelo E, Boriani G, Breithardt G, Camm AJ, Caso V, Nielsen JC, De Melis M, De Potter T, Dichtl W, Diederichsen SZ, Dobrev D, Doll N, Duncker D, Dworatzek E, Eckardt L, Eisert C, Fabritz L, Farkowski M, Filgueiras-Rama D, Goette A, Guasch E, Hack G, Hatem S, Haeusler KG, Healey JS, Heidbuechel H, Hijazi Z, Hofmeister LH, Hove-Madsen L, Huebner T, Kääb S, Kotecha D, Malaczynska-Rajpold K, Merino JL, Metzner A, Mont L, Ng GA, Oeff M, Parwani AS, Puererfellner H, Ravens U, Rienstra M, Sanders P, Scherr D, Schnabel R, Schotten U, Sohns C, Steinbeck G, Steven D, Toennis T, Tzeis S, van Gelder IC, van Leerdam RH, Vernooy K, Wadhwa M, Wakili R, Willems S, Witt H, Zeemering S, Kirchhof P. Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference. Europace 2024; 26:euae070. [PMID: 38591838 PMCID: PMC11003300 DOI: 10.1093/europace/euae070] [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: 12/22/2023] [Accepted: 02/16/2024] [Indexed: 04/10/2024] Open
Abstract
AIMS Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). METHODS AND RESULTS Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF. CONCLUSIONS Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF.
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Affiliation(s)
- Dominik Linz
- Department of Cardiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jason G Andrade
- Division of Cardiology, Vancouver General Hospital, Vancouver, Canada
- Montreal Heart Institute, Montreal, Canada
| | - Elena Arbelo
- Institut Clínic Cardiovascular, Hospital Clinic, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Institut d’Investigació August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart—ERN GUARD-Heart
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Polyclinic of Modena, Modena, Italy
| | - Guenter Breithardt
- Department of Cardiovascular Medicine, University Hospital, Münster, Germany
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
| | - A John Camm
- Cardiology Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George's University of London, London, UK
| | - Valeria Caso
- Stroke Unit, Santa Maria della Misericordia Hospital, University of Perugia, Perugia, Italy
| | - Jens Cosedis Nielsen
- Department of Cardiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | | | | | - Wolfgang Dichtl
- Department of Internal Medicine III, Cardiology and Angiology, Medical University Innsbruck, Innsbruck, Austria
| | | | - Dobromir Dobrev
- Institute of Pharmacology, Faculty of Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Nicolas Doll
- Department of Cardiac Surgery, Schüchtermann-Klinik, Bad Rothenfelde, Germany
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
| | | | - Lars Eckardt
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Cardiology II—Electrophysiology, University Hospital Münster, Münster, Germany
| | | | - Larissa Fabritz
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- University Center of Cardiovascular Science, UHZ, UKE, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Michal Farkowski
- Department of Cardiology, Ministry of Interior and Administration, National Medical Institute, Warsaw, Poland
| | - David Filgueiras-Rama
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Novel Arrhythmogenic Mechanisms Program, Madrid, Spain
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Cardiovascular Institute, C/ Profesor Martín Lagos, Madrid, Spain
| | - Andreas Goette
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Cardiology and Intensive Care Medicine, St Vincenz-Hospital Paderborn, Paderborn, Germany
| | - Eduard Guasch
- Institut d’Investigació August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- Clinic Barcelona, University of Barcelona, Barcelona, Spain
| | - Guido Hack
- Bristol-Myers Squibb GmbH & Co. KGaA, Munich, Germany
| | | | - Karl Georg Haeusler
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Neurology, Universitätsklinikum Würzburg (UKW), Würzburg, Germany
| | - Jeff S Healey
- Division of Cardiology, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Hein Heidbuechel
- Antwerp University Hospital, Cardiovascular Sciences, University of Antwerp, Antwerp, Belgium
| | - Ziad Hijazi
- Antwerp University Hospital, Cardiovascular Sciences, University of Antwerp, Antwerp, Belgium
- Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | | | - Leif Hove-Madsen
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- Biomedical Research Institute Barcelona (IIBB-CSIC), Barcelona, Spain
- IR Sant Pau, Hospital de Sant Pau, Barcelona, Spain
| | | | - Stefan Kääb
- European Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart—ERN GUARD-Heart
- Department of Medicine I, University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Munich Heart Alliance, Munich, Germany
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Trust, Birmingham, UK
| | - Katarzyna Malaczynska-Rajpold
- Lister Hospital, East and North Hertfordshire NHS Trust, Stevenage, UK
- Royal Brompton Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - José Luis Merino
- La Paz University Hospital, IdiPaz, Autonomous University of Madrid, Madrid, Spain
| | - Andreas Metzner
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
| | - Lluís Mont
- Institut Clínic Cardiovascular, Hospital Clinic, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Institut d’Investigació August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Ghulam Andre Ng
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Michael Oeff
