1
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Sulaiman SA, Saeed AE, Khatib ANA, Yamin S, Mohammed HF, Rumman OMA, Abida HA, Jain H, Goyal A. Mavacamten in hypertrophic obstructive cardiomyopathy: Prospects for AI integration and mitigating healthcare disparities. Curr Probl Cardiol 2024; 49:102786. [PMID: 39122099 DOI: 10.1016/j.cpcardiol.2024.102786] [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: 08/02/2024] [Accepted: 08/07/2024] [Indexed: 08/12/2024]
Abstract
Hypertrophic obstructive cardiomyopathy (HOCM) is an autosomal dominant condition that still remains significantly under-diagnosed worldwide. Early detection through clinical evaluation, imaging, and familial history is crucial to prevent severe complications such as heart failure and sudden cardiac death. While cuddsnt management strategies primarily offer symptomatic relief through pharmacotherapy or invasive procedures, their effectiveness and accessibility are limited, revealing substantial gaps in care. The emergence of Mavacamten, a recently FDA-approved drug, could potentially revolutionize HOCM management as it addresses the underlying pathophysiology by inhibiting cardiac myosin ATPase, showing promise in reducing obstruction and improving cardiac function. Our review aims to assess mavacamten's efficacy, emphasizing the pivotal role of genetic testing in identifying at-risk individuals and guiding precise diagnoses for personalized treatments. Additionally, we aim to highlight disparities in access to advanced diagnostics and therapies, particularly affecting underserved populations globally and within communities, as well as explore the potential of artificial intelligence (AI) in enhancing early detection and monitoring treatment responses in HOCM. This review thus offers valuable insights to inform future research directions and clinical practices aimed at optimizing outcomes for individuals with HOCM.
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Affiliation(s)
| | - Ahmad E Saeed
- School of Medicine, University of Jordan, Amman, Jordan.
| | | | - Saif Yamin
- School of Medicine, University of Jordan, Amman, Jordan.
| | | | | | | | - Hritvik Jain
- Department of Internal Medicine, All India Institute of Medical Sciences (AIIMS)-Jodhpur, Jodhpur, Rajasthan, India.
| | - Aman Goyal
- Department of Internal Medicine, Seth GS Medical College and KEM Hospital, Mumbai, India.
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2
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Gill R, Siddiqui A, Yee B, DiCaro MV, Houshmand N, Tak T. Advancements in the Diagnosis and Treatment of Hypertrophic Cardiomyopathy: A Comprehensive Review. J Cardiovasc Dev Dis 2024; 11:290. [PMID: 39330348 PMCID: PMC11431942 DOI: 10.3390/jcdd11090290] [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: 08/09/2024] [Revised: 09/06/2024] [Accepted: 09/16/2024] [Indexed: 09/28/2024] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is characterized by excessive growth of myocardial tissue, most commonly due to genetic mutations in sarcomere proteins. This can lead to complications such as heart failure, mitral regurgitation, syncope, arrhythmias, sudden cardiac death, and myocardial ischemia. While we have come a long way in our understanding of the pathophysiology, genetics, and epidemiology of HCM, the past 10 years have seen significant advancements in diagnosis and treatment. As the body of evidence on hypertrophic cardiomyopathy continues to grow, a comprehensive review of the current literature is an invaluable resource in organizing this knowledge. By doing so, the vast progress that has been made thus far will be widely available to all experts in the field. This review provides a comprehensive analysis of the scientific literature, exploring both well-established and cutting-edge diagnostic and therapeutic options. It also presents a unique perspective by incorporating topics such as exercise testing, genetic testing, radiofrequency ablation, risk stratification, and symptomatic management in non-obstructive HCM. Lastly, this review highlights areas where current and future research is at the forefront of innovation in hypertrophic cardiomyopathy.
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Affiliation(s)
- Randeep Gill
- Department of Internal Medicine, Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV 89102, USA
| | - Arsalan Siddiqui
- Department of Internal Medicine, Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV 89102, USA
| | - Brianna Yee
- Department of Internal Medicine, Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV 89102, USA
| | - Michael V DiCaro
- Department of Internal Medicine, Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV 89102, USA
| | - Nazanin Houshmand
- Department of Internal Medicine, Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV 89102, USA
| | - Tahir Tak
- Department of Internal Medicine, Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV 89102, USA
- VA Southern Nevada Healthcare System, 6900 N. Pecos Road, North Las Vegas, NV 89086, USA
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3
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Park H, Kwon OS, Shim J, Kim D, Park JW, Kim YG, Yu HT, Kim TH, Uhm JS, Choi JI, Joung B, Lee MH, Pak HN. Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation. NPJ Digit Med 2024; 7:234. [PMID: 39237703 PMCID: PMC11377779 DOI: 10.1038/s41746-024-01234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 08/22/2024] [Indexed: 09/07/2024] Open
Abstract
The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.
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Affiliation(s)
- Hanjin Park
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Oh-Seok Kwon
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Jaemin Shim
- Division of Cardiology, Department of Internal Medicine, Korea University Medical Center, Seoul, Republic of Korea.
| | - Daehoon Kim
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Je-Wook Park
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Yun-Gi Kim
- Division of Cardiology, Department of Internal Medicine, Korea University Medical Center, Seoul, Republic of Korea
| | - Hee Tae Yu
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Tae-Hoon Kim
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Jae-Sun Uhm
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Jong-Il Choi
- Division of Cardiology, Department of Internal Medicine, Korea University Medical Center, Seoul, Republic of Korea
| | - Boyoung Joung
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Moon-Hyoung Lee
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Hui-Nam Pak
- Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea.
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4
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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 2024:10.1038/s41591-024-03243-9. [PMID: 39223284 DOI: 10.1038/s41591-024-03243-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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5
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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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Udompap P, Liu K, Attia IZ, Canning RE, Benson JT, Therneau TM, Noseworthy PA, Friedman PA, Rattan P, Ahn JC, Simonetto DA, Shah VH, Kamath PS, Allen AM. Performance of AI-Enabled Electrocardiogram in the Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00777-8. [PMID: 39209186 DOI: 10.1016/j.cgh.2024.08.009] [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: 01/16/2024] [Revised: 07/30/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND AIMS Accessible noninvasive screening tools for metabolic dysfunction-associated steatotic liver disease (MASLD) are needed. We aim to explore the performance of a deep learning-based artificial intelligence (AI) model in distinguishing the presence of MASLD using 12-lead electrocardiogram (ECG). METHODS This is a retrospective study of adults diagnosed with MASLD in Olmsted County, Minnesota, between 1996 and 2019. Both cases and controls had ECGs performed within 6 years before and 1 year after study entry. An AI-based ECG model using a convolutional neural network was trained, validated, and tested in 70%, 10%, and 20% of the cohort, respectively. External validation was performed in an independent cohort from Mayo Clinic Enterprise. The primary outcome was the performance of ECG to identify MASLD, alone or when added to clinical parameters. RESULTS A total of 3468 MASLD cases and 25,407 controls were identified. The AI-ECG model predicted the presence of MASLD with an area under the curve (AUC) of 0.69 (original cohort) and 0.62 (validation cohort). The performance was similar or superior to age- and sex-adjusted models using body mass index (AUC, 0.71), presence of diabetes, hypertension or hyperlipidemia (AUC, 0.68), or diabetes alone (AUC, 0.66). The model combining ECG, age, sex, body mass index, diabetes, and alanine aminotransferase had the highest AUC: 0.76 (original) and 0.72 (validation). CONCLUSIONS This is a proof-of-concept study that an AI-based ECG model can detect MASLD with a comparable or superior performance as compared with the models using a single clinical parameter but not superior to the combination of clinical parameters. ECG can serve as another screening tool for MASLD in the nonhepatology space.
