Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram
Abstract
Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found 1-4. An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A landmark 2019 study demonstrated that AI-ECG could identify reduced LVEF from routine 12-lead recordings with strong discriminatory performance, establishing the principle that an inexpensive and widely available test could function as an upstream screening instrument for abnormalities ordinarily confirmed by echocardiography.
“A landmark 2019 study demonstrated that artificial intelligence (AI)-ECG could identify reduced left ventricular ejection fraction (LVEF) from routine 12-lead recordings with strong discriminatory performance, establishing the principle that an inexpensive and widely available test could function as an upstream screening instrument for abnormalities ordinarily confirmed by echocardiography.12”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
At present, AI-ECG is better interpreted as a tool for risk enrichment and follow-up prioritization in selected screening settings than as a stand-alone mandate for structured surveillance or immediate pathway escalation.
“At present, AI-ECG is better interpreted as a tool for risk enrichment and follow-up prioritization in selected screening settings than as a stand-alone mandate for structured surveillance or immediate pathway escalation.12,19,44,45”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
Patients without baseline ventricular dysfunction but with a positive AI-ECG screen had a fourfold higher risk of developing future ventricular dysfunction than those with a negative screen.
“In the same study, patients without baseline ventricular dysfunction but with a positive AI-ECG screen had a fourfold higher risk of developing future ventricular dysfunction than those with a negative screen.12”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
In the original Attia et al. study, patients with false-positive low-EF screens had a fourfold higher risk of developing future ventricular dysfunction (hazard ratio 4.1; 95% CI 3.3-5.0).
“In the original Attia et al study, patients with false-positive low-EF screens had a fourfold higher risk of developing future ventricular dysfunction (hazard ratio 4.1; 95% CI 3.3–5.0).12”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
Attia et al 2019 found that AI-ECG for reduced LVEF achieved AUC 0.93 and NPV 98.7%, and AI-positive/echo-negative patients had higher future ventricular dysfunction risk.
“AUC 0.93; NPV 98.7%; AI-positive/echo-negative patients had higher future ventricular dysfunction risk”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
Attia et al. in 2019 demonstrated that a convolutional neural network could detect LVEF ≤35% with an AUC of 0.93 and a negative predictive value of 98.7%.
“The field was established by Attia et al in 2019, who demonstrated that a convolutional neural network could detect LVEF ≤35% with an AUC of 0.93 and a negative predictive value of 98.7%.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
The regulatory maturity of AI-ECG for SHD screening remains uneven.
“The regulatory maturity of AI-ECG for SHD screening remains uneven.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
Low-LVEF detection is the most mature use case, having already progressed from model development to randomized trials and early implementation studies.
“Low-LVEF detection is the most mature use case, having already progressed from model development to randomized trials and early implementation studies.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [12]
ECG waveforms contain latent signatures of ventricular dysfunction.
“Emerging evidence suggests that ECG waveforms contain latent signatures of ventricular dysfunction [4], structural heart disease [5], metabolic abnormalities [6], and broader physiologic characteristics such as biological age [7] and future mortality risk [8].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
Attia et al.'s CNN identified patients with LVEF ≤ 35% with an AUC of 0.93, sensitivity of 86.3%, and specificity of 85.7%.
“The model achieved an area under the receiver operating characteristic curve (AUC) of 0.93, with a sensitivity of 86.3% and specificity of 85.7%, a strong performance for a test this cheap and widely available [4].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
Patients with a positive AI-ECG despite a preserved ejection fraction were more likely to develop ventricular dysfunction during follow-up.
“Notably, patients with a positive AI-ECG despite a preserved ejection fraction were more likely to develop ventricular dysfunction during follow-up, suggesting that the model may be identifying subclinical disease rather than only established systolic dysfunction, positioning AI-ECG as a screening tool for both current and future ventricular dysfunction.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
AI-ECG may identify subclinical disease rather than only established systolic dysfunction.
“Notably, patients with a positive AI-ECG despite a preserved ejection fraction were more likely to develop ventricular dysfunction during follow-up, suggesting that the model may be identifying subclinical disease rather than only established systolic dysfunction, positioning AI-ECG as a screening tool for both current and future ventricular dysfunction.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
AI-ECG can be positioned as a screening tool for both current and future ventricular dysfunction.
“Notably, patients with a positive AI-ECG despite a preserved ejection fraction were more likely to develop ventricular dysfunction during follow-up, suggesting that the model may be identifying subclinical disease rather than only established systolic dysfunction, positioning AI-ECG as a screening tool for both current and future ventricular dysfunction.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
AI systems may identify clinically relevant information that is not readily apparent through conventional interpretation or visual inspection by clinicians.