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Cardiology Department, Medizinische Hochschule Brandenburg, Brandenburg/Havel, Germany
| | - Abdul Shokor Parwani
- Department of Cardiology, Deutsches Herzzentrum der Charité (CVK), Berlin, Germany
| | | | - Ursula Ravens
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Institute of Experimental Cardiovascular Medicine, University Clinic Freiburg, Freiburg, Germany
| | - Michiel Rienstra
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Prashanthan Sanders
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia
| | - Daniel Scherr
- Division of Cardiology, Medical University of Graz, Graz, Austria
| | - Renate Schnabel
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
| | - Ulrich Schotten
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Departments of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Christian Sohns
- Herz- und Diabeteszentrum Nordrhein-Westfalen, Universitätsklinik der Ruhr-Universität Bochum, Klinik für Elektrophysiologie—Rhythmologie, Bad Oeynhausen, Germany
| | - Gerhard Steinbeck
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Center for Cardiology at Clinic Starnberg, Starnberg, Germany
| | - Daniel Steven
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Heart Center, Department of Electrophysiology, University Hospital Cologne, Cologne, Germany
| | - Tobias Toennis
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
| | | | - Isabelle C van Gelder
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Kevin Vernooy
- Department of Cardiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Manish Wadhwa
- Medical Office, Philips Ambulatory Monitoring and Diagnostics, San Diego, CA, USA
| | - Reza Wakili
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- Department of Medicine and Cardiology, Goethe University, Frankfurt, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Germany
| | - Stephan Willems
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Asklepios Hospital St. Georg, Department of Cardiology and Internal Care Medicine, Faculty of Medicine, Semmelweis University Campus, Hamburg, Germany
| | | | - Stef Zeemering
- Departments of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Paulus Kirchhof
- Atrial Fibrillation NETwork (AFNET), Muenster, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site: Hamburg/Kiel/Lübeck, Hamburg, Germany
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, UK
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany
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Avola D, Cannistraci I, Cascio M, Cinque L, Fagioli A, Foresti GL, Rodolà E, Solito L. MV-MS-FETE: Multi-view multi-scale feature extractor and transformer encoder for stenosis recognition in echocardiograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108037. [PMID: 38271793 DOI: 10.1016/j.cmpb.2024.108037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 12/27/2023] [Accepted: 01/15/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND aortic stenosis is a common heart valve disease that mainly affects older people in developed countries. Its early detection is crucial to prevent the irreversible disease progression and, eventually, death. A typical screening technique to detect stenosis uses echocardiograms; however, variations introduced by other tissues, camera movements, and uneven lighting can hamper the visual inspection, leading to misdiagnosis. To address these issues, effective solutions involve employing deep learning algorithms to assist clinicians in detecting and classifying stenosis by developing models that can predict this pathology from single heart views. Although promising, the visual information conveyed by a single image may not be sufficient for an accurate diagnosis, especially when using an automatic system; thus, this indicates that different solutions should be explored. METHODOLOGY following this rationale, this paper proposes a novel deep learning architecture, composed of a multi-view, multi-scale feature extractor, and a transformer encoder (MV-MS-FETE) to predict stenosis from parasternal long and short-axis views. In particular, starting from the latter, the designed model extracts relevant features at multiple scales along its feature extractor component and takes advantage of a transformer encoder to perform the final classification. RESULTS experiments were performed on the recently released Tufts medical echocardiogram public dataset, which comprises 27,788 images split into training, validation, and test sets. Due to the recent release of this collection, tests were also conducted on several state-of-the-art models to create multi-view and single-view benchmarks. For all models, standard classification metrics were computed (e.g., precision, F1-score). The obtained results show that the proposed approach outperforms other multi-view methods in terms of accuracy and F1-score and has more stable performance throughout the training procedure. Furthermore, the experiments also highlight that multi-view methods generally perform better than their single-view counterparts. CONCLUSION this paper introduces a novel multi-view and multi-scale model for aortic stenosis recognition, as well as three benchmarks to evaluate it, effectively providing multi-view and single-view comparisons that fully highlight the model's effectiveness in aiding clinicians in performing diagnoses while also producing several baselines for the aortic stenosis recognition task.
