Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram
Open accessAbstract
Background Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. Objectives This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG). Methods A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects. Results In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM group and 57.5 ± 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval [CI]: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN's AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively. Conclusions ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening.
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Hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension AI-ECG applications remain earlier or pathway-incomplete.
“hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary hypertension remain earlier or pathway-incomplete applications.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [26]
The Conclusions and Study Limitations sections describe the AI-ECG model for hypertrophic cardiomyopathy as promising but still requiring refinement and external validation before broader screening use. The paper does not address AI-ECG applications for cardiac amyloidosis or pulmonary hypertension, so it supports only the hypertrophic-cardiomyopathy part of the claim. Page numbers were not supplied in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredHCM and cardiac amyloidosis should be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.
“Taken together, HCM and CA should therefore be regarded as clinically important but pathway-incomplete domains rather than deployable screening indications comparable to low-LVEF AI-ECG.26–30,43”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [26]
The Discussion says the HCM model may not yet be effective for widespread screening and that external validation is critical; Study Limitations says applicability in other settings remains undetermined (page numbers unavailable). This supports treating HCM screening as not yet ready for widespread use. The paper does not address cardiac amyloidosis or establish a comparison with the deployability of low-LVEF AI-ECG. (LLM, conf=0.86)
Experimental — accuracy not yet measuredHCM has shown consistently high discrimination, international external validation, and early real-world implementation, making it one of the more reproducible structural AI-ECG targets.
“HCM has shown consistently high discrimination, international external validation, and early real-world implementation, making it one of the more reproducible structural AI-ECG targets.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [10]
The Discussion (page not provided) reports high discrimination across the training, validation, and testing cohorts, with AUCs of 0.97, 0.95, and 0.96. But the Conclusions and Study Limitations (pages not provided) say the model still requires external validation and describe screening as a future possibility; this paper does not support international external validation or real-world implementation. (LLM, conf=0.90)
Experimental — accuracy not yet measuredDeep 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 [20]
The Abstract reports that a deep-learning model detected hypertrophic cardiomyopathy from 12-lead ECGs and mentions earlier success detecting left ventricular dysfunction. The Subgroup Analyses treat left ventricular hypertrophy and aortic stenosis as patient subgroups or comparators, not conditions the model was trained to detect. The paper does not establish detection of the listed valvular diseases. Page numbers were not supplied. (LLM, conf=0.86)
Experimental — accuracy not yet measuredKo et al. developed the first AI-ECG model for HCM detection, achieving an AUC of 0.96 in an independent testing cohort, with 87% sensitivity and 90% specificity at the prespecified threshold.
“Ko et al at the Mayo Clinic developed the first AI-ECG model for HCM detection, achieving an AUC of 0.96 in an independent testing cohort, with 87% sensitivity and 90% specificity at the prespecified threshold.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [26]
A CNN using standard 12-lead ECGs detected HCM with an AUC of 0.96, sensitivity of 87%, and specificity of 90%.
“In a landmark study, Ko et al. developed a convolutional neural network using standard 12-lead ECGs to detect HCM and reported strong diagnostic performance, with an AUC of 0.96, sensitivity of 87%, and specificity of 90% [10].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [10]
The low prevalence of individual structural heart diseases limits the potential implementation of models for broad screening because of the low PPVs of the proposed models.
“While these models provide a strong foundation for the role of ECG-based detection of SHDs, the low prevalence of these individual diseases, their potential implementation for broad screening is limited by the low PPVs of the proposed models.10-12,16,32-34”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [32]
Prior studies have reported the use of deep learning on 12-lead ECGs to detect individual structural cardiovascular conditions, including LVSD, hypertrophic cardiomyopathy, cardiac amyloidosis, and aortic stenosis.
“Prior studies have reported the use of deep learning on 12-lead ECGs to detect individual structural cardiovascular conditions, including LVSD,10,11,13 hypertrophic cardiomyopathy,16,32,33 cardiac amyloidosis,34,35 aortic stenosis,12 among others.15,35-37”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [32]
The Mayo Clinic algorithm generally performed better in the adolescent group than in the pediatric group.
“The study found that the algorithm generally performed better in the adolescent group than in the pediatric group.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [154]
The Mayo Clinic algorithm achieved an AUC of 0.96, sensitivity of 87%, and specificity of 90% for adult HCM detection.
“The algorithm performed impressively in adult HCM patient ECG detection, with an AUC of 0.96, sensitivity of 87%, and specificity of 90%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [154]
The Mayo Clinic algorithm achieved an AUC of 0.98, sensitivity of 92%, specificity of 95%, PPV of 22%, and NPV of 99% in a test of 300 children and over 18,000 controls.