“As a result, AI systems may identify clinically relevant information that is not readily apparent through conventional interpretation or visual inspection by clinicians [4].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
A routine ECG carries information about cardiac structure that a human reader cannot extract from the same tracing.
“The more striking implication was that a routine ECG carries information about cardiac structure that a human reader cannot extract from the same tracing.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
Evidence from reduced-ejection-fraction models and the EAGLE trial suggests that AI-ECG screening can identify subclinical disease and influence diagnostic evaluation.
“Evidence from reduced-ejection-fraction models and the EAGLE trial suggests that this approach can identify subclinical disease and influence diagnostic evaluation [4,19].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [4]
Previously, applications of artificial intelligence for electrocardiograms have shown potential to detect signatures of structural heart diseases.
“Previously applications of artificial intelligence for electrocardiograms (AI-ECG) have shown potential to detect signatures of SHDs.10-18”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [10]
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 [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 [10]
A key challenge of AI-ECG models in detecting specific cardiac disorders using ECGs is low precision driven by the low prevalence of individual disorders.
“A key challenge of AI-ECG models in detecting specific cardiac disorders using ECGs is the low precision driven by the low prevalence of individual disorders.10-12”
From Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images (2025) · cites this paper as [10]
LVEF < 40% is a threshold present in most heart failure diagnosis guidelines and consistent with prior work.
“The cutoff for low LVEF was set as LVEF < 40%, a threshold present in most heart failure diagnosis guidelines4, and consistent with prior work in this space8,22.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [8]
Artificial intelligence can detect LVSD from ECGs.
“Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs)”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [8]
LVEF below 40% is a threshold present in most heart failure diagnosis guidelines.
“Both models were trained to detect LVEF below 40%, a threshold present in most heart failure diagnosis guidelines4, and consistent with prior work8,22.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [8]
A convolutional neural network developed with 12-lead ECGs classified ventricular dysfunction with good accuracy.
“In another study, a convolutional neural network developed with a training dataset of 35,970, 12-lead ECGs and validated in an independent dataset of 52,870 ECGs classified ventricular dysfunction with good accuracy60.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [60]
Attia et al. used 1-lead ECGs from an AW coupled with an AI algorithm to detect patients with left ventricular dysfunction with an area under the curve of 0.885.
“Attia et al. used 1-lead ECGs from an AW coupled with an AI algorithm to detect patients with left ventricular dysfunction with an area under the curve of 0.885.16”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [16]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · see citing works
2013 ACCF/AHA Guideline for the Management of Heart Failure
Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Drazner, M. H., Fonarow, G. C., Geraci, S. A., Horwich, T., Januzzi, J. L., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.05.019 · Cited by 12,536 · see citing works
Pfeffer, M. A., Braunwald, E., Moyé, L. A., Basta, L., Brown, E. J., Cuddy, T. E., Davis, B. R., Geltman, E. M., Goldman, S., Flaker, G. C., et al. (1992) · New England Journal of Medicine · doi:10.1056/nejm199209033271001 · Cited by 5,911 · see citing works
Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., de Ferranti, S., Després, J.-P., Fullerton, H. J., Howard, V. J., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000152 · Cited by 5,797 · see citing works
Priori, S. G., Blomström-Lundqvist, C., Mazzanti, A., Blom, N., Borggrefe, M., Camm, J., Elliott, P. M., Fitzsimons, D., Hatala, R., Hindricks, G., et al. (2015) · European Heart Journal · doi:10.1093/eurheartj/ehv316 · Cited by 3,893 · see citing works
2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure
Yancy, C. W., Jessup, M. L., Bozkurt, B., Butler, J., Casey, D. E., Colvin, M. M., Drazner, M. H., Filippatos, G. S., Fonarow, G. C., Givertz, M. M., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.04.025 · Cited by 3,476 · see citing works
Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Colvin, M. M., Drazner, M. H., Filippatos, G. S., Fonarow, G. C., Givertz, M. M., et al. (2017) · Circulation · doi:10.1161/cir.0000000000000509 · Cited by 3,109 · see citing works
Forecasting the Impact of Heart Failure in the United States
Heidenreich, P. A., Albert, N. M., Allen, L. A., Bluemke, D. A., Butler, J., Fonarow, G. C., Ikonomidis, J. S., Khavjou, O., Konstam, M. A., Maddox, T. M., et al. (2013) · Circulation Heart Failure · doi:10.1161/hhf.0b013e318291329a · Cited by 2,802 · see citing works
Al‐Khatib, S. M., Stevenson, W. G., Ackerman, M. J., Bryant, W. J., Callans, D. J., Curtis, A. B., Deal, B. J., Dickfeld, T. M., Field, M. E., Fonarow, G. C., et al. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.10.054 · Cited by 1,883 · see citing works
Quiñones, M. A., Waggoner, A. D., Reduto, L. A., Nelson, J. G., Young, J. B., Winters, W. L., RIBEIRO, L. G. T., & Miller, R. R. (1981) · Circulation · doi:10.1161/01.cir.64.4.744 · Cited by 736 · see citing works
Al‐Khatib, S. M., Stevenson, W. G., Ackerman, M. J., Bryant, W. J., Callans, D. J., Curtis, A. B., Deal, B. J., Dickfeld, T. M., Field, M. E., Fonarow, G. C., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.10.053 · Cited by 685 · see citing works
Max-pooling convolutional neural networks for vision-based hand gesture recognition
Nagi, J., Ducatelle, F., Di, G. A., Ciresan, D. C., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., & Gambardella, L. M. (2011) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa.2011.6144164 · Cited by 667 · see citing works
Al‐Khatib, S. M., Stevenson, W. G., Ackerman, M. J., Bryant, W. J., Callans, D. J., Curtis, A. B., Deal, B. J., Dickfeld, T. M., Field, M. E., Fonarow, G. C., et al. (2017) · Heart Rhythm · doi:10.1016/j.hrthm.2017.10.036 · Cited by 639 · see citing works
The precordial electrocardiogram
Wilson, F. N., Johnston, F. D., Rosenbaum, F. F., et al. (1944) · American Heart Journal · doi:10.1016/s0002-8703(44)90603-4 · Cited by 543 · see citing works
Jaggi, P., Mejías, A., Tremoulet, A. H., Burns, J. C., Wang, W., & Ramilo, O. (2015) · Circulation · doi:10.1161/circ.131.suppl_2.o29 · Cited by 468 · see citing works
Al-Khatib, S. M., Stevenson, W. G., Ackerman, M. J., Bryant, W. J., Callans, D. J., Curtis, A. B., Deal, B. J., Dickfeld, T., Field, M. E., Fonarow, G. C., et al. (2018) · Circulation · doi:10.1161/cir.0000000000000548 · Cited by 412 · see citing works
Costello‐Boerrigter, L. C., Boerrigter, G., Redfield, M. M., Rodeheffer, R. J., Urban, L. H., Mahoney, D. W., Jacobsen, S. J., Heublein, D. M., & Burnett, J. C. J. (2006) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2005.09.025 · Cited by 371 · see citing works
Russo, A. M., Stainback, R. F., Bailey, S. R., Epstein, A. E., Heidenreich, P. A., Jessup, M. L., Kapa, S., Kremers, M. S., Lindsay, B. D., & Stevenson, L. W. (2013) · Heart Rhythm · doi:10.1016/j.hrthm.2013.01.008 · Cited by 348 · see citing works
Self-citationRusso, A. M., Stainback, R. F., Bailey, S. R., Epstein, A. E., Heidenreich, P. A., Jessup, M. L., Kapa, S., Kremers, M. S., Lindsay, B. D., & Stevenson, L. W. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.12.017 · Cited by 347 · see citing works
Self-citationPlasma Brain Natriuretic Peptide to Detect Preclinical Ventricular Systolic or Diastolic Dysfunction
Redfield, M. M., Rodeheffer, R. J., Jacobsen, S. J., Mahoney, D. W., Bailey, K. R., & Burnett, J. C. J. (2004) · Circulation · doi:10.1161/01.cir.0000130845.38133.8f · Cited by 260 · see citing works
Priori, S. G., Blomström‐Lundqvist, C., Mazzanti, A., et al. (2016) · Utrecht University Repository (Utrecht University) · Cited by 214 · see citing works
Betti, I., Castelli, G., Barchielli, A., Beligni, C., Boscherini, V., Luca, L. D., Messeri, G., Gheorghiade, M., Maisel, A., & Zuppiroli, A. (2009) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2008.12.002 · Cited by 109 · see citing works
Automated histological classification of whole-slide images of gastric biopsy specimens
Yoshida, H., Shimazu, T., Kiyuna, T., et al. (2017) · Gastric Cancer · doi:10.1007/s10120-017-0731-8 · Cited by 103 · see citing works
Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles