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Affiliation(s)
- Danilo Avola
- Department of Computer Science, Sapienza University, Via Salaria 113, 00185, Rome, Italy
| | - Irene Cannistraci
- Department of Computer Science, Sapienza University, Via Salaria 113, 00185, Rome, Italy
| | - Marco Cascio
- Department of Computer Science, Sapienza University, Via Salaria 113, 00185, Rome, Italy
| | - Luigi Cinque
- Department of Computer Science, Sapienza University, Via Salaria 113, 00185, Rome, Italy
| | - Alessio Fagioli
- Department of Computer Science, Sapienza University, Via Salaria 113, 00185, Rome, Italy.
| | - Gian Luca Foresti
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
| | - Emanuele Rodolà
- Department of Computer Science, Sapienza University, Via Salaria 113, 00185, Rome, Italy
| | - Luciana Solito
- Department of Computer Science, Sapienza University, Via Salaria 113, 00185, Rome, Italy
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Ali AM, Ghobashy AA, Sultan AA, Elkhodary KI, El-Morsi M. A 3D scaling law for supravalvular aortic stenosis suited for stethoscopic auscultations. Heliyon 2024; 10:e26190. [PMID: 38390109 PMCID: PMC10881376 DOI: 10.1016/j.heliyon.2024.e26190] [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: 05/10/2023] [Revised: 11/24/2023] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
In this study a frequency scaling law for 3D anatomically representative supravalvular aortic stenosis (SVAS) cases is proposed. The law is uncovered for stethoscopy's preferred auscultation range (70-120 Hz). LES simulations are performed on the CFD solver Fluent, leveraging Simulia's Living Heart Human Model (LHHM), modified to feature hourglass stenoses that range between 30 to 80 percent (mild to severe) in addition to the descending aorta. For physiological hemodynamic boundary conditions the Windkessel model is implemented via a UDF subroutine. The flow-generated acoustic signal is then extracted using the FW-H model and analyzed using FFT. A preferred receiver location that matches clinical practice is confirmed (right intercostal space) and a correlation between the degree of stenosis and a corresponding acoustic frequency is obtained. Five clinical auscultation signals are tested against the scaling law, with the findings interpreted in relation to the NHS classification of stenosis and to the assessments of experienced cardiologists. The scaling law is thus shown to succeed as a potential quantitative decision-support tool for clinicians, enabling them to reliably interpret stethoscopic auscultations for all degrees of stenosis, which is especially useful for moderate degrees of SVAS. Computational investigation of more complex stenotic cases would enhance the clinical relevance of this proposed scaling law, and will be explored in future research.
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Affiliation(s)
- Ahmed M Ali
- Department of Mechanical Engineering, The American University in Cairo, 11835 New Cairo, Egypt
| | - Aly A Ghobashy
- Department of Mechanical Engineering, The American University in Cairo, 11835 New Cairo, Egypt
| | - Abdelrahman A Sultan
- Department of Mechanical Engineering, The American University in Cairo, 11835 New Cairo, Egypt
| | - Khalil I Elkhodary
- Department of Mechanical Engineering, The American University in Cairo, 11835 New Cairo, Egypt
| | - Mohamed El-Morsi
- Department of Mechanical Engineering, The American University in Cairo, 11835 New Cairo, Egypt
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Chen Y, Xiao X, He Q, Yao RQ, Zhang GY, Fan JR, Xue CX, Huang L. Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022: A bibliometric analysis. Heliyon 2024; 10:e25318. [PMID: 38356571 PMCID: PMC10864893 DOI: 10.1016/j.heliyon.2024.e25318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/22/2023] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Objective To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field. Methods The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table. Results A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was JMIR mHealth and uHealth. Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus. Conclusion Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.
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Affiliation(s)
- Ying Chen
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
| | - Xiang Xiao
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
| | - Qing He
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Rui-Qi Yao
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Gao-Yu Zhang
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Jia-Rong Fan
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Chong-Xiang Xue
- Beijing University of Chinese Medicine, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Li Huang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
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Singh S, Chaudhary R, Bliden KP, Tantry US, Gurbel PA, Visweswaran S, Harinstein ME. Meta-Analysis of the Performance of AI-Driven ECG Interpretation in the Diagnosis of Valvular Heart Diseases. Am J Cardiol 2024; 213:126-131. [PMID: 38103769 PMCID: PMC10842912 DOI: 10.1016/j.amjcard.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/17/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
Valvular heart diseases (VHDs) significantly impact morbidity and mortality rates worldwide. Early diagnosis improves patient outcomes. Artificial intelligence (AI) applied to electrocardiogram (ECG) interpretation presents a promising approach for early VHD detection. We conducted a meta-analysis on the efficacy of AI models in this context. We reviewed databases including PubMed, MEDLINE, Embase, Scopus, and Cochrane until August 20, 2023, focusing on AI for ECG-based VHD detection. The outcomes included pooled accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value. The pooled proportions were derived using a random-effects model with 95% confidence intervals (CIs). Study heterogeneity was evaluated with the I-squared statistic. Our analysis included 10 studies, involving ECG data from 713,537 patients. The AI algorithms mainly screened for aortic stenosis (n = 6), mitral regurgitation (n = 4), aortic regurgitation (n = 3), mitral stenosis (n = 1), mitral valve prolapse (n = 2), and tricuspid regurgitation (n = 1). A total of 9 studies used convolution neural network models, whereas 1 study combined the strengths of support vector machine logistic regression and multilayer perceptron for ECG interpretation. The collective AI models demonstrated a pooled accuracy of 81% (95% CI 73 to 89, I² = 92%), sensitivity was 83% (95% CI 77 to 88, I² = 86%), specificity was 72% (95% CI 68 to 75, I² = 52%), PPV was 13% (95% CI 7 to 19, I² = 90%), and negative predictive value was 99% (95% CI 97 to 99, I² = 50%). The subgroup analyses for aortic stenosis and mitral regurgitation detection yielded analogous outcomes. In conclusion, AI-driven ECG offers high accuracy in VHD screening. However, its low PPV indicates the need for a combined approach with clinical judgment, especially in primary care settings.