“The algorithm’s performance in a test of 300 children and over 18,000 age and gender-matched controls was equally impressive: the HCM detection model achieved an AUC of 0.98, sensitivity of 92%, specificity of 95%, Positive Predictive Value (PPV) of 22%, and Negative Predictive Value (NPV) of 99%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [154]
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Cites
Prevalence of Hypertrophic Cardiomyopathy in a General Population of Young Adults
Maron, B. J., Gardin, J. M., Flack, J. M., Gidding, S. S., Kurosaki, T., & Bild, D. E. (1995) · Circulation · doi:10.1161/01.cir.92.4.785 · Cited by 2,274 · see citing works
2011 ACCF/AHA Guideline for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy
Gersh, B. J., Maron, B. J., Bonow, R. O., Dearani, J. A., Fifer, M. A., Link, M. S., Naidu, S. S., Nishimura, R. A., Ommen, S. R., Rakowski, H., et al. (2011) · Circulation · doi:10.1161/cir.0b013e318223e2bd · Cited by 2,105 · see citing works
Self-citationAttia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389 · see citing works
Self-citationNew Perspectives on the Prevalence of Hypertrophic Cardiomyopathy
Semsarian, C., Ingles, J., Maron, M. S., & Maron, B. J. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.01.019 · Cited by 1,386 · see citing works
2011 ACCF/AHA Guideline for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy
Gersh, B. J., Maron, B. J., Bonow, R. O., Dearani, J. A., Fifer, M. A., Link, M. S., Naidu, S. S., Nishimura, R. A., Ommen, S. R., Rakowski, H., et al. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2011.06.011 · Cited by 1,276 · see citing works
Self-citationCorrado, D., Pelliccia, A., Björnstad, H. H., Vanhees, L., Biffi, A., Börjesson, M., Panhuyzen-Goedkoop, N. M., Deligiannis, A. P., Solberg, E. E., Dugmore, D., et al. (2005) · European Heart Journal · doi:10.1093/eurheartj/ehi108 · Cited by 1,101 · see citing works
Prescott, E., & On behalf of the Danish task force (2006) · European Heart Journal · doi:10.1093/eurheartj/ehl342 · Cited by 875 · see citing works
Recommendations for interpretation of 12-lead electrocardiogram in the athlete
Corrado, D., Pelliccia, A., Heidbüchel, H., Sharma, S., Link, M. S., Basso, C., Biffi, A., Buja, G., Delise, P., Gussac, I., et al. (2009) · European Heart Journal · doi:10.1093/eurheartj/ehp473 · Cited by 843 · see citing works
Electrocardiographic interpretation in athletes: the ‘Seattle Criteria’
Drezner, J. A., Ackerman, M. J., Anderson, J., Ashley, E. A., Asplund, C. A., Baggish, A. L., Börjesson, M., Cannon, B. C., Corrado, D., DiFiori, J. P., et al. (2013) · British Journal of Sports Medicine · doi:10.1136/bjsports-2012-092067 · Cited by 551 · see citing works
Self-citationClinical Significance of Abnormal Electrocardiographic Patterns in Trained Athletes
Pelliccia, A., Maron, B. J., Culasso, F., Paolo, F. M. D., Spataro, A., Biffi, A., Caselli, G., & Piovano, P. (2000) · Circulation · doi:10.1161/01.cir.102.3.278 · Cited by 545 · see citing works
Gender-Related Differences in the Clinical Presentation and Outcome of Hypertrophic Cardiomyopathy
Olivotto, I., Maron, M. S., Adabağ, S., Casey, S. A., Vargiu, D., Link, M. S., Udelson, J. E., Cecchi, F., & Maron, B. J. (2005) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2005.04.043 · Cited by 431 · see citing works
Maron, B. J., Friedman, R. A., Kligfield, P., Levine, B. D., Viskin, S., CHAITMAN, B. R., Okin, P. M., Saul, J. P., Salberg, L. R., Hare, G. F. V., et al. (2014) · Circulation · doi:10.1161/cir.0000000000000025 · Cited by 395 · see citing works
Incidence and Causes of Sudden Death in U.S. College Athletes
Maron, B. J., Haas, T. S., Murphy, C. J., Ahluwalia, A., & Rutten-Ramos, S. C. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.01.041 · Cited by 344 · see citing works
Sheikh, N., Papadakis, M., Ghani, S., Zaidi, A., Gati, S., Adami, P. E., Carré, F., Schnell, F., Wilson, M. G., Ávila, P., et al. (2014) · Circulation · doi:10.1161/circulationaha.113.006179 · Cited by 326 · see citing works