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Affiliation(s)
- Prowpanga Udompap
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Kan Liu
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Itzhak Zachi Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Rachel E Canning
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Joanne T Benson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Puru Rattan
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Joseph C Ahn
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Patrick S Kamath
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Alina M Allen
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota.
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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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8
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Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics (Basel) 2024; 14:1839. [PMID: 39272624 PMCID: PMC11394310 DOI: 10.3390/diagnostics14171839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 08/17/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.
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Affiliation(s)
- Luiza Camelia Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Aurel Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Andreea Elena Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Daniel Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Mihaela Debita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Carmina Liana Musat
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
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9
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Carrick RT, Carruth ED, Gasperetti A, Murray B, Tichnell C, Gaine S, Sampognaro J, Muller SA, Asatryan B, Haggerty C, Thiemann D, Calkins H, James CA, Wu KC. Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning. Heart Rhythm 2024:S1547-5271(24)03149-7. [PMID: 39168295 DOI: 10.1016/j.hrthm.2024.08.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 07/11/2024] [Accepted: 08/10/2024] [Indexed: 08/23/2024]
Abstract
BACKGROUND Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease associated with life-threatening ventricular arrhythmias. Diagnosis of ARVC is based on the 2010 Task Force Criteria (TFC), application of which often requires clinical expertise at specialized centers. OBJECTIVE The purpose of this study was to develop and validate an electrocardiogram (ECG) deep learning (DL) tool for ARVC diagnosis. METHODS ECGs of patients referred for ARVC evaluation were used to develop (n = 551 [80.1%]) and test (n = 137 [19.9%]) an ECG-DL model for prediction of TFC-defined ARVC diagnosis. The ARVC ECG-DL model was externally validated in a cohort of patients with pathogenic or likely pathogenic (P/LP) ARVC gene variants identified through the Geisinger MyCode Community Health Initiative (N = 167). RESULTS Of 688 patients evaluated at Johns Hopkins Hospital (JHH) (57.3% male, mean age 40.2 years), 329 (47.8%) were diagnosed with ARVC. Although ARVC diagnosis made by referring cardiologist ECG interpretation was unreliable (c-statistic 0.53; confidence interval [CI] 0.52-0.53), ECG-DL discrimination in the hold-out testing cohort was excellent (0.87; 0.86-0.89) and compared favorably to that of ECG interpretation by an ARVC expert (0.85; 0.84-0.86). In the Geisinger cohort, prevalence of ARVC was lower (n = 17 [10.2%]), but ECG-DL-based identification of ARVC phenotype remained reliable (0.80; 0.77-0.83). Discrimination was further increased when ECG-DL predictions were combined with non-ECG-derived TFC in the JHH testing (c-statistic 0.940; 95% CI 0.933-0.948) and Geisinger validation (0.897; 95% CI 0.883-0.912) cohorts. CONCLUSION ECG-DL augments diagnosis of ARVC to the level of an ARVC expert and can differentiate true ARVC diagnosis from phenotype-mimics and at-risk family members/genotype-positive individuals.
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Affiliation(s)
- Richard T Carrick
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland.
| | - Eric D Carruth
- Department of Genomic Health, Geisinger Medical Center, Danville, Pennsylvania
| | - Alessio Gasperetti
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Brittney Murray
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Crystal Tichnell
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Sean Gaine
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - James Sampognaro
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Steven A Muller
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Babken Asatryan
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Chris Haggerty
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - David Thiemann
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Hugh Calkins
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Cynthia A James
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
| | - Katherine C Wu
- Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, Maryland
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10
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Wong CK, Lau YM, Lui HW, Chan WF, San WC, Zhou M, Cheng Y, Huang D, Lai WH, Lau YM, Siu CW. Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones. Heart 2024; 110:1074-1082. [PMID: 38768982 DOI: 10.1136/heartjnl-2023-323822] [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: 12/17/2023] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG machines do not permit installation of new algorithms. OBJECTIVE To develop a smartphone application that automatically extract ECG waveforms from photos and to convert them to voltage-time series for downstream analysis by a variety of diagnostic algorithms built by researchers. METHODS A novel approach of using objective detection and image segmentation models to automatically extract ECG waveforms from photos taken by clinicians was devised. Modular machine learning models were developed to sequentially perform waveform identification, gridline removal, and scale calibration. The extracted data were then analysed using a machine learning-based cardiac rhythm classifier. RESULTS Waveforms from 40 516 scanned and 444 photographed ECGs were automatically extracted. 12 828 of 13 258 (96.8%) scanned and 5399 of 5743 (94.0%) photographed waveforms were correctly cropped and labelled. 11 604 of 12 735 (91.1%) scanned and 5062 of 5752 (88.0%) photographed waveforms achieved successful voltage-time signal extraction after automatic gridline and background noise removal. In a proof-of-concept demonstration, an atrial fibrillation diagnostic algorithm achieved 91.3% sensitivity, 94.2% specificity, 95.6% positive predictive value, 88.6% negative predictive value and 93.4% F1 score, using photos of ECGs as input. CONCLUSION Object detection and image segmentation models allow automatic extraction of ECG signals from photos for downstream diagnostics. This novel pipeline circumvents the need for costly ECG hardware upgrades, thereby paving the way for large-scale implementation of machine learning-based diagnostic algorithms.
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Affiliation(s)
- Chun-Ka Wong
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yuk Ming Lau
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Hin Wai Lui
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wai Fung Chan
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wing Chun San
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mi Zhou
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yangyang Cheng
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Duo Huang
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wing Hon Lai
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yee Man Lau
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Chung Wah Siu
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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Ordine L, Canciello G, Borrelli F, Lombardi R, Di Napoli S, Polizzi R, Falcone C, Napolitano B, Moscano L, Spinelli A, Masciari E, Esposito G, Losi MA. Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management. Trends Cardiovasc Med 2024:S1050-1738(24)00075-6. [PMID: 39147002 DOI: 10.1016/j.tcm.2024.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/30/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artificial Intelligence (AI) and Machine Learning (ML) techniques hold promise in transforming the role of Electrocardiography (ECG) in HCM diagnosis, prognosis, and management. AI, including Deep Learning (DL), enables computers to learn patterns from data, allowing for the development of models capable of analyzing ECG signals. DL models, such as convolutional neural networks, have shown promise in accurately identifying HCM-related abnormalities in ECGs, surpassing traditional diagnostic methods. In diagnosing HCM, ML models have demonstrated high accuracy in distinguishing between HCM and other cardiac conditions, even in cases with normal ECG findings. Additionally, AI models have enhanced risk assessment by predicting arrhythmic events leading to sudden cardiac death and identifying patients at risk for atrial fibrillation and heart failure. These models incorporate clinical and imaging data, offering a comprehensive evaluation of patient risk profiles. Challenges remain, including the need for larger and more diverse datasets to improve model generalizability and address imbalances inherent in rare event prediction. Nevertheless, AI-driven approaches have the potential to revolutionize HCM management by providing timely and accurate diagnoses, prognoses, and personalized treatment strategies based on individual patient risk profiles. This review explores the current landscape of AI applications in ECG analysis for HCM, focusing on advancements in AI methodologies and their specific implementation in HCM care.