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Affiliation(s)
- Sahib Singh
- Department of Medicine, Sinai Hospital of Baltimore, Baltimore, Maryland
| | - Rahul Chaudhary
- Artificial Intelligence for Holistic Evaluation and Advancement of Cardiovascular Thrombosis (AI-HEART) Lab, Pittsburgh, Pennsylvania; Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania.
| | - Kevin P Bliden
- Department of Cardiology, Sinai Center of Thrombosis Research and Drug Development, Baltimore, Maryland
| | - Udaya S Tantry
- Department of Cardiology, Sinai Center of Thrombosis Research and Drug Development, Baltimore, Maryland
| | - Paul A Gurbel
- Department of Medicine, Sinai Hospital of Baltimore, Baltimore, Maryland; Department of Cardiology, Sinai Center of Thrombosis Research and Drug Development, Baltimore, Maryland
| | - Shyam Visweswaran
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Matthew E Harinstein
- Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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Di Costanzo A, Spaccarotella CAM, Esposito G, Indolfi C. An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review. J Clin Med 2024; 13:1033. [PMID: 38398346 PMCID: PMC10889404 DOI: 10.3390/jcm13041033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.
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Affiliation(s)
- Assunta Di Costanzo
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
| | - Carmen Anna Maria Spaccarotella
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Giovanni Esposito
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Ciro Indolfi
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
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Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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Crea F. The key role of thrombosis: focus on acute coronary syndrome, venous thrombo-embolism, and atrial fibrillation. Eur Heart J 2024; 45:1-4. [PMID: 38160711 DOI: 10.1093/eurheartj/ehad835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Affiliation(s)
- Filippo Crea
- Centre of Excellence of Cardiovascular Sciences, Gemelli Isola Hospital, Rome, Italy
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Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
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Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
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Sager P, Rusch A, Weber L, Breuss A, Appert S, Brenner R, Buser M, Ammann P, Rickli H, Maeder MT. ECG left ventricular hypertrophy in aortic stenosis: Relationship with cardiac structure, invasive hemodynamics, and long-term mortality. Clin Cardiol 2024; 47:e24155. [PMID: 37740622 PMCID: PMC10765998 DOI: 10.1002/clc.24155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/04/2023] [Accepted: 09/07/2023] [Indexed: 09/24/2023] Open
Abstract
BACKGROUND In aortic stenosis (AS), left ventricular hypertrophy (LVH) is the response to pressure overload and represents the substrate for a maladaptive cascade, the so-called AS-related cardiac damage. We hypothesized that in AS patients electrocardiogram (ECG) LVH not only predicts echocardiography LVH but also other noninvasive and invasive markers of cardiac damage and prognosis after aortic valve replacement (AVR). METHODS In 279 patients with severe AS undergoing ECG, echocardiography, and cardiac catheterization before AVR, the Sokolow-Lyon index, the Cornell product, the Romhilt-Estes score, and the Peguero-Lo Presti score were assessed. RESULTS The mean left ventricular mass index was 109 ± 34 g/m2 , and 131 (47%) patients had echocardiography LVH. The areas under the receiver operator characteristics curve (AUC) for the Sokolow-Lyon index, the Cornell product, the Romhilt-Estes score, and the Peguero-Lo Presti score for the prediction of echocardiography LVH were 0.59, 0.70, 0.63, and 0.65. The Peguero-Lo Presti score had the numerically greatest AUC for the prediction of left ventricular end-diastolic pressure >15 mmHg, mean pulmonary artery wedge pressure >15 mmHg, pulmonary vascular resistance >3 Wood units, mean right atrial pressure >14 mmHg, and stroke volume index <31 mL/m2 . After a median follow-up of 1365 (interquartile range: 931-1851) days after AVR only the Peguero-Lo Presti score was significantly associated with all-cause mortality [hazard ratio: 1.24 (95% confidence interval: 1.01-1.54); per 1 mV increase; p = .045]. CONCLUSIONS Among severe AS patients, the Peguero-Lo Presti score is associated with abnormalities in cardiac structure including LVH, invasive measures of cardiac damage, and long-term mortality after AVR.