Siontis, K. C., Geske, J. B., Ong, K. C., Nishimura, R. A., Ommen, S. R., & Gersh, B. J. (2014) · Journal of the American Heart Association · doi:10.1161/jaha.114.001002 · Cited by 323 · see citing works
Self-citationGersh, B. J., Maron, B. J., Bonow, R. O., Dearani, J. A., Fifer, M. A., Link, M. S., Naidu, S. S., Nishimura, R. A., Ommen, S. R., Rakowski, H., et al. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2011.10.825 · Cited by 278 · see citing works
Self-citationWheeler, M. T., Heidenreich, P. A., Froelicher, V. F., Hlatky, M. A., & Ashley, E. A. (2010) · Annals of Internal Medicine · doi:10.7326/0003-4819-152-5-201003020-00005 · Cited by 260 · see citing works
Interpretation of the Electrocardiogram of Young Athletes
Uberoi, A., Stein, R., Perez, M. V., Freeman, J. V., Wheeler, M. T., Dewey, F. E., Peidro, R. M., Hadley, D., Drezner, J. A., Sharma, S., et al. (2011) · Circulation · doi:10.1161/circulationaha.110.013078 · Cited by 233 · see citing works
Women with hypertrophic cardiomyopathy have worse survival
Geske, J. B., Ong, K. C., Siontis, K. C., Hebl, V. B., Ackerman, M. J., Hodge, D. O., Miller, V. M., Nishimura, R. A., Oh, J. K., Schaff, H. V., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx527 · Cited by 226 · see citing works
Self-citationMaron, B. J., Friedman, R. A., Kligfield, P., Levine, B. D., Viskin, S., CHAITMAN, B. R., Okin, P. M., Saul, J. P., Salberg, L. R., Hare, G. F. V., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.05.006 · Cited by 204 · see citing works
Outcome of Patients With Hypertrophic Cardiomyopathy and a Normal Electrocardiogram
McLeod, C. J., Ackerman, M. J., Nishimura, R. A., Tajik, A. J., Gersh, B. J., & Ommen, S. R. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.02.071 · Cited by 188 · see citing works
Self-citationRowin, E. J., Maron, B. J., Appelbaum, E. A., Link, M. S., Gibson, C. M., Lesser, J. R., Haas, T. S., Udelson, J. E., Manning, W. J., & Maron, M. S. (2012) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2012.05.035 · Cited by 119 · see citing works
Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification
Rahman, Q. A., Tereshchenko, L. G., Kongkatong, M., Abraham, T. P., Abraham, M. R., & Shatkay, H. (2015) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2015.2426213 · Cited by 100 · see citing works
Lyon, A., Ariga, R., Mincholé, A., Mahmod, M., Ormondroyd, E., Laguna, P., de Freitas, N., Neubauer, S., Watkins, H. C., & Rodríguez, B. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00213 · Cited by 86 · see citing works
Cost Implications of Using Different ECG Criteria for Screening Young Athletes in the United Kingdom
Dhutia, H., Malhotra, A., Gabus, V., Merghani, A., Finocchiaro, G., Millar, L. M., Narain, R., Papadakis, M., Naci, H., Tome, M., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.05.076 · Cited by 70 · see citing works
Comparison of three ECG criteria for athlete pre-participation screening
Pickham, D. M., Zarafshar, S., Sani, D., Kumar, N., & Froelicher, V. F. (2014) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2014.07.019 · Cited by 68 · see citing works
Modest agreement in ECG interpretation limits the application of ECG screening in young athletes
Brosnan, M. J., Gerche, A. L., Kumar, S., Lo, W., Kalman, J. M., & Prior, D. (2014) · Heart Rhythm · doi:10.1016/j.hrthm.2014.09.060 · Cited by 53 · see citing works
Riding, N. R., Sharma, S., McClean, G., Adamuz, C., Watt, V., & Wilson, M. G. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy521 · Cited by 49 · see citing works
Pilot study analyzing automated ECG screening of hypertrophic cardiomyopathy
Campbell, M. J., Zhou, X., Han, C. Y., Abrishami, H., Webster, G., Miyake, C. Y., Sower, C. T., Anderson, J. B., Knilans, T. K., & Czosek, R. J. (2017) · Heart Rhythm · doi:10.1016/j.hrthm.2017.02.011 · Cited by 14 · see citing works
Using a neural network to diagnose the hypertrophic portions of hypertrophic cardiomyopathy.