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Affiliation(s)
- Leopoldo Ordine
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Grazia Canciello
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Felice Borrelli
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Raffaella Lombardi
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Salvatore Di Napoli
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Roberto Polizzi
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Cristina Falcone
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Brigida Napolitano
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Lorenzo Moscano
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Alessandra Spinelli
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Elio Masciari
- Department of Electrical Engineering and Information Technologies, University Federico II, Naples, Italy
| | - Giovanni Esposito
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Maria-Angela Losi
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
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12
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Chinni BK, Manlhiot C. Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease. Can J Cardiol 2024:S0828-282X(24)00585-3. [PMID: 39097187 DOI: 10.1016/j.cjca.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/05/2024] Open
Abstract
Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.
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Affiliation(s)
- Bhargava K Chinni
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Research Institute, SickKids Hospital, Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada.
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13
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Carrick RT, Ahamed H, Sung E, Maron MS, Madias C, Avula V, Studley R, Bao C, Bokhari N, Quintana E, Rajesh-Kannan R, Maron BJ, Wu KC, Rowin EJ. Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach. Heart Rhythm 2024; 21:1390-1397. [PMID: 38280624 PMCID: PMC11272903 DOI: 10.1016/j.hrthm.2024.01.031] [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: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide. OBJECTIVE The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features. METHODS Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India). RESULTS ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%. CONCLUSION In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.
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Affiliation(s)
- Richard T Carrick
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland.
| | - Hisham Ahamed
- Amrita Institute of Medical Sciences and Research Centre, Amrita Hypertrophic Cardiomyopathy Center, Kochi, Kerala, India
| | - Eric Sung
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland
| | - Martin S Maron
- Lahey Hospital and Medical Center, Hypertrophic Cardiomyopathy Center, Burlington, Massachusetts
| | | | - Vennela Avula
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland
| | - Rachael Studley
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Chen Bao
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Nadia Bokhari
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Erick Quintana
- Tufts Medical Center, Cardiac Arrhythmia Center, Boston, Massachusetts
| | - Ramiah Rajesh-Kannan
- Amrita Institute of Medical Sciences and Research Centre, Amrita Hypertrophic Cardiomyopathy Center, Kochi, Kerala, India
| | - Barry J Maron
- Lahey Hospital and Medical Center, Hypertrophic Cardiomyopathy Center, Burlington, Massachusetts
| | - Katherine C Wu
- Johns Hopkins University School of Medicine, Heart and Vascular Institute, Baltimore, Maryland
| | - Ethan J Rowin
- Lahey Hospital and Medical Center, Hypertrophic Cardiomyopathy Center, Burlington, Massachusetts
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14
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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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15
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Faierstein K, Fiman M, Loutati R, Rubin N, Manor U, Am-Shalom A, Cohen-Shelly M, Blank N, Lotan D, Zhao Q, Schwammenthal E, Klempfner R, Zimlichman E, Raanani E, Maor E. Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality. J Am Soc Echocardiogr 2024; 37:725-735. [PMID: 38740271 DOI: 10.1016/j.echo.2024.04.017] [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: 03/04/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. METHODS The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. RESULTS The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001). CONCLUSIONS Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.
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Affiliation(s)
- Kobi Faierstein
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
| | | | - Ranel Loutati
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel
| | | | - Uri Manor
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | | | | | - Nimrod Blank
- Echocardiography Unit, Division of Cardiovascular Medicine, Baruch-Padeh Medical Center, Poria, Israel
| | - Dor Lotan
- Division of Cardiology, Department of Medicine, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, New York
| | - Qiong Zhao
- Inova Heart and Vascular Institute, Inova Fairfax Hospital, Falls Church, Virginia
| | - Ehud Schwammenthal
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Robert Klempfner
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Eyal Zimlichman
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ehud Raanani
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Elad Maor
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
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16
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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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17
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Lee SH, Jeon KL, Lee YJ, You SC, Lee SJ, Hong SJ, Ahn CM, Kim JS, Kim BK, Ko YG, Choi D, Hong MK. Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction. Ann Emerg Med 2024:S0196-0644(24)00327-5. [PMID: 39066765 DOI: 10.1016/j.annemergmed.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/14/2024] [Accepted: 06/03/2024] [Indexed: 07/30/2024]
Abstract
STUDY OBJECTIVE Although the importance of primary percutaneous coronary intervention has been emphasized for ST-segment elevation myocardial infarction (STEMI), the appropriateness of the cardiac catheterization laboratory activation remains suboptimal. This study aimed to develop a precise artificial intelligence (AI) model for the diagnosis of STEMI and accurate cardiac catheterization laboratory activation. METHODS We used electrocardiography (ECG) waveform data from a prospective percutaneous coronary intervention registry in Korea in this study. Two independent board-certified cardiologists established a criterion standard (STEMI or Not STEMI) for each ECG based on corresponding coronary angiography data. We developed a deep ensemble model by combining 5 convolutional neural networks. In addition, we performed clinical validation based on a symptom-based ECG data set, comparisons with clinical physicians, and external validation. RESULTS We used 18,697 ECGs for the model development data set, and 1,745 (9.3%) were STEMI. The AI model achieved an accuracy of 92.1%, sensitivity of 95.4%, and specificity of 91.8 %. The performances of the AI model were well balanced and outstanding in the clinical validation, comparison with clinical physicians, and the external validation. CONCLUSION The deep ensemble AI model showed a well-balanced and outstanding performance. As visualized with gradient-weighted class activation mapping, the AI model has a reasonable explainability. Further studies with prospective validation regarding clinical benefit in a real-world setting should be warranted.