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Affiliation(s)
- Patrizia Sager
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
- University of BaselBaselSwitzerland
| | - Andreas Rusch
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
- Department of CardiologyKantonsspital MünsterlingenMünsterlingenSwitzerland
| | - Lukas Weber
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
| | - Alexander Breuss
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
| | - Sharon Appert
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
| | - Roman Brenner
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
| | - Marc Buser
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
| | - Peter Ammann
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
| | - Hans Rickli
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
| | - Micha T. Maeder
- Department of CardiologyKantonsspital St. GallenSt. GallenSwitzerland
- University of BaselBaselSwitzerland
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Shiraga T, Makimoto H, Kohlmann B, Magnisali CE, Imai Y, Itani Y, Makimoto A, Schölzel F, Bejinariu A, Kelm M, Rana O. Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:9834. [PMID: 38139680 PMCID: PMC10748155 DOI: 10.3390/s23249834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.
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Affiliation(s)
| | - Hisaki Makimoto
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke-City 329-0498, Japan
| | - Benita Kohlmann
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Christofori-Eleni Magnisali
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Yoshie Imai
- Mitsubishi Electric Inc., Kamakura 247-0056, Japan
| | - Yusuke Itani
- Mitsubishi Electric Inc., Kamakura 247-0056, Japan
| | - Asuka Makimoto
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Fabian Schölzel
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Alexandru Bejinariu
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Malte Kelm
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- CARID—Cardiovascular Research Institute Düsseldorf, 40225 Düsseldorf, Germany
| | - Obaida Rana
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- CARID—Cardiovascular Research Institute Düsseldorf, 40225 Düsseldorf, Germany
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Yuan N, Duffy G, Dhruva SS, Oesterle A, Pellegrini CN, Theurer J, Vali M, Heidenreich PA, Keyhani S, Ouyang D. Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation. JAMA Cardiol 2023; 8:1131-1139. [PMID: 37851434 PMCID: PMC10585587 DOI: 10.1001/jamacardio.2023.3701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/31/2023] [Indexed: 10/19/2023]
Abstract
Importance Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases. Objective To determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population. Design, Setting, and Participants This prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm. Main Outcomes and Measures A convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center. Results A total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater. Conclusions and Relevance Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.
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Affiliation(s)
- Neal Yuan
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Grant Duffy
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sanket S. Dhruva
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Adam Oesterle
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Cara N. Pellegrini
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - John Theurer
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Marzieh Vali
- Department of Medicine, University of California, San Francisco
- Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Paul A. Heidenreich
- Division of Cardiology, Palo Alto Veterans Affairs Medical Center, Palo Alto, California
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Palo Alto, California
| | - Salomeh Keyhani
- Department of Medicine, University of California, San Francisco
- Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - David Ouyang
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
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Avula V, Wu KC, Carrick RT. Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review. JACC. ADVANCES 2023; 2:100686. [PMID: 38288263 PMCID: PMC10824530 DOI: 10.1016/j.jacadv.2023.100686] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 08/31/2023] [Accepted: 09/19/2023] [Indexed: 01/31/2024]
Abstract
BACKGROUND The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced computational techniques such as deep learning has dramatically expanded the breadth of clinical problems that can be addressed using ECG data, leading to increasing popularity of ECG deep-learning models aimed at predicting clinical endpoints. OBJECTIVES The purpose of this study was to define the current landscape of clinically relevant ECG deep-learning models and examine practices in the scientific reporting of these studies. METHODS We performed a systematic review of PubMed and EMBASE databases to identify clinically relevant ECG deep-learning models published through July 1, 2022. RESULTS We identified 44 manuscripts including 53 unique, clinically relevant ECG deep-learning models. The rate of publication of ECG deep-learning models is increasing rapidly. The most common clinical applications of ECG deep learning were identification of cardiomyopathy (14/53 [26%]), followed by arrhythmia detection (9/53 [17%]). Methodologic reporting varied; while 33/44 (75%) publications included model architecture diagrams, complete information required to reproduce these models was provided in only 10/44 (23%). Saliency analysis was performed in 20/44 (46%) of publications. Only 18/53 (34%) models were tested within external validation cohorts. Model code or resources allowing for model implementation by external groups were available for only 5/44 (11%) publications. CONCLUSIONS While ECG deep-learning models are increasingly clinically relevant, their reporting is highly variable, and few publications provide sufficient detail for methodologic reproduction or model validation by external groups. The field of ECG deep learning would benefit from adherence to a set of standardized scientific reporting guidelines.