Ouyang, N., & Yamauchi, K. (1998) · PubMed · Cited by 10 · see citing works
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Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00503-2 · Cited by 914 · see citing works
Self-citationApplication of artificial intelligence to the electrocardiogram
Attia, Z. I., Harmon, D., Behr, E. R., & Friedman, P. A. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab649 · Cited by 313 · see citing works
Self-citationDeep learning and the electrocardiogram: review of the current state-of-the-art
Somani, S. S., Russak, A. J., Richter, F., Zhao, S., Vaid, A., Chaudhry, F. F., Freitas, J. K. D., Naik, N., Miotto, R., Nadkarni, G. N., et al. (2020) · EP Europace · doi:10.1093/europace/euaa377 · Cited by 267 · 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
Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology
Feeny, A. K., Chung, M. K., Madabhushi, A., Attia, Z. I., Čikeš, M., Firouznia, M., Friedman, P. A., Kalscheur, M. M., Kapa, S., Narayan, S. M., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007952 · Cited by 210 · see citing works
Self-citationHuang, J.-D., Wang, J., Ramsey, E., Leavey, G., Chico, T., & Condell, J. V. (2022) · Sensors · doi:10.3390/s22208002 · Cited by 191 · see citing works
Soft bioelectronics for the management of cardiovascular diseases
Sunwoo, S., Han, S. I., Park, C. S., Kim, J. H., Georgiou, J. S., Lee, S., Kim, D., & Hyeon, T. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00102-z · Cited by 176 · see citing works
Artificial Intelligence–Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis
Grogan, M., Lopez-Jimenez, F., Cohen-Shelly, M., Dispenzieri, A., Attia, Z. I., Abou Ezzedine, O. F., Lin, G., Kapa, S., Borgeson, D. D., Friedman, P. A., et al. (2021) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2021.04.023 · Cited by 166 · see citing works
Self-citationHerman, R., Meyers, H. P., Smith, S. W., Bertolone, D. T., Leone, A., Bermpeis, K., Viscusi, M. M., Belmonte, M., Demolder, A., Boža, V., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad074 · Cited by 165 · see citing works
Yasmin, F., Shah, S. M. I., Naeem, A., Shujauddin, S. M., Jabeen, A., Kazmi, S., Siddiqui, S. A., Kumar, P., Salman, S., Hassan, S. A., et al. (2021) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2204121 · Cited by 171 · see citing works
Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
Attia, Z. I., Harmon, D. M., Dugan, J., Manka, L., Lopez-Jimenez, F., Lerman, A., Siontis, K. C., Noseworthy, P. A., Yao, X., Klavetter, E. W., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02053-1 · Cited by 154 · see citing works
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Muzammil, M. A., Javid, S., Afridi, A. K., Siddineni, R., Shahabi, M., Haseeb, M. A., Fariha, F., Kumar, S., Zaveri, S., & Nashwan, A. J. (2024) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2024.01.006 · Cited by 137 · see citing works
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Nagarajan, V. D., Su-Lin, L., Robertus, J. L., Nienaber, C. A., Trayanova, N. A., & Ernst, S. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab544 · Cited by 139 · see citing works
Giudicessi, J. R., Schram, M. C., Bos, J. M., Galloway, C. D., Shreibati, J. B., Johnson, P. W., Carter, R. E., Disrud, L. W., Kleiman, R., Attia, Z. I., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.050231 · Cited by 130 · see citing works
Self-citationHughes, J. W., Olgin, J. E., Avram, R., Abreau, S. A., Sittler, T., Radia, K., Hsia, H. H., Walters, T. E., Lee, B. K., Gonzalez, J. E., et al. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2021.2746 · Cited by 130 · see citing works
Goto, S., Solanki, D., John, J. E., Yagi, R., Homilius, M., Ichihara, G., Katsumata, Y., Gaggin, H. K., Itabashi, Y., MacRae, C. A., et al. (2022) · Circulation · doi:10.1161/circulationaha.121.058696 · Cited by 130 · see citing works
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Self-citationOtaki, Y., Singh, A., Kavanagh, P., Miller, R. J. H., Parekh, T., Tamarappoo, B. K., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., et al. (2021) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2021.04.030 · Cited by 132 · see citing works
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Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools
Adedinsewo, D. A., Pollak, A. W., Phillips, S. D., Smith, T. L., Svatikova, A., Hayes, S. N., Mulvagh, S. L., Norris, C., Roger, V. L., Noseworthy, P. A., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.319876 · Cited by 92 · see citing works