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Affiliation(s)
- Sang-Hyup Lee
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyu Lee Jeon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea; Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea
| | - Yong-Joon Lee
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea; Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea.
| | - Seung-Jun Lee
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung-Jin Hong
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chul-Min Ahn
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Jung-Sun Kim
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Byeong-Keuk Kim
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young-Guk Ko
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Donghoon Choi
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Myeong-Ki Hong
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
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18
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Tao Y, Zhang D, Tan C, Wang Y, Shi L, Chi H, Geng S, Ma Z, Hong S, Liu XP. An artificial intelligence-enabled electrocardiogram algorithm for the prediction of left atrial low-voltage areas in persistent atrial fibrillation. J Cardiovasc Electrophysiol 2024. [PMID: 39054663 DOI: 10.1111/jce.16373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/19/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVES We aimed to construct an artificial intelligence-enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low-voltage areas (LVAs) in patients with persistent atrial fibrillation. METHODS The study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12-lead ECGs obtained before the ablation procedures were performed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs. The DR-FLASH and APPLE clinical scores for LVA prediction were calculated. We used a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis to evaluate model performance. RESULTS The data obtained from the participants were split into training (n = 469), validation (n = 58), and test sets (n = 60). LVAs were detected in 53.7% of all participants. Using ECG alone, the deep learning algorithm achieved an area under the ROC curve (AUROC) of 0.752, outperforming both the DR-FLASH score (AUROC = 0.610) and the APPLE score (AUROC = 0.510). The random forest classification model, which integrated a probabilistic deep learning model and clinical features, showed a maximum AUROC of 0.759. Moreover, the ECG-based deep learning algorithm for predicting extensive LVAs achieved an AUROC of 0.775, with a sensitivity of 0.816 and a specificity of 0.896. The random forest classification model for predicting extensive LVAs achieved an AUROC of 0.897, with a sensitivity of 0.862, and a specificity of 0.935. CONCLUSION The deep learning model based exclusively on ECG data and the machine learning model that combined a probabilistic deep learning model and clinical features both predicted the presence of LVAs with a higher degree of accuracy than the DR-FLASH and the APPLE risk scores.
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Affiliation(s)
- Yirao Tao
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Deyun Zhang
- HeartVoice Medical Technology, Hefei, China
- HeartRhythm-HeartVoice Joint Laboratory, Beijing, China
| | - Chen Tan
- Department of Cardiology, Hebei Yanda Hospital, Hebei, Hebei Province, China
| | - Yanjiang Wang
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Liang Shi
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Hongjie Chi
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Shijia Geng
- HeartVoice Medical Technology, Hefei, China
- HeartRhythm-HeartVoice Joint Laboratory, Beijing, China
| | - Zhimin Ma
- Department of Cardiology, Heart Rhythm Cardiovascular Hospital, Shandong, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Health Science Center of Peking University, Institute of Medical Technology, Beijing, China
| | - Xing Peng Liu
- Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
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19
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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2024:S1109-9666(24)00158-1. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [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: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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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; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, 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, New Haven, CT, USA
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20
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Siontis KC, Wieczorek MA, Maanja M, Hodge DO, Kim HK, Lee HJ, Lee H, Lim J, Park CS, Ariga R, Raman B, Mahmod M, Watkins H, Neubauer S, Windecker S, Siontis GCM, Gersh BJ, Ackerman MJ, Attia ZI, Friedman PA, Noseworthy PA. Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:416-426. [PMID: 39081936 PMCID: PMC11284003 DOI: 10.1093/ehjdh/ztae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 08/02/2024]
Abstract
Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm's ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910-0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.
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Affiliation(s)
- Konstantinos C Siontis
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Mikolaj A Wieczorek
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA
| | - Maren Maanja
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
- Department of Clinical Physiology, Karolinska University Hospital, Karolinska Institutet, Eugeniavägen 3, Solna, Sweden
| | - David O Hodge
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA
| | - Hyung-Kwan Kim
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
- Division of Cardiology, Cardiovascular Center, Seoul National University Hospital, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Hyun-Jung Lee
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
- Division of Cardiology, Cardiovascular Center, Seoul National University Hospital, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Heesun Lee
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
- Healthcare System Gangnam Center, Seoul National University Hospital, 152 Tehran Street, Gangnam-gu, Seoul, Republic of Korea
| | - Jaehyun Lim
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
- Division of Cardiology, Cardiovascular Center, Seoul National University Hospital, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Chan Soon Park
- Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
- Division of Cardiology, Cardiovascular Center, Seoul National University Hospital, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Rina Ariga
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Masliza Mahmod
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Stephan Windecker
- Department of Cardiology, Bern University Hospital, University of Bern, Freiburgstrasse 20, 3010 Bern, Switzerland
| | - George C M Siontis
- Department of Cardiology, Bern University Hospital, University of Bern, Freiburgstrasse 20, 3010 Bern, Switzerland
| | - Bernard J Gersh
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Michael J Ackerman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
- Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Windland Smith Rice Genetic Heart Rhythm Clinic, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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21
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DuBrock HM, Wagner TE, Carlson K, Carpenter CL, Awasthi S, Attia ZI, Frantz RP, Friedman PA, Kapa S, Annis J, Brittain EL, Hemnes AR, Asirvatham SJ, Babu M, Prasad A, Yoo U, Barve R, Selej M, Agron P, Kogan E, Quinn D, Dunnmon P, Khan N, Soundararajan V. An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension. Eur Respir J 2024; 64:2400192. [PMID: 38936966 PMCID: PMC11269769 DOI: 10.1183/13993003.00192-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/19/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. METHODS The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.
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Affiliation(s)
- Hilary M DuBrock
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
- Co-first authors
| | - Tyler E Wagner
- nference, Cambridge, MA, USA
- Anumana, Cambridge, MA, USA
- Co-first authors
| | | | | | - Samir Awasthi
- nference, Cambridge, MA, USA
- Anumana, Cambridge, MA, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Robert P Frantz
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jeffrey Annis
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Evan L Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anna R Hemnes
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Melwin Babu
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | - Ashim Prasad
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | | | - Rakesh Barve
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | - Mona Selej
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Peter Agron
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Emily Kogan
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Deborah Quinn
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Preston Dunnmon
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Najat Khan
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
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22
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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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23
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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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24
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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:S0828-282X(24)00429-X. [PMID: 38838932 DOI: 10.1016/j.cjca.2024.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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25
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Matsuo T, Ochi Y, Kubo T, Baba Y, Miyagawa K, Noguchi T, Hirota T, Hamada T, Yamasaki N, Kitaoka H. Associations between electrocardiographic findings and echocardiographic profiles in patients with hypertrophic cardiomyopathy. J Cardiol 2024; 83:359-364. [PMID: 37541430 DOI: 10.1016/j.jjcc.2023.07.017] [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: 04/07/2023] [Revised: 07/25/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023]
Abstract
BACKGROUND The relationships between electrocardiography (ECG) findings and echocardiographic profiles in patients with hypertrophic cardiomyopathy (HCM) are not fully understood. METHODS One hundred forty patients (mean age: 62.9 ± 15.3 years, 96 men) with HCM were studied. We assessed the associations between ECG findings and echocardiographic findings including maximum left ventricular wall thickness, HCM subtypes and distribution of left ventricular hypertrophy (LVH): the LV was divided into basal, mid, and apical segments by dividing it into thirds along the long axis. RESULTS In ECG, LVH by voltage criteria, abnormal Q wave, negative T wave, and giant negative T wave (GNT) were observed in 74 (53 %), 30 (21 %), 132 (94 %), and 25 (18 %) of the patients, respectively. In two groups with and without an LVH pattern according to voltage criteria in ECG, there were no significant differences in maximum LV wall thickness, subtype of HCM, and distribution of LVH. Regarding an abnormal Q wave, the proportion of patients with LVH in the basal segment was significantly higher in patients with an abnormal Q wave than in patients without an abnormal Q wave (87 % vs 61 %, p = 0.008). An abnormal Q wave was not observed in patients with LVH confined to the apex. Patients with a GNT included patients with LVH located at only the apex (apical HCM), LVH from the mid segment to apex, and LVH from the base to apex. No GNT was found in patients with hypertrophy located in the upper region from the base to mid segment of the LV. CONCLUSIONS In patients with HCM, there was no significant correlation between the presence of LVH by voltage criteria in ECG and echocardiographic findings. An abnormal Q wave was associated with disproportionate hypertrophy of the basal wall and a GNT reflected the presence of LVH in the apical segment.