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Affiliation(s)
- Vennela Avula
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Katherine C. Wu
- Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA
| | - Richard T. Carrick
- Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA
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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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Holste G, Oikonomou EK, Mortazavi BJ, Coppi A, Faridi KF, Miller EJ, Forrest JK, McNamara RL, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz HM, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. Eur Heart J 2023; 44:4592-4604. [PMID: 37611002 PMCID: PMC11004929 DOI: 10.1093/eurheartj/ehad456] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/21/2023] [Accepted: 07/11/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND AND AIMS Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND RESULTS In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. CONCLUSION This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.
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Affiliation(s)
- Gregory Holste
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Bobak J Mortazavi
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
| | - Andreas Coppi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
| | - Kamil F Faridi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - John K Forrest
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Robert L McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Lucila Ohno-Machado
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Neal Yuan
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Aakriti Gupta
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College St, New Haven, CT, USA
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Siontis KC, Suárez AB, Sehrawat O, Ackerman MJ, Attia ZI, Friedman PA, Noseworthy PA, Maanja M. Saliency maps provide insights into artificial intelligence-based electrocardiography models for detecting hypertrophic cardiomyopathy. J Electrocardiol 2023; 81:286-291. [PMID: 37599145 DOI: 10.1016/j.jelectrocard.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 06/13/2023] [Accepted: 07/04/2023] [Indexed: 08/22/2023]
Abstract
INTRODUCTION A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM. METHODS We derived a new one‑lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12‑lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One‑lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM. RESULTS The one‑lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12‑lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs. CONCLUSIONS Saliency maps of a one‑lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.
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Affiliation(s)
| | | | - Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Maren Maanja
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden.
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Dweck MR, Loganath K, Bing R, Treibel TA, McCann GP, Newby DE, Leipsic J, Fraccaro C, Paolisso P, Cosyns B, Habib G, Cavalcante J, Donal E, Lancellotti P, Clavel MA, Otto CM, Pibarot P. Multi-modality imaging in aortic stenosis: an EACVI clinical consensus document. Eur Heart J Cardiovasc Imaging 2023; 24:1430-1443. [PMID: 37395329 DOI: 10.1093/ehjci/jead153] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 06/16/2023] [Indexed: 07/04/2023] Open
Abstract
In this EACVI clinical scientific update, we will explore the current use of multi-modality imaging in the diagnosis, risk stratification, and follow-up of patients with aortic stenosis, with a particular focus on recent developments and future directions. Echocardiography is and will likely remain the key method of diagnosis and surveillance of aortic stenosis providing detailed assessments of valve haemodynamics and the cardiac remodelling response. Computed tomography (CT) is already widely used in the planning of transcutaneous aortic valve implantation. We anticipate its increased use as an anatomical adjudicator to clarify disease severity in patients with discordant echocardiographic measurements. CT calcium scoring is currently used for this purpose; however, contrast CT techniques are emerging that allow identification of both calcific and fibrotic valve thickening. Additionally, improved assessments of myocardial decompensation with echocardiography, cardiac magnetic resonance, and CT will become more commonplace in our routine assessment of aortic stenosis. Underpinning all of this will be widespread application of artificial intelligence. In combination, we believe this new era of multi-modality imaging in aortic stenosis will improve the diagnosis, follow-up, and timing of intervention in aortic stenosis as well as potentially accelerate the development of the novel pharmacological treatments required for this disease.
