Assessment of disease status and treatment response with artificial Intelligence- Enhanced electrocardiography in obstructive hypertrophic cardiomyopathy
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Deep learning has been shown to be beneficial in detecting specific heart diseases from the 12-lead ECG, including aortic stenosis, low LVEF, low left ventricular hypertrophy and composites of valvular diseases.
“The application of deep learning, a subset of artificial intelligence (AI), has been shown to be beneficial in the detection of specific heart diseases from the 12-lead electrocardiogram (ECG). This includes conditions such as aortic stenosis, low left ventricular ejection fraction (LVEF) and low left ventricular hypertrophy, as well as composites of many valvular diseases3,17–21.”
From Detecting structural heart disease from electrocardiograms using AI (2025) · cites this paper as [21]
The Abstract reports accurate automated detection of hypertrophic cardiomyopathy using AI analysis of standard 12-lead ECGs, supporting the general point that AI can detect a heart disease from an ECG. It does not report detection of aortic stenosis, low LVEF, low left ventricular hypertrophy, or composites of valvular diseases. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI-ECG can extract more physiologically and pathophysiologically relevant information related to obstructive HCM from ECGs than traditional manual interpretation methods.
“The study noted that AI-ECG could extract more physiologically and pathophysiologically relevant information related to obstructive HCM from ECGs, surpassing traditional manual interpretation methods.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [153]
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Cites
Ko, W.-Y., Siontis, K. C., Attia, Z. I., Carter, R. E., Kapa, S., Ommen, S. R., Demuth, S. J., Ackerman, M. J., Gersh, B. J., Arruda-Olson, A. M., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.12.030 · Cited by 404 · see citing works
Self-citationAutomated and interpretable patient ECG profiles for disease detection, tracking, and discovery
Tison, G. H., Zhang, J., Delling, F. N., & Deo, R. C. (2019) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.118.005289 · Cited by 208 · see citing works
Self-citationThe electrocardiogram in the diagnosis and management of patients with hypertrophic cardiomyopathy
Finocchiaro, G., Sheikh, N., Biagini, E., Papadakis, M., Maurizi, N., Sinagra, G., Pelliccia, A., Rapezzi, C., Sharma, S., & Olivotto, I. (2019) · Heart Rhythm · doi:10.1016/j.hrthm.2019.07.019 · Cited by 132 · see citing works
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Research works printed but not linked: 1
- [3]Heitner SB, Lester S, Wang A, Precision pharmacological treatment for obstructive hypertrophic cardiomyopathy with mavacamten: one-year results from PIONEER-OLE. Circulation 2019; 140: A13962.
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Sorted by citations
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347 · see citing works
“42 Detecting hypertrophic cardiomyopathy by AI/ML of the ECG can also guide strategies to improve outcomes.”
BackgroundSelf-citationTransforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 309 · see citing works
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
Mavacamten: a first-in-class myosin inhibitor for obstructive hypertrophic cardiomyopathy
D, E. B. M., Saberi, S., Abraham, T. P., Elliot, P. M., & Olivotto, I. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad637 · Cited by 161 · see citing works
“In an analysis using artificial intelligence (AI)-enhanced electrocardiography (AI-ECG) to monitor disease status, 44 mean HCM scores from two different AI-ECG algorithms decreased over time with mava-camten, suggesting improvement in ECG morphology.”
MethodCurrent and Future Use of Artificial Intelligence in Electrocardiography
Martínez‐Sellés, M., & Breysse, M. M. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10040175 · Cited by 150 · see citing works
“AI ECG has the potential to be a tool to monitor disease status, cardiac hemodynamics, and drug therapeutic response as seen in preliminary data from patients with obstructive hypertrophic cardiomyopathy [93], where the AI ECG score likely reflect changes in the raw ECG waveform detectable by AI ECGs that correlate wi…”
MethodDetecting structural heart disease from electrocardiograms using AI
Poterucha, T. J., Jing, L., Ricart, R. P., Adjei-Mosi, M., Finer, J., Hartzel, D., Kelsey, C., Long, A., Rocha, D., Ruhl, J. A., et al. (2025) · Nature · doi:10.1038/s41586-025-09227-0 · Cited by 116 · see citing works
“This includes conditions such as aortic stenosis, low left ventricular ejection fraction (LVEF) and low left ventricular hypertrophy, as well as composites of many valvular diseases 3,17–21 .”