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Affiliation(s)
- Tomomi Matsuo
- Innovative Medicine, Kochi Medical School, Kochi University, Kochi, Japan
| | - Yuri Ochi
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
| | - Toru Kubo
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan.
| | - Yuichi Baba
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
| | - Kazuya Miyagawa
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
| | - Tatsuya Noguchi
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
| | - Takayoshi Hirota
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
| | - Tomoyuki Hamada
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
| | - Naohito Yamasaki
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
| | - Hiroaki Kitaoka
- Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan
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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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Nishii N, Baba K, Morooka K, Shirae H, Mizuno T, Masuda T, Ueoka A, Asada S, Miyamoto M, Ejiri K, Kawada S, Nakagawa K, Nakamura K, Morita H, Yuasa S. Artificial intelligence to detect noise events in remote monitoring data. J Arrhythm 2024; 40:560-577. [PMID: 38939795 PMCID: PMC11199815 DOI: 10.1002/joa3.13037] [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: 01/25/2024] [Revised: 03/21/2024] [Accepted: 03/30/2024] [Indexed: 06/29/2024] Open
Abstract
Background Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED. A human must analyze the intracardiac electrogram to accurately detect lead failure. However, the number of arrhythmic events is too large for human analysis. Artificial intelligence (AI) seems to be helpful in the early and accurate detection of lead failure before human analysis. Objective To test whether a neural network can be trained to precisely identify noise events in the intracardiac electrogram of RM data. Methods We analyzed 21 918 RM data consisting of 12 925 and 1884 Medtronic and Boston Scientific data, respectively. Among these, 153 and 52 Medtronic and Boston Scientific data, respectively, were diagnosed as noise events by human analysis. In Medtronic, 306 events, including 153 noise events and randomly selected 153 out of 12 692 nonnoise events, were analyzed in a five-fold cross-validation with a convolutional neural network. The Boston Scientific data were analyzed similarly. Results The precision rate, recall rate, F1 score, accuracy rate, and the area under the curve were 85.8 ± 4.0%, 91.6 ± 6.7%, 88.4 ± 2.0%, 88.0 ± 2.0%, and 0.958 ± 0.021 in Medtronic and 88.4 ± 12.8%, 81.0 ± 9.3%, 84.1 ± 8.3%, 84.2 ± 8.3% and 0.928 ± 0.041 in Boston Scientific. Five-fold cross-validation with a weighted loss function could increase the recall rate. Conclusions AI can accurately detect noise events. AI analysis may be helpful for detecting lead failure events early and accurately.
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Affiliation(s)
- Nobuhiro Nishii
- Department of Cardiovascular TherapeuticsOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Kensuke Baba
- Cyber‐Physical Engineering Informatics Research CoreOkayama UniversityOkayamaJapan
| | - Ken'ichi Morooka
- Division of Industrial Innovation Sciences, Graduate School of Natural Science and TechnologyOkayama UniversityOkayamaJapan
| | - Haruto Shirae
- Division of Industrial Innovation Sciences, Graduate School of Natural Science and TechnologyOkayama UniversityOkayamaJapan
| | - Tomofumi Mizuno
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Takuro Masuda
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Akira Ueoka
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Saori Asada
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Masakazu Miyamoto
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Kentaro Ejiri
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Satoshi Kawada
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Koji Nakagawa
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Kazufumi Nakamura
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Hiroshi Morita
- Department of Cardiovascular TherapeuticsOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
| | - Shinsuke Yuasa
- Department of Cardiovascular MedicineOkayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesOkayamaJapan
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Hirota N, Suzuki S, Motogi J, Umemoto T, Nakai H, Matsuzawa W, Takayanagi T, Hyodo A, Satoh K, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Otsuka T, Uejima T, Oikawa Y, Hori T, Matsuhama M, Iida M, Yajima J, Yamashita T. Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting. Heart Vessels 2024; 39:524-538. [PMID: 38553520 DOI: 10.1007/s00380-024-02367-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/24/2024] [Indexed: 05/05/2024]
Abstract
The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010-2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ''basic diagnosis'' model (total disease label) and a ''comprehensive diagnosis'' model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ''comprehensive diagnosis'' model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.
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Affiliation(s)
- Naomi Hirota
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan.
| | - Shinya Suzuki
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | | | | | - Hiroshi Nakai
- Information System Division, The Cardiovascular Institute, Tokyo, Japan
| | | | | | | | | | - Takuto Arita
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Naoharu Yagi
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Mikio Kishi
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Hiroaki Semba
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Hiroto Kano
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Shunsuke Matsuno
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Yuko Kato
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Takayuki Otsuka
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Tokuhisa Uejima
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Yuji Oikawa
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Takayuki Hori
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Minoru Matsuhama
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Mitsuru Iida
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Junji Yajima
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Takeshi Yamashita
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
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29
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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30
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Cha YM, Attia IZ, Metzger C, Lopez-Jimenez F, Tan NY, Cruz J, Upadhyay GA, Mullane S, Harrell C, Kinar Y, Sedelnikov I, Lerman A, Friedman PA, Asirvatham SJ. Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators. Heart Rhythm 2024:S1547-5271(24)02634-1. [PMID: 38797305 DOI: 10.1016/j.hrthm.2024.05.040] [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: 01/30/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable. OBJECTIVE The study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs. METHODS The study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed. RESULTS Of 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79-0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events. CONCLUSION In this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.
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Affiliation(s)
- Yong-Mei Cha
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
| | - Itzhak Zachi Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | - Nicholas Y Tan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Jessica Cruz
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Gaurav A Upadhyay
- Department of Cardiology, The University of Chicago Medicine, Chicago, Illinois
| | | | | | | | | | - Amir Lerman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
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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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Mangold KE, Carter RE, Siontis KC, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Friedman PA, Attia ZI. Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:314-323. [PMID: 38774362 PMCID: PMC11104462 DOI: 10.1093/ehjdh/ztae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 05/24/2024]
Abstract
Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.