Self-citationWu, Z., & Guo, C. (2025) · BioMedical Engineering OnLine · doi:10.1186/s12938-025-01349-w · Cited by 104 · see citing works
Artificial Intelligence in Cardiology—A Narrative Review of Current Status
Koulaouzidis, G., Jadczyk, T., Iakovidis, D. K., Koulaouzidis, A., Bisnaire, M., & Charisopoulou, D. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11133910 · Cited by 90 · see citing works
Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy
Soto, J. T., Weston Hughes, J., Sanchez, P. A., Perez, M., Ouyang, D., & Ashley, E. A. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac033 · Cited by 76 · see citing works
A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging
Li, S., Deng, Y., Zhu, Z., Hua, H., & Tao, Z. (2021) · Diagnostics · doi:10.3390/diagnostics11091523 · Cited by 71 · see citing works
Deep learning evaluation of biomarkers from echocardiogram videos
Hughes, J. W., Yuan, N., He, B., Ouyang, J., Ebinger, J., Botting, P., Lee, J., Theurer, J., Tooley, J. E., Nieman, K., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103613 · Cited by 71 · see citing works
An ECG-based artificial intelligence model for assessment of sudden cardiac death risk
Holmström, L. T. A., Chugh, H. S., Nakamura, K., Bhanji, Z., Seifer, M., Uy‐Evanado, A., Reinier, K., Ouyang, D., & Chugh, S. S. (2024) · Communications Medicine · doi:10.1038/s43856-024-00451-9 · Cited by 68 · see citing works
Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation
Yuan, N., Duffy, G., Dhruva, S. S., Oesterle, A., Pellegrini, C. N., Theurer, J., Vali, M., Heidenreich, P. A., Keyhani, S., & Ouyang, D. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.3701 · Cited by 67 · see citing works
Ahn, J. C., Attia, Z. I., Rattan, P., Mullan, A. F., Buryska, S. M., Allen, A. M., Kamath, P. S., Friedman, P. A., Shah, V. H., Noseworthy, P. A., et al. (2021) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000001617 · Cited by 65 · see citing works
Self-citationArtificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review
Ose, B. M., Sattar, Z., Gupta, A., Gahona, C. C. T., Harvey, C., & Noheria, A. (2024) · Current Cardiology Reports · doi:10.1007/s11886-024-02062-1 · Cited by 67 · see citing works
Adedinsewo, D. A., Morales-Lara, A. C., Afolabi, B. B., Kushimo, O. A., Mbakwem, A. C., Ibiyemi, K. F., Ogunmodede, J. A., Raji, H. O., Ringim, S. H., Habib, A. A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03243-9 · Cited by 63 · see citing works
Self-citationJCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia
Takase, B., Ikeda, T., Shimizu, W., Abe, H., Aiba, T., Chinushi, M., Koba, S., KUSANO, K. F., Niwano, S., Takahashi, N., et al. (2023) · Circulation Journal · doi:10.1253/circj.cj-22-0827 · Cited by 67 · see citing works
Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
Mayourian, J., La Cava, W. G., Vaid, A., Nadkarni, G. N., Ghelani, S. J., Mannix, R., Geva, T., Dionne, A., Alexander, M. E., Duong, S. Q., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.067750 · Cited by 59 · see citing works
Anderson–Fabry disease management: role of the cardiologist
Pieroni, M., Namdar, M., Olivotto, I., & Desnick, R. J. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae148 · Cited by 59 · see citing works
Temporal and Global Trends of the Incidence of Sudden Cardiac Death in Hypertrophic Cardiomyopathy
Abdelfattah, O., Martinez, M. W., Sayed, A., ElRefaei, M., Abushouk, A. I., Hassan, A. A., Masri, A., Winters, S. L., Kapadia, S. R., Maron, B. J., et al. (2022) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2022.07.012 · Cited by 56 · see citing works
Bleijendaal, H., Ramos, L. A., Lopes, R. R., Verstraelen, T. E., Baalman, S. W. E., Pool, M. O., Tjong, F. V. Y., Melgarejo-Meseguer, F.-M., Gimeno-Blanes, F.-J., Gimeno, J. R., et al. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.08.021 · Cited by 56 · see citing works
Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning
van de Leur, R. R., Taha, K., Bos, M. N., van der Heijden, J. F., Gupta, D. K., Cramer, M. J., Hassink, R. J., van der Harst, P., Doevendans, P. A., Asselbergs, F. W., et al. (2021) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009056 · Cited by 56 · see citing works
Shu, S., Ren, J., & Song, J. (2021) · Circulation Journal · doi:10.1253/circj.cj-20-1121 · Cited by 59 · see citing works
Haimovich, J. S., Diamant, N., Khurshid, S. S., Achille, P. D., Reeder, C., Friedman, S. F., Singh, P., Spurlock, W., Ellinor, P. T., Philippakis, A., et al. (2023) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2023.03.001 · Cited by 54 · see citing works
Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review
Almansouri, N. E., Awe, M., Rajavelu, S., Jahnavi, K., Shastry, R., Hasan, A., Hasan, H., Lakkimsetti, M., AlAbbasi, R. K., Criollo-Gutiérrez, B. D., et al. (2024) · Cureus · doi:10.7759/cureus.55869 · Cited by 69 · see citing works
Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Shankar, S. V., Coppi, A., Foppa, M., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., et al. (2025) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.01.030 · Cited by 53 · see citing works
A fully-automated paper ECG digitisation algorithm using deep learning
Wu, H., Patel, K. H. K., Li, X., Zhang, B., Galazis, C., Bajaj, N., Sau, A., Shi, X., Sun, L., Tao, Y., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25284-1 · Cited by 53 · see citing works
Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects
Haq, I. U., Chhatwal, K., SANAKA, K. O., & Xu, B. (2022) · Vascular Health and Risk Management · doi:10.2147/vhrm.s279337 · Cited by 61 · see citing works
Artificial Intelligence in Pediatric Cardiology: A Scoping Review
Sethi, Y., Patel, N., Kaka, N., Desai, A., Kaiwan, O., Sheth, M., Sharma, R., Huang, H. Y. R., Chopra, H., Khandaker, M. U., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11237072 · Cited by 53 · see citing works
Palermi, S., Vecchiato, M., Saglietto, A., Niederseer, D., Oxborough, D., Ortega‐Martorell, S., Olier, I., Castelletti, S., Baggish, A. L., Maffessanti, F., et al. (2024) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwae008 · Cited by 50 · see citing works
Machine learning models for prediction of adverse events after percutaneous coronary intervention
Niimi, N., Shiraishi, Y., Sawano, M., Ikemura, N., Inohara, T., Ueda, I., Fukuda, K., & Kohsaka, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-10346-1 · Cited by 49 · see citing works
The application of deep learning in electrocardiogram: Where we came from and where we should go?
Sun, J.-Y., Shen, H., Qu, Q., Sun, W., & Kong, X.-Q. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2021.05.017 · Cited by 48 · see citing works
Electrocardiogram 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
Wearable biosensors for cardiovascular monitoring leveraging nanomaterials
Chen, X., Manshaii, F., Tioran, K., Wang, S., Zhou, Y., Zhao, J., Yang, M., Yin, X., Liu, S., & Wang, K. (2024) · Advanced Composites and Hybrid Materials · doi:10.1007/s42114-024-00906-6 · Cited by 50 · see citing works
Stamate, E., Piraianu, A. I., Ciobotaru, O. R., Crassas, R., Duca, O.-M., Fulga, A., Grigore, I., Vintila, V., Fulga, I., & Ciobotaru, O. C. (2024) · Diagnostics · doi:10.3390/diagnostics14111103 · Cited by 49 · see citing works
Wide Complex Tachycardia Differentiation: A Reappraisal of the State‐of‐the‐Art
Kashou, A. H., Noseworthy, P. A., DeSimone, C. V., Deshmukh, A. J., Asirvatham, S. J., & May, A. M. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016598 · Cited by 46 · see citing works
Self-citationLin, C.-H., Liu, Z., Chu, P., Chen, J., Wu, H.-H., Wen, M., Kuo, C., & Chang, T.-Y. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01410-3 · Cited by 45 · see citing works
Siontis, 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-citationCostanzo, A. D., Spaccarotella, C., Esposito, G., & Indolfi, C. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13041033 · Cited by 45 · see citing works
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., et al. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2022.01.005 · Cited by 42 · see citing works
Self-citationKalmady, S. V., Salimi, A., Sun, W., Sepehrvand, N., Nademi, Y., Bainey, K. R., Ezekowitz, J. A., Hindle, A., McAlister, F. A., Greiner, R., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01130-8 · Cited by 42 · see citing works
Artificial intelligence and the electrocardiogram: A modern renaissance
Palermi, S., Vecchiato, M., Ng, F. S., Attia, Z., Cho, Y., Anselmino, M., De Ferrari, G. M., Saglietto, A., Sau, A., Chiu, I.-M., et al. (2025) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2025.04.036 · Cited by 42 · see citing works
Self-citationArtificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology
Kashou, A. H., May, A. M., & Noseworthy, P. A. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01317-x · Cited by 44 · see citing works
Self-citationIdentification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning
Sangha, V., Dhingra, L. S., Aminorroaya, A., Croon, P. M., Sikand, N. V., Sen, S., Martinez, M. W., Maron, M. S., Krumholz, H. M., Asselbergs, F. W., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00685-3 · Cited by 41 · see citing works