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Affiliation(s)
- Marc R Dweck
- Centre for Cardiovascular Science, University of Edinburgh, Chancellors Building, Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Krithika Loganath
- Centre for Cardiovascular Science, University of Edinburgh, Chancellors Building, Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Rong Bing
- Centre for Cardiovascular Science, University of Edinburgh, Chancellors Building, Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Thomas A Treibel
- Barts Heart Centre, Bart's Health NHS Trust, W Smithfield, EC1A 7BE, London, UK
- University College London Institute of Cardiovascular Science, 62 Huntley St, WC1E 6DD, London, UK
| | - Gerry P McCann
- Department of Cardiovascular Sciences, University of Leicester, University Rd, Leicester LE1 7RH, UK
- The NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK
| | - David E Newby
- Centre for Cardiovascular Science, University of Edinburgh, Chancellors Building, Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Jonathon Leipsic
- Centre for Cardiovascular Innovation, St Paul's and Vancouver General Hospital, 1081 Burrard St Room 166, Vancouver, British Columbia V6Z 1Y6, Canada
| | - Chiara Fraccaro
- Department of Cardiac, Thoracic and Vascular Science and Public Health, Via Giustiniani, 2 - 35128, Padua, Italy
| | - Pasquale Paolisso
- Cardiovascular Center Aalst, OLV Clinic, Moorselbaan 164, 9300 Aalst, Belgium
- Department of Advanced Biomedical Sciences, University of Naples, Federico II, 80125 Naples, Italy
| | - Bernard Cosyns
- Department of Cardiology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Jette, Belgium
| | - Gilbert Habib
- Cardiology Department, Hôpital La Timone, 264 Rue Saint-Pierre, 13005 Marseille, France
| | - João Cavalcante
- Allina Health Minneapolis Heart Institute, Abbott Northwestern Hospital, 800 E 28th St, Minneapolis, MN 55407, USA
| | - Erwan Donal
- Cardiology and CIC, Université Rennes, 2 Rue Henri Le Guilloux, 35033 Rennes, France
| | - Patrizio Lancellotti
- GIGA Cardiovascular Sciences, Department of Cardiology, University of Liège Hospital, CHU Sart Tilman, Liège, Belgium
- Gruppo Villa Maria Care and Research, Corso Giuseppe Garibaldi, 11, 48022 Lugo RA, Italy
| | - Marie-Annick Clavel
- Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, 2725 Ch Ste-Foy, Québec, QC G1V 4G5, Canada
- Faculté de Médecine-Département de Médecine, Université Laval, Ferdinand Vandry Pavillon, 1050 Av. de la Médecine, Québec City, Quebec G1V 0A6, Canada
| | - Catherine M Otto
- Division of Cardiology, Department of Medicine, University of Washington School of Medicine, 4333 Brooklyn Ave NE Box 359458, Seattle, WA 98195-9458, USA
| | - Phillipe Pibarot
- Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, 2725 Ch Ste-Foy, Québec, QC G1V 4G5, Canada
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Di Costanzo A, Indolfi C, Franzone A, Esposito G, Spaccarotella CAM. Lp(a) in the Pathogenesis of Aortic Stenosis and Approach to Therapy with Antisense Oligonucleotides or Short Interfering RNA. Int J Mol Sci 2023; 24:14939. [PMID: 37834387 PMCID: PMC10573862 DOI: 10.3390/ijms241914939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/28/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
To date, no medical therapy can slow the progression of aortic stenosis. Fibrocalcific stenosis is the most frequent form in the general population and affects about 6% of the elderly population. Over the years, diagnosis has evolved thanks to echocardiography and computed tomography assessments. The application of artificial intelligence to electrocardiography could further implement early diagnosis. Patients with severe aortic stenosis, especially symptomatic patients, have valve repair as their only therapeutic option by surgical or percutaneous technique (TAVI). The discovery that the pathogenetic mechanism of aortic stenosis is similar to the atherosclerosis process has made it possible to evaluate the hypothesis of medical therapy for aortic stenosis. Several drugs have been tested to reduce low-density lipoprotein (LDL) and lipoprotein(a) (Lp(a)) levels, inflammation, and calcification. The Proprotein Convertase Subtilisin/Kexin type 9 inhibitors (PCSK9-i) could decrease the progression of aortic stenosis and the requirement for valve implantation. Great interest is related to circulating Lp(a) levels as causally linked to degenerative aortic stenosis. New therapies with ASO (antisense oligonucleotides) and siRNA (small interfering RNA) are currently being tested. Olpasiran and pelacarsen reduce circulating Lp(a) levels by 85-90%. Phase 3 studies are underway to evaluate the effect of these drugs on cardiovascular events (cardiovascular death, non-fatal myocardial injury, and non-fatal stroke) in patients with elevated Lp(a) and CVD (cardiovascular diseases). For instance, if a reduction in Lp(a) levels is associated with aortic stenosis prevention or progression, further prospective clinical trials are warranted to confirm this observation in this high-risk population.
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Affiliation(s)
- Assunta Di Costanzo
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy;
| | - Ciro Indolfi
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy;
| | - Anna Franzone
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (A.F.); (G.E.); (C.A.M.S.)
| | - Giovanni Esposito
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (A.F.); (G.E.); (C.A.M.S.)
| | - Carmen Anna Maria Spaccarotella
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy; (A.F.); (G.E.); (C.A.M.S.)