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Affiliation(s)
- Kathryn E Mangold
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | | | - Peter A Noseworthy
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | | | - Samuel J Asirvatham
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
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Crean AM. Scanning the Imaging Horizon for Hypertrophic Cardiomyopathy. Can J Cardiol 2024; 40:899-906. [PMID: 38467329 DOI: 10.1016/j.cjca.2024.02.030] [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: 01/30/2024] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/13/2024] Open
Abstract
In this article some of the recent advances in the use of noninvasive imaging applied to patients with hypertrophic cardiomyopathy (HCM) are discussed. Echocardiography and cardiac computed tomography are briefly discussed with respect to their power to detect apical aneurysmal disease. Echocardiographic phenotype-genotype correlations and the use of echocardiography to characterize myocardial work are reviewed. Positron emission tomography is reviewed in the context of ischemia imaging and also in the context of the use of a new tracer that might allow for recognition of early activation of the fibrosis pathway. Next, the technical capabilities of cardiovascular magnetic resonance to measure myocardial perfusion, oxygenation, and disarray are discussed as they apply to HCM. The application of radiomics to improve prediction of sudden cardiac death is touched upon. Finally, a deep learning approach to the recognition of HCM vs phenocopies is presented as a potential future diagnostic aid in the not-too-distant future.
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Affiliation(s)
- Andrew M Crean
- Manchester Heart Center, University of Manchester, Manchester, United Kingdom; Division of Cardiology, Ottawa Heart Institute, Ottawa, Ontario, Canada.
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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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Sigfstead S, Jiang R, Avram R, Davies B, Krahn AD, Cheung CC. Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes. Can J Cardiol 2024:S0828-282X(24)00335-0. [PMID: 38670456 DOI: 10.1016/j.cjca.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.
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Affiliation(s)
- Sophie Sigfstead
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - River Jiang
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Quebec, Canada; Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, Quebec, Canada
| | - Brianna Davies
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew D Krahn
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Christopher C Cheung
- Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
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Pieroni M, Namdar M, Olivotto I, Desnick RJ. Anderson-Fabry disease management: role of the cardiologist. Eur Heart J 2024; 45:1395-1409. [PMID: 38486361 DOI: 10.1093/eurheartj/ehae148] [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: 09/03/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 04/22/2024] Open
Abstract
Anderson-Fabry disease (AFD) is a lysosomal storage disorder characterized by glycolipid accumulation in cardiac cells, associated with a peculiar form of hypertrophic cardiomyopathy (HCM). Up to 1% of patients with a diagnosis of HCM indeed have AFD. With the availability of targeted therapies for sarcomeric HCM and its genocopies, a timely differential diagnosis is essential. Specifically, the therapeutic landscape for AFD is rapidly evolving and offers increasingly effective, disease-modifying treatment options. However, diagnosing AFD may be difficult, particularly in the non-classic phenotype with prominent or isolated cardiac involvement and no systemic red flags. For many AFD patients, the clinical journey from initial clinical manifestations to diagnosis and appropriate treatment remains challenging, due to late recognition or utter neglect. Consequently, late initiation of treatment results in an exacerbation of cardiac involvement, representing the main cause of morbidity and mortality, irrespective of gender. Optimal management of AFD patients requires a dedicated multidisciplinary team, in which the cardiologist plays a decisive role, ranging from the differential diagnosis to the prevention of complications and the evaluation of timing for disease-specific therapies. The present review aims to redefine the role of cardiologists across the main decision nodes in contemporary AFD clinical care and drug discovery.
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Affiliation(s)
- Maurizio Pieroni
- Cardiovascular Department, San Donato Hospital, Via Pietro Nenni 22, 52100 Arezzo, Italy
| | - Mehdi Namdar
- Cardiology Division, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi Hospital and Meyer Children's Hospital IRCCS, Florence, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Robert J Desnick
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Dadon Z, Rav Acha M, Orlev A, Carasso S, Glikson M, Gottlieb S, Alpert EA. Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction. Diagnostics (Basel) 2024; 14:767. [PMID: 38611680 PMCID: PMC11011323 DOI: 10.3390/diagnostics14070767] [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: 03/11/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
INTRODUCTION Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. AIM To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. METHODS Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. RESULTS The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. CONCLUSION AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
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Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Moshe Rav Acha
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shemy Carasso
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Emergency Medicine, Hadassah Medical Center—Ein Kerem, Jerusalem 9112001, Israel
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Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, Prabhu MA, Hegde A, Salvi M, Yeong CH, Barua PD, Molinari F, Acharya UR. Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023). Comput Biol Med 2024; 172:108207. [PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207] [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: 12/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, 576104, India
| | - M Maithri
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mukund A Prabhu
- Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Manipal Hospitals, Bengaluru, Karnataka, 560102, India
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
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Cai C, Imai T, Hasumi E, Fujiu K. One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108097. [PMID: 38428250 DOI: 10.1016/j.cmpb.2024.108097] [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: 09/26/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND AND OBJECTIVE Left ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN). METHODS Utilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis. RESULTS For LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model's performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807). CONCLUSIONS This study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.
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Affiliation(s)
- Chun Cai
- Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Japan
| | - Takeshi Imai
- Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Japan.
| | - Eriko Hasumi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Japan
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Japan
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Schiavone WA, Majdalany DS. The Value of the Electrocardiogram in Adult Congenital Heart Disease. J Pers Med 2024; 14:367. [PMID: 38672995 PMCID: PMC11051035 DOI: 10.3390/jpm14040367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
The electrocardiogram is the first test that is undertaken when evaluating a patient's heart. Diagnosing congenital heart disease in an adult (ACHD) can be facilitated by knowing the classical electrocardiographic (EKG) findings. These EKG findings often result from the congenital defect that prevents a part of the cardiac conduction system from occupying its normal anatomic position. When these classical EKG findings are not present, the clinician should consider alternate diagnoses. As the patient with congenital heart disease ages, with native anatomy or after surgical or device repair, the EKG can be used to assess the patient's status and to decide if and when treatment requires adjustment. This is because the electrocardiogram (EKG) can diagnose the hypertrophy or enlargement in a cardiac chamber that results from the congenital defect or anomaly and can diagnose an arrhythmia that might compromise an otherwise stable anatomy. While ACHD often involves intracardiac shunting, in many cases the abnormality only involves cardiac electrical conduction block or ventricular repolarization. These life-threatening diseases can be diagnosed with an EKG. This review will demonstrate and explain how the EKG can be used to diagnose and follow adults with congenital heart disease. When coupled with history and physical examination, the value of the EKG in ACHD will be apparent. A diagnosis can then be made or a differential diagnosis proposed, before an imaging study is ordered.
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Affiliation(s)
| | - David S. Majdalany
- Department of Cardiovascular Diseases, Mayo Clinic, Phoenix, AZ 85054, USA
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Mayourian J, La Cava WG, Vaid A, Nadkarni GN, Ghelani SJ, Mannix R, Geva T, Dionne A, Alexander ME, Duong SQ, Triedman JK. Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation 2024; 149:917-931. [PMID: 38314583 PMCID: PMC10948312 DOI: 10.1161/circulationaha.123.067750] [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: 10/26/2023] [Accepted: 12/20/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). RESULTS The training cohort comprised 92 377 ECG-echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation). CONCLUSIONS This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.