Self-citationElectrocardiogram Detection of Pulmonary Hypertension Using Deep Learning
ARAS, M. A., ABREAU, S., MILLS, H., RADHAKRISHNAN, L., KLEIN, L., MANTRI, N., RUBIN, B., BARRIOS, J., CHEHOUD, C., KOGAN, E., et al. (2023) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2022.12.016 · Cited by 46 · see citing works
Self-citationSiontis, K. C., Wieczorek, M. A., Maanja, M., Hodge, D. O., Kim, H.-K., Lee, H.-J., Lee, H., Lim, J., Park, C. S., Ariga, R., et al. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae029 · Cited by 44 · see citing works
Self-citationAdopting artificial intelligence in cardiovascular medicine: a scoping review
Makimoto, H., & Kohro, T. (2023) · Hypertension Research · doi:10.1038/s41440-023-01469-7 · Cited by 36 · see citing works
Ottaviani, A., Mansour, D., Molinari, L. V., Galanti, K., Mantini, C., Khanji, M. Y., Chahal, C. A. A., Zimarino, M., Renda, G., Sciarra, L., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12175710 · Cited by 35 · see citing works
“[106] use of artificial intelligence-enhanced electrocardiogram (AI-ECG) readings was investigated for assessing disease status and treatment response in patients with oHCM.”
MethodPatient-Level Artificial Intelligence–Enhanced Electrocardiography in Hypertrophic Cardiomyopathy
Siontis, K. C., Abreau, S. A., Attia, Z. I., Barrios, J. P., Dewland, T. A., Agarwal, P., Balasubramanyam, A., Li, Y., Lester, S. J., Masri, A., et al. (2023) · JACC Advances · doi:10.1016/j.jacadv.2023.100582 · Cited by 25 · see citing works
“In this study, we demonstrated that 2 independently derived AI-ECG algorithms performed well to identify HCM.”
MethodInfluentialSelf-citationOrdine, L., Canciello, G., Borrelli, F., Lombardi, R., Napoli, S., Polizzi, R., Falcone, C., Napolitano, B., Moscano, L., Spinelli, A. M., et al. (2024) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2024.08.002 · Cited by 24 · see citing works
The Electrocardiogram at 100 Years: History and Future
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“Beyond drug discovery, AI models trained on electrocardiogram (ECG) data have shown promise in revealing the patient's response to CVD treatments, paving the way for more personalized and efficient therapeutic strategies (Tison et al., 2022).”
Desai, M. Y., Jadam, S., Abusafia, M., Rutkowski, K., Ospina, S., Gaballa, A., Sultana, S., Thamilarasan, M., Xu, B., & Popovic, Z. B. (2025) · JACC: Clinical Electrophysiology · doi:10.1016/j.jacep.2025.02.024 · Cited by 19 · see citing works
Dhingra, L. S., Sangha, V., Aminorroaya, A., Bryde, R., Gaballa, A., Ali, A. H., Mehra, N., Krumholz, H. M., Sen, S., Kramer, C. M., et al. (2025) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2024.11.028 · Cited by 18 · see citing works
Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective
Barrios, J. P., & Tison, G. H. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100869 · Cited by 19 · see citing works
Self-citationDeep learning augmented ECG analysis to identify biomarker-defined myocardial injury
Chaudhari, G., Mayfield, J. J., Barrios, J. P., Abreau, S. A., Avram, R., Olgin, J. E., & Tison, G. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-29989-9 · Cited by 16 · see citing works
“3% (19,283) never had a positive TnI value (≥ 0.”
BackgroundSelf-citationDicorato, M. M., Citarelli, G., Mangini, F., Alemanni, R., Albanese, M., Cicco, S., Greco, C. A., Forleo, C., Basile, P., Carella, M. C., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13051256 · Cited by 12 · see citing works
“Furthermore, applying AI to multimodal imaging is a promising way to assess disease progression and treatment response, facilitating timely interventions [147,148].”
Asatryan, B., Bleijendaal, H., & Wilde, A. A. M. (2023) · Heart Rhythm · doi:10.1016/j.hrthm.2023.07.001 · Cited by 12 · see citing works
Coleman, J. A., Doste, R., Beltrami, M., Coppini, R., Olivotto, I., Raman, B., & Bueno‐Orovio, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107829 · Cited by 11 · see citing works
AI derived ECG global longitudinal strain compared to echocardiographic measurements
Choi, H., Kim, J., Park, J., Park, J., Kim, H., Choi, H. J., Yoon, Y. E., Cho, G., Cho, Y., & Hwang, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-78268-8 · Cited by 10 · see citing works
“Moreover, AI-enabled ECG HCM scores were correlated with decreases in LV outflow tract gradients and N-terminal pro-B-natriuretic peptide levels over time in patients with obstructive HCM who were prescribed mavacamten 19 .”