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Harmon DM, Mangold K, Baez Suarez A, Scott CG, Murphree DH, Malik A, Attia ZI, Lopez-Jimenez F, Friedman PA, Dispenzieri A, Grogan M. Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis. JACC. ADVANCES 2023; 2:100612. [PMID: 38638999 PMCID: PMC11025724 DOI: 10.1016/j.jacadv.2023.100612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/29/2023] [Accepted: 07/07/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND We have previously applied artificial intelligence (AI) to an electrocardiogram (ECG) to detect cardiac amyloidosis (CA). OBJECTIVES In this validation study, the authors observe the postdevelopment performance of the AI-enhanced ECG to detect CA with respect to multiple potential confounders. METHODS Amyloid patients diagnosed after algorithm development (June 2019-January 2022) with a 12-lead ECG were identified (n = 440) and were required to have CA. A 15:1 age- and sex-matched control group was identified (n = 6,600). Area under the receiver operating characteristic (AUC) was determined for the cohort and subgroups. RESULTS The average age was 70.4 ± 10.3 years, 25.0% were female, and most patients were White (91.3%). In this validation, the AI-ECG for amyloidosis had an AUC of 0.84 (95% CI: 0.82-0.86) for the overall cohort and between amyloid subtypes, which is a slight decrease from the original study (AUC 0.91). White, Black, and patients of "other" races had similar algorithm performance (AUC >0.81) with a decreased performance for Hispanic patients (AUC 0.66). Algorithm performance shift over time was not observed. Low ECG voltage and infarct pattern exhibited high AUC (>0.90), while left ventricular hypertrophy and left bundle branch block demonstrated lesser performance (AUC 0.75 and 0.76, respectively). CONCLUSIONS The AI-ECG for the detection of CA maintained an overall strong performance with respect to patient age, sex, race, and amyloid subtype. Lower performance was noted in left bundle branch block, left ventricular hypertrophy, and ethnically diverse populations emphasizing the need for subgroup-specific validation efforts.
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Affiliation(s)
- David M. Harmon
- Department of Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, Minnesota, USA
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Kathryn Mangold
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Abraham Baez Suarez
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Christopher G. Scott
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Dennis H. Murphree
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Awais Malik
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Zachi I. Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Angela Dispenzieri
- Division of Hematology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Martha Grogan
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
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Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol 2023; 22:259. [PMID: 37749579 PMCID: PMC10521578 DOI: 10.1186/s12933-023-01985-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/07/2023] [Indexed: 09/27/2023] Open
Abstract
Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St, 6th floor, New Haven, CT, 06510, USA.
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Lee YH, Hsieh MT, Chang CC, Tsai YL, Chou RH, Lu HHS, Huang PH. Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm. Atherosclerosis 2023; 381:117238. [PMID: 37607462 DOI: 10.1016/j.atherosclerosis.2023.117238] [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: 10/10/2022] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023]
Abstract
BACKGROUND AND AIMS To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, traditional cardiovascular risk factors (CVRFs), and a 12-lead electrocardiogram (ECG). However, currently, there are no established criteria for interpreting an ECG to diagnose CAD. Therefore, we sought to develop an artificial intelligence (AI)-enabled ECG model to assist in identifying patients with CAD. METHODS In this study, we included patients who underwent coronary angiography (CAG) at a single center between 2017 and 2019. Preprocedural 12-lead ECG performed within 24 h was obtained. Obstructive CAD was defined as ≥ 50% diameter stenosis. Using age, gender and ECG data, we developed stacking models using both deep learning and machine learning. Then we compared the performance of our models with CVRFs and with cardiologists' ECG interpretation. Additionally, we validated our model on an external cohort from a different hospital. RESULTS We included 4951 patients with a mean age of 65.5 ± 12.5 years, of whom 67.0% were men. Based on CAG, obstructive CAD was confirmed in 2637 patients (53.2%). Our best AI model demonstrated comparable performance to CVRFs in predicting CAD, with an AUC of 0.70 (95% CI: 0.66-0.75) compared to 0.71 (95% CI: 0.66-0.76). The sensitivity and specificity of the AI model were 0.75 and 0.54, respectively, while those of CVRFs were 0.67 and 0.63. Compared to cardiologists, the AI model showed better performance with an F1 score of 0.68 vs 0.41. The external validation showed generally consistent diagnostic findings, although there was a slightly lower level of agreement observed in the external cohort. Incorporating ECG and CVRFs improved the AUC to 0.72. CONCLUSIONS Our study suggests that an AI-enabled ECG model can assist in identifying patients with obstructive CAD, with diagnostic performance similar to that of the traditional approach based on CVRFs. This model could serve as a useful clinical tool in an outpatient setting to identify patients who require further diagnostic tests.
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Affiliation(s)
- Yin-Hao Lee
- Division of Cardiology, Department of Medicine, Taipei City Hospital, Yang Ming Branch, Taipei, Taiwan; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ming-Tsung Hsieh
- Institute of Statistics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Chin Chang
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Lin Tsai
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ruey-Hsing Chou
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Henry Hong-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Po-Hsun Huang
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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