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Affiliation(s)
- Joshua Mayourian
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - William G. La Cava
- Computational Health Informatics Program, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Sunil J. Ghelani
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Rebekah Mannix
- Department of Medicine, Division of Emergency Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Tal Geva
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Audrey Dionne
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Mark E. Alexander
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Son Q. Duong
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John K. Triedman
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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Soh CH, de Sá AGC, Potter E, Halabi A, Ascher DB, Marwick TH. Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus. Cardiovasc Diabetol 2024; 23:91. [PMID: 38448993 PMCID: PMC10918872 DOI: 10.1186/s12933-024-02141-1] [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: 11/26/2023] [Accepted: 01/22/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Recent guidelines propose N-terminal pro-B-type natriuretic peptide (NT-proBNP) for recognition of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes mellitus (T2DM). Wavelet Transform based signal-processing transforms electrocardiogram (ECG) waveforms into an energy distribution waveform (ew)ECG, providing frequency and energy features that machine learning can use as additional inputs to improve the identification of SBHF. Accordingly, we sought whether machine learning model based on ewECG features was superior to NT-proBNP, as well as a conventional screening tool-the Atherosclerosis Risk in Communities (ARIC) HF risk score, in SBHF screening among patients with T2DM. METHODS Participants in two clinical trials of SBHF (defined as diastolic dysfunction [DD], reduced global longitudinal strain [GLS ≤ 18%] or LV hypertrophy [LVH]) in T2DM underwent 12-lead ECG with additional ewECG feature and echocardiography. Supervised machine learning was adopted to identify the optimal combination of ewECG extracted features for SBHF screening in 178 participants in one trial and tested in 97 participants in the other trial. The accuracy of the ewECG model in SBHF screening was compared with NT-proBNP and ARIC HF. RESULTS SBHF was identified in 128 (72%) participants in the training dataset (median 72 years, 41% female) and 64 (66%) in the validation dataset (median 70 years, 43% female). Fifteen ewECG features showed an area under the curve (AUC) of 0.81 (95% CI 0.787-0.794) in identifying SBHF, significantly better than both NT-proBNP (AUC 0.56, 95% CI 0.44-0.68, p < 0.001) and ARIC HF (AUC 0.67, 95%CI 0.56-0.79, p = 0.002). ewECG features were also led to robust models screening for DD (AUC 0.74, 95% CI 0.73-0.74), reduced GLS (AUC 0.76, 95% CI 0.73-0.74) and LVH (AUC 0.90, 95% CI 0.88-0.89). CONCLUSIONS Machine learning based modelling using additional ewECG extracted features are superior to NT-proBNP and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram to confirm diagnosis. Trial registration LEAVE-DM, ACTRN 12619001393145 and Vic-ELF, ACTRN 12617000116325.
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Affiliation(s)
- Cheng Hwee Soh
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Alex G C de Sá
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia
- Systems and Computational Biology, Bio21 Institute, Parkville, Australia
| | - Elizabeth Potter
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
| | - Amera Halabi
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
| | - David B Ascher
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia
- Systems and Computational Biology, Bio21 Institute, Parkville, Australia
| | - Thomas H Marwick
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia.
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia.
- Menzies Institute for Medical Research, Hobart, Australia.
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Palermi S, Vecchiato M, Saglietto A, Niederseer D, Oxborough D, Ortega-Martorell S, Olier I, Castelletti S, Baggish A, Maffessanti F, Biffi A, D'Andrea A, Zorzi A, Cavarretta E, D'Ascenzi F. Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart? Eur J Prev Cardiol 2024; 31:470-482. [PMID: 38198776 DOI: 10.1093/eurjpc/zwae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 01/01/2024] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.
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Affiliation(s)
- Stefano Palermi
- Public Health Department, University of Naples Federico II, via Pansini 5, 80131 Naples, Italy
| | - Marco Vecchiato
- Sports and Exercise Medicine Division, Department of Medicine, University of Padova, 35128 Padova, Italy
| | - Andrea Saglietto
- Division of Cardiology, Cardiovascular and Thoracic Department, 'Citta della Salute e della Scienza' Hospital, 10129 Turin, Italy
- Department of Medical Sciences, University of Turin, 10129 Turin, Italy
| | - David Niederseer
- Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - David Oxborough
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Ivan Olier
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Silvia Castelletti
- Cardiology Department, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Aaron Baggish
- Cardiovascular Performance Program, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Alessandro Biffi
- Med-Ex, Medicine & Exercise, Medical Partner Scuderia Ferrari, 00187 Rome, Italy
| | - Antonello D'Andrea
- Department of Cardiology, Umberto I Hospital, 84014 Nocera Inferiore, Italy
| | - Alessandro Zorzi
- Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
| | - Elena Cavarretta
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Naples, Italy
| | - Flavio D'Ascenzi
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
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Demolder A, Nauwynck M, De Pauw M, De Buyzere M, Duytschaever M, Timmermans F, De Pooter J. Prediction of certainty in artificial intelligence-enabled electrocardiography. J Electrocardiol 2024; 83:71-79. [PMID: 38367372 DOI: 10.1016/j.jelectrocard.2024.01.008] [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: 06/23/2023] [Revised: 12/31/2023] [Accepted: 01/28/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND The 12‑lead ECG provides an excellent substrate for artificial intelligence (AI) enabled prediction of various cardiovascular diseases. However, a measure of prediction certainty is lacking. OBJECTIVES To assess a novel approach for estimating certainty of AI-ECG predictions. METHODS Two convolutional neural networks (CNN) were developed to predict patient age and sex. Model 1 applied a 5 s sliding time-window, allowing multiple CNN predictions. The consistency of the output values, expressed as interquartile range (IQR), was used to estimate prediction certainty. Model 2 was trained on the full 10s ECG signal, resulting in a single CNN point prediction value. Performance was evaluated on an internal test set and externally validated on the PTB-XL dataset. RESULTS Both CNNs were trained on 269,979 standard 12‑lead ECGs (82,477 patients). Model 1 showed higher accuracy for both age and sex prediction (mean absolute error, MAE 6.9 ± 6.3 years vs. 7.7 ± 6.3 years and AUC 0.946 vs. 0.916, respectively, P < 0.001 for both). The IQR of multiple CNN output values allowed to differentiate between high and low accuracy of ECG based predictions (P < 0.001 for both). Among 10% of patients with narrowest IQR, sex prediction accuracy increased from 65.4% to 99.2%, and MAE of age prediction decreased from 9.7 to 4.1 years compared to the 10% with widest IQR. Accuracy and estimation of prediction certainty of model 1 remained true in the external validation dataset. CONCLUSIONS Sliding window-based approach improves ECG based prediction of age and sex and may aid in addressing the challenge of prediction certainty estimation.