Siontis, K. C., Ommen, S. R., & Geske, J. B. (2023) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2023.08.005 · Cited by 9 · see citing works
Self-citationEvaluation of mavacamten in patients with hypertrophic cardiomyopathy
Liao, H.-L., Liang, Y., & Liang, B. (2024) · Journal of Cardiovascular Medicine · doi:10.2459/jcm.0000000000001638 · Cited by 8 · see citing works
Attia, Z. I., & Friedman, P. A. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac733 · Cited by 8 · see citing works
Self-citationSuppah, M., Abdalla, H., Roehl, K. M., Farina, J., Arsanjani, R., Geske, J. B., Ommen, S. R., & Alsidawi, S. (2024) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2024.10.001 · Cited by 6 · see citing works
Guo, L., Ma, Z., Yang, W., Zhang, F., Shao, H., Liu, L., Gao, C., & Tao, L. (2023) · Global Heart · doi:10.5334/gh.1250 · Cited by 4 · see citing works
Suppah, M., Roehl, K. M., Lew, K., Arsanjani, R., Lester, S. J., Ommen, S. R., Geske, J. B., Siontis, K. C., Schaff, H. V., & Alsidawi, S. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.04.002 · Cited by 3 · see citing works
Self-citationAdvances in Cardiovascular Pharmacotherapy. I. Cardiac Myosin Inhibitors
Pagel, P. S., Hang, D., Freed, J. K., & Crystal, G. J. (2025) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2025.02.009 · Cited by 3 · see citing works
Advancement in AI-Based Devices for Monitoring Heart Health in Patients with Cardiac Conditions
Anand, N. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem32609 · Cited by 2 · see citing works
Sarwer, K., Lashari, S., Rafaqat, N., Maher, Raheem, A., Rehman, M. U., & Abbas, S. M. I. (2024) · The Egyptian Heart Journal · doi:10.1186/s43044-024-00587-y · Cited by 4 · see citing works
An Overview of Artificial Intelligence in Primary Care and Administrative Medicine
Morelli, V. (2025) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2025.07.008 · Cited by 1 · see citing works
Swain, W. H., Giudicessi, J. R., Geske, J. B., Newman, D. B., Bos, J. M., Karim, S., Noseworthy, P. A., Attia, Z. I., Friedman, P. A., Arruda-Olson, A. M., et al. (2025) · Heart Rhythm O2 · doi:10.1016/j.hroo.2025.08.040 · Cited by 1 · see citing works
Self-citationChoi, H., Kim, J., Park, J., Park, J., Kim, H.-K., Cho, G., Yoon, Y. E., Cho, G., Cho, Y., & Hwang, I. (2024) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(24)04350-x · Cited by 1 · see citing works
Carrick, R. T., & Rowin, E. J. (2024) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2024.08.003 · Cited by 1 · see citing works
Suppah, M., Abdullah, H. A., Badr, A., Roehl, K. M., Arsanjani, R., Sorajja, D., Geske, J. B., & Alsidawi, S. (2025) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2025.07.006
Alkhouli, M. A., & Al-Abcha, A. K. (2025) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2025.10.031
Güler, G. B., Güler, A., & Tanboga, I. H. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag120
Yang, Y., Bao, Y., Zhang, J., & Liu, Y. (2026) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm49021
“Several studies [47,95,96] have shown that AI has significant potential in the detection, prediction, and treatment of HCM-related myocardial fibrosis, and it is expected to provide more precise diagnoses and personalized treatment options in clinical practice.”
Reading between the waves: what does AI-ECG capture when estimating NT-proBNP?
Lee, D., Grittner, U., & Friebel, J. (2026) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcag141
Role of Polygenic Risk Scores and Artificial Intelligence
Rossi, M., Gandin, I., Barbati, G., Garoia, F., Ferro, M. D., Gigli, M., & Sinagra, G. (2026) · Springer eBooks · doi:10.1007/978-3-032-34334-5_13
Treatment options for hypertrophic obstructive cardiomyopathy in the era of myosin inhibitors
Nwuba, C., & Dungu, J. N. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1814860
“Further exploration revealed improvements in electrocardiogram (ECG) morphology and NT-proBNP levels (113).”
- Authors
- Tison, G. H., Siontis, K. C., Abreau, S., Attia, Z., Agarwal, P., Balasubramanyam, A., Li, Y., Sehnert, A. J., Edelberg, J. M., Friedman, P. A., Olgin, J. E., & Noseworthy, P. A.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Tison, Geoffrey H.
- ORCID
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 79
- Issue
- 10
- Pages
- 1032-1034
- ISSN
- 0735-1097 (ISSN-L)
- PMC
- PMC10101773
- OpenAlex
- W4220653799
- Cited by
- 42
- References
- 3
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 15 · 2023: 11 · 2022: 2
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K23HL135274)
- Janssen Pharmaceuticals
- Agency for Healthcare Research and Quality
- MyoKardia
- Mayo Clinic
- American Heart Association
- National Institute on Aging
- Medtronic
- National Institutes of Health (U2CEB021881)
- U.S. Food and Drug Administration
- Samsung
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiomyopathy and Myosin Studies
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- hypertrophic obstructive cardiomyopathy, artificial intelligence, electrocardiogram, disease status assessment, treatment response, hemodynamic indices, laboratory biomarkers, longitudinal follow-up
- MeSH terms
- Artificial Intelligence, Cardiomyopathy, Hypertrophic, Electrocardiography, Humans
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