Artificial intelligence in cardiovascular medicine: An updated review of the literature
Zargarzadeh, A., Javanshir, E., Ghaffari, A., Mosharkesh, E., & Anari, B. (2023) · Journal of Cardiovascular and Thoracic Research · doi:10.34172/jcvtr.2023.33031 · Cited by 41 · see citing works
Artificial intelligence and cardiology: Current status and perspective
Nakamura, T., & Sasano, T. (2021) · Journal of Cardiology · doi:10.1016/j.jjcc.2021.11.017 · Cited by 43 · see citing works
Smaranda, A. M., Drăgoiu, T. S., Caramoci, A., Afetelor, A. A., Ionescu, A. M., & Bădărău, I. A. (2024) · Sports · doi:10.3390/sports12060144 · Cited by 42 · see citing works
Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19
Attia, Z. I., Kapa, S., Noseworthy, P. A., López-Jiménez, F. A., & Friedman, P. A. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2020.09.020 · Cited by 39 · see citing works
Self-citationAn electrocardiogram-based AI algorithm for early detection of pulmonary hypertension
DuBrock, H. M., Wagner, T. E., Carlson, K., Carpenter, C. L., Awasthi, S., Attia, Z. I., Frantz, R. P., Friedman, P. A., Kapa, S., Annis, J., et al. (2024) · European Respiratory Journal · doi:10.1183/13993003.00192-2024 · Cited by 38 · see citing works
Self-citationSiontis, K. C., Suárez, A. B., Sehrawat, O., Ackerman, M. J., Attia, Z. I., Friedman, P. A., Noseworthy, P. A., & Maanja, M. (2023) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2023.07.002 · Cited by 38 · see citing works
Self-citationAcute Myocardial Infarction Detection Using Deep Learning-Enabled Electrocardiograms
Chen, X., Guo, W., Zhao, L., Huang, W., Wang, L., Sun, A., Li, L., & Mo, F. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.654515 · Cited by 37 · see citing works
Machine learning in cardiology: Clinical application and basic research
Komuro, J., Kusumoto, D., Hashimoto, H., & Yuasa, S. (2023) · Journal of Cardiology · doi:10.1016/j.jjcc.2023.04.020 · Cited by 39 · see citing works
Sammani, A., van de Leur, R. R., Henkens, M. T. H. M., Meine, M., Loh, P. K. K., Hassink, R. J., Oberski, D. L., Heymans, S. R. B., Doevendans, P. A., Asselbergs, F. W., et al. (2022) · EP Europace · doi:10.1093/europace/euac054 · Cited by 37 · see citing works
Adopting 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
A method to screen left ventricular dysfunction through ECG based on convolutional neural network
Sun, J., Qiu, Y., Guo, H., Yang, H., Shao, B., Qiao, Y., Guo, J., Ding, H., Zhang, Z., Miao, L., et al. (2021) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.14936 · Cited by 36 · see citing works
Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms
Yuan, N., Kwan, A. C., Duffy, G., Theurer, J., Chen, J. H., Nieman, K., Botting, P. G., Dey, D., Berman, D. S., Cheng, S. C., et al. (2022) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2022.12.014 · Cited by 36 · see citing works
Jentzer, J. C., Kashou, A. H., & Murphree, D. H. (2023) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2023.100089 · Cited by 36 · see citing works
Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis
Allegra, A., Mirabile, G., Tonacci, A., Genovese, S., Pioggia, G., & Gangemi, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065680 · Cited by 34 · see citing works
Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology
van de Leur, R. R., Boonstra, M., Bagheri, A., Roudijk, R. W., Sammani, A., Taha, K., Doevendans, P. A., van der Harst, P., van Dam, P. M., Hassink, R. J., et al. (2020) · Arrhythmia & Electrophysiology Review · doi:10.15420/aer.2020.26 · Cited by 33 · see citing works
Somani, S. S., Honarvar, H., Narula, S., Landi, I., Lee, S. Y., Khachatoorian, Y., Rehmani, A., Kim, A., Freitas, J. K. D., Teng, S., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab101 · Cited by 34 · see citing works
Adedinsewo, D. A., Hardway, H., Morales-Lara, A. C., Wieczorek, M. A., Johnson, P. W., Douglass, E. J., Dangott, B. J., Nakhleh, R. E., Narula, T., Patel, P. C., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad001 · Cited by 33 · see citing works
Self-citationFiorina, L., Carbonati, T., Narayanan, K., Li, J., Henry, C., Singh, J. P., & Marijon, É. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf073 · Cited by 32 · see citing works
Diagnostic and prognostic electrocardiographic features in patients with hypertrophic cardiomyopathy
Bernardini, A., Crotti, L., Olivotto, I., & Cecchi, F. (2023) · European Heart Journal Supplements · doi:10.1093/eurheartjsupp/suad074 · Cited by 31 · see citing works