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Affiliation(s)
- Anthony Demolder
- Department of Cardiology, Ghent University Hospital, Ghent, Belgium.
| | - Maxime Nauwynck
- Department of Cardiology, Ghent University Hospital, Ghent, Belgium
| | - Michel De Pauw
- Department of Cardiology, Ghent University Hospital, Ghent, Belgium
| | - Marc De Buyzere
- Department of Cardiology, Ghent University Hospital, Ghent, Belgium
| | | | - Frank Timmermans
- Department of Cardiology, Ghent University Hospital, Ghent, Belgium
| | - Jan De Pooter
- Department of Cardiology, Ghent University Hospital, Ghent, Belgium
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Muzammil MA, Javid S, Afridi AK, Siddineni R, Shahabi M, Haseeb M, Fariha FNU, Kumar S, Zaveri S, Nashwan AJ. Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J Electrocardiol 2024; 83:30-40. [PMID: 38301492 DOI: 10.1016/j.jelectrocard.2024.01.006] [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: 10/23/2023] [Revised: 12/28/2023] [Accepted: 01/22/2024] [Indexed: 02/03/2024]
Abstract
Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.
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Affiliation(s)
| | - Saman Javid
- CMH Kharian Medical College, Gujrat, Pakistan
| | | | | | | | | | - F N U Fariha
- Dow University of Health Sciences, Karachi, Pakistan
| | - Satesh Kumar
- Shaheed Mohtarma Benazir Bhutto Medical College, Karachi, Pakistan
| | - Sahil Zaveri
- Department of Medicine, SUNY Downstate Health Sciences University, New York, USA; Cardiovascular Research Program, VA New York Harbor Healthcare System, New York, USA
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Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [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] [Accepted: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
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Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
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Arabadjian M, Montgomery S, Pleasure M, Nicolas B, Collins M, Reuter M, Massera D, Shimbo D, Sherrid MV. Clinical course of adults with co-occurring hypertrophic cardiomyopathy and hypertension: A scoping review. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2024; 39:100367. [PMID: 38510995 PMCID: PMC10945972 DOI: 10.1016/j.ahjo.2024.100367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 03/22/2024]
Abstract
Introduction Hypertension affects approximately 50 % of patients with hypertrophic cardiomyopathy (HCM) but clinical course in adults with co-occurring HCM and hypertension is underexplored. Management may be challenging as routine anti-hypertensive medications may worsen obstructive HCM, the most common HCM phenotype. In this scoping review, we sought to synthesize the available literature related to clinical course and outcomes in adults with both conditions and to highlight knowledge gaps to inform future research directions. Methods We searched 5 electronic databases (PubMed, CINAHL, Scopus, Embase, Web of Science) to identify peer-reviewed articles, 2011-2023. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review (PRISMA-ScR) guideline. Results Eleven articles met eligibility. Adults with both conditions were older and had higher rates of obesity and diabetes than adults with HCM alone. Results related to functional class and arrhythmia were equivocal in cross-sectional studies. Only 1 article investigated changes in medical therapy among adults with both conditions. Hypertension was a predictor of worse functional class, but was not associated with all-cause mortality, heart failure-related mortality, or sudden-death. No data was found that related to common hypertension-related outcomes, including renal disease progression, nor patient-reported outcomes, including quality of life. Conclusions Our results highlight areas for future research to improve understanding of co-occurring HCM and hypertension. These include a need for tailored approaches to medical management to optimize outcomes, evaluation of symptom burden and quality of life, and investigation of hypertension-related outcomes, like renal disease and ischemic stroke, to inform cardiovascular risk mitigation strategies.
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Affiliation(s)
- Milla Arabadjian
- Center for Population and Health Services Research, Department of Foundations of Medicine, NYU Grossman Long Island School of Medicine, Mineola, NY, United States of America
| | - Sophie Montgomery
- NYU Grossman School of Medicine, New York, NY, United States of America
| | - Mitchell Pleasure
- NYU Grossman School of Medicine, New York, NY, United States of America
| | - Barnaby Nicolas
- Department of Foundations of Medicine, NYU Grossman Long Island School of Medicine, Mineola, NY, United States of America
| | - Maxine Collins
- School of Nursing University of Connecticut, Storrs, CT, United States of America
| | - Maria Reuter
- Hypertrophic Cardiomyopathy Program, Leon H. Charney Division of Cardiology, Department of Medicine, NYU Langone Health, New York, NY, United States of America
| | - Daniele Massera
- Hypertrophic Cardiomyopathy Program, Leon H. Charney Division of Cardiology, Department of Medicine, NYU Langone Health, New York, NY, United States of America
| | - Daichi Shimbo
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Mark V. Sherrid
- Hypertrophic Cardiomyopathy Program, Leon H. Charney Division of Cardiology, Department of Medicine, NYU Langone Health, New York, NY, United States of America
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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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Holmstrom L, Chugh H, Nakamura K, Bhanji Z, Seifer M, Uy-Evanado A, Reinier K, Ouyang D, Chugh SS. An ECG-based artificial intelligence model for assessment of sudden cardiac death risk. COMMUNICATIONS MEDICINE 2024; 4:17. [PMID: 38413711 PMCID: PMC10899257 DOI: 10.1038/s43856-024-00451-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 02/02/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Conventional ECG-based algorithms could contribute to sudden cardiac death (SCD) risk stratification but demonstrate moderate predictive capabilities. Deep learning (DL) models use the entire digital signal and could potentially improve predictive power. We aimed to train and validate a 12 lead ECG-based DL algorithm for SCD risk assessment. METHODS Out-of-hospital SCD cases were prospectively ascertained in the Portland, Oregon, metro area. A total of 1,827 pre- cardiac arrest 12 lead ECGs from 1,796 SCD cases were retrospectively collected and analyzed to develop an ECG-based DL model. External validation was performed in 714 ECGs from 714 SCD cases from Ventura County, CA. Two separate control group samples were obtained from 1342 ECGs taken from 1325 individuals of which at least 50% had established coronary artery disease. The DL model was compared with a previously validated conventional 6 variable ECG risk model. RESULTS The DL model achieves an AUROC of 0.889 (95% CI 0.861-0.917) for the detection of SCD cases vs. controls in the internal held-out test dataset, and is successfully validated in external SCD cases with an AUROC of 0.820 (0.794-0.847). The DL model performs significantly better than the conventional ECG model that achieves an AUROC of 0.712 (0.668-0.756) in the internal and 0.743 (0.711-0.775) in the external cohort. CONCLUSIONS An ECG-based DL model distinguishes SCD cases from controls with improved accuracy and performs better than a conventional ECG risk model. Further detailed investigation is warranted to evaluate how the DL model could contribute to improved SCD risk stratification.
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Affiliation(s)
- Lauri Holmstrom
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harpriya Chugh
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kotoka Nakamura
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ziana Bhanji
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Madison Seifer
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Audrey Uy-Evanado
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kyndaron Reinier
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sumeet S Chugh
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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