The biomedical applications of artificial intelligence: an overview of decades of research
Naskar, S., Sharma, S., Kuotsu, K., Halder, S., Pal, G., Saha, S., Mondal, S., Biswas, U. K., Jana, M., & Bhattacharjee, S. (2025) · Journal of drug targeting (Print) · doi:10.1080/1061186x.2024.2448711 · Cited by 18 · see citing works
Iyer, A. A., & Umadevi, K. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-08824-3 · Cited by 31 · see citing works
Liu, C., Hsieh, M.-E., Hu, Y., Wei, T.-Y., Wu, I., Chen, P., Lin, Y., Higa, S., Yagi, N., Chen, S., et al. (2022) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.008360 · Cited by 31 · see citing works
Gaps in Evidence for Risk Stratification for Sudden Cardiac Death in Hypertrophic Cardiomyopathy
Pelliccia, F., Gersh, B. J., & Camici, P. G. (2021) · Circulation · doi:10.1161/circulationaha.120.051968 · Cited by 31 · see citing works
Self-citationKochav, S. M., Raita, Y., Fifer, M. A., Takayama, H., Ginns, J. N., Maurer, M. S., Reilly, M. P., Hasegawa, K., & Shimada, Y. J. (2020) · International Journal of Cardiology · doi:10.1016/j.ijcard.2020.11.003 · Cited by 30 · see citing works
Miura, K., Yagi, R., Miyama, H., Kimura, M., Kanazawa, H., Hashimoto, M., Kobayashi, S., Nakahara, S., Ishikawa, T., Taguchi, I., et al. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.102141 · Cited by 28 · see citing works
Deep learning for electrocardiogram interpretation: Bench to bedside
Schots, B. B. S., Pizarro, C. S., Arends, B. K. O., Oerlemans, M. I. F. J., Ahmetagić, D., van der Harst, P., & van Es, R. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.70002 · Cited by 28 · see citing works
Avula, V. R., Wu, K. C., & Carrick, R. T. (2023) · JACC Advances · doi:10.1016/j.jacadv.2023.100686 · Cited by 28 · see citing works
High precision ECG digitization using artificial intelligence
Demolder, A., Krešňáková, V., Hojcka, M., Boža, V., Iring, A., Rafajdus, A., Rovder, S., Palus, T., Herman, M., Bauer, F., et al. (2025) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2025.153900 · Cited by 26 · see citing works
Fabry Cardiomyopathy: Current Practice and Future Directions
Yim, J., Yau, O., Yeung, D. F., & Tsang, T. S. M. (2021) · Cells · doi:10.3390/cells10061532 · Cited by 26 · see citing works
Artificial intelligence and atrial fibrillation
Sehrawat, O., Kashou, A. H., & Noseworthy, P. A. (2022) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.15440 · Cited by 26 · see citing works
Self-citationWu, L., Zhou, B., Liu, D., Wang, L., Zhang, X., Xu, L., Yuan, L., Zhang, H., Ling, Y., Shi, G., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11185408 · Cited by 26 · see citing works
Next Generation ECG: The Impact of Artificial Intelligence and Machine Learning
Adasuriya, G., & Haldar, S. (2023) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-023-00723-4 · Cited by 26 · see citing works
Oikonomou, E. K., & Khera, R. (2024) · Hellenic Journal of Cardiology · doi:10.1016/j.hjc.2024.07.003 · Cited by 26 · see citing works
Sport practice in hypertrophic cardiomyopathy: running to stand still?
Mascia, G., Olivotto, I., Brugada, J., Arbelo, E., Donna, P. D., Bona, R. D., Canepa, M., & Porto, I. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2021.10.013 · Cited by 25 · see citing works
Patient-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
Self-citation
- Authors
- 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., Geske, J. B., Asirvatham, S. J., Lopez-Jimenez, F., Nishimura, R. A., Friedman, P. A., & Noseworthy, P. A.
- Affiliations
- Mayo Clinic (US)
- Mayo Clinic in Florida (US)
- Corresponding author
- Noseworthy, Peter A.
- ORCID
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 75
- Issue
- 7
- Pages
- 722-733
- ISSN
- 0735-1097 (ISSN-L)
- OpenAlex
- W3008117014
- Cited by
- 404
- References
- 30
- Citations by year
- 2026: 53 · 2025: 89 · 2024: 86 · 2023: 70 · 2022: 41 · 2021: 47 · 2020: 17
- FWCI
- 27.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K01HL124045)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- hypertrophic cardiomyopathy, electrocardiogram, convolutional neural networks, artificial intelligence, automated disease detection, left ventricular hypertrophy, sarcomeric mutations, sudden cardiac death, diagnostic accuracy
- MeSH terms
- Adult, Aged, Algorithms, Cardiomyopathy, Hypertrophic, Electrocardiography, Female, Humans, Male, Middle Aged, Models, Cardiovascular, Neural Networks, Computer
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