Pasquier, M., Quek, C. H., & Toh, M. (2001) · Neural Networks · doi:10.1016/s0893-6080(01)00048-x · Cited by 64 · see citing works
Bhalla, V., Isakson, S. R., BHALLA, M., LIN, J., Clopton, P., Gardetto, N. J., & Maisel, A. (2005) · American Journal of Hypertension · doi:10.1016/j.amjhyper.2004.11.044 · Cited by 63 · see citing works
Yasin, O. Z., Attia, Z. I., Dillon, J. J., DeSimone, C. V., Sapir, Y., Dugan, J. L., Somers, V. K., Ackerman, M. J., Asirvatham, S. J., Scott, C. G., et al. (2017) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2017.06.008 · Cited by 50 · see citing works
Self-citationScreening for Asymptomatic Left Ventricular Dysfunction Using B‐Type Natriuretic Peptide
McDonagh, T. A., McDonald, K., & Maisel, A. S. (2008) · Congestive Heart Failure · doi:10.1111/j.1751-7133.2008.tb00002.x · Cited by 35 · see citing works
Providing Cardiology Care in Rural Areas Through Visiting Consultant Clinics
Gruca, T. S., Pyo, T.-H., & Nelson, G. C. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.115.002909 · Cited by 35 · see citing works
T-wave morphology abnormalities in benign, potent, and arrhythmogenic Ikr inhibition
Couderc, J., Xia, X., Peterson, D. R., et al. (2011) · Heart Rhythm · doi:10.1016/j.hrthm.2011.02.005 · Cited by 33 · see citing works
Identification of Concealed and Manifest Long QT Syndrome Using a Novel T Wave Analysis Program
Sugrue, A. M., Noseworthy, P. A., Křemen, V., Bos, J. M., Qiang, B., Rohatgi, R. K., Sapir, Y., Attia, Z. I., Brady, P. A., Asirvatham, S. J., et al. (2016) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.115.003830 · Cited by 28 · see citing works
Self-citationRedfield, M. M., Rodeheffer, R. J., Jacobsen, S. J., et al. (2004) · ACC Current Journal Review · doi:10.1016/j.accreview.2004.08.099 · Cited by 22 · see citing works
Detection and extraction of the ECG signal parameters
GholamHosseini, H., & Nazeran, H. (2002) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.1998.745846 · Cited by 20 · see citing works
A new electrode placement method for obtaining 12-lead ECGs
Khan, G. M. (2015) · Open Heart · doi:10.1136/openhrt-2014-000226 · Cited by 17 · see citing works
Three‐Dimensional Echocardiography in Evaluation of Left Ventricular Indices
Yamani, H. N., Cai, Q., & Ahmad, M. (2012) · Echocardiography · doi:10.1111/j.1540-8175.2011.01526.x · Cited by 15 · see citing works
Location of mammograms ROI's and reduction of false-positive
Salazar-Licea, L. A., Pedraza‐Ortega, J. C., Palma, A. P., et al. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.02.003 · Cited by 14 · see citing works
Wingfield, C., Su, L., Liu, X., Zhang, C., Woodland, P. C., Thwaites, A., Fonteneau, E., & Marslen‐Wilson, W. D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005617 · Cited by 8 · see citing works
Evaluating a Pivot-Based Approach for Bilingual Lexicon Extraction
Kim, J., Kwon, H.-S., & Seo, H.-W. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/434153 · Cited by 7 · see citing works
Wingfield, C., Su, L., Liu, X., Zhang, C., Woodland, P. C., Thwaites, A., Fonteneau, E., & Marslen‐Wilson, W. D. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/074799 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31721-0 · Cited by 1,613 · see citing works
Self-citationSmart wearable devices in cardiovascular care: Where we are and how to move forward
Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., Martin, S. S., Muse, E. D., Turakhia, M. P., Tarakji, K. G., et al. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00522-7 · Cited by 829 · see citing works
Ethics and governance of trustworthy medical artificial intelligence
Zhang, J., & Zhang, Z.-M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02103-9 · Cited by 507 · see citing works
Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01335-4 · Cited by 424 · see citing works
Self-citationKo, 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-citationPrediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0870-z · Cited by 314 · see citing works
Artificial intelligence in surgery
Varghese, C., Harrison, E. M., O’Grady, G., & Topol, E. J. (2024) · Nature Medicine · doi:10.1038/s41591-024-02970-3 · Cited by 287 · see citing works
How to use digital devices to detect and manage arrhythmias: an EHRA practical guide
Svennberg, E., Tjong, F., Goette, A., Akoum, N., Di Biase, L., Bordachar, P., Boriani, G., Burri, H., Conte, G., Deharo, J. C., et al. (2022) · Europace · doi:10.1093/europace/euac038 · Cited by 261 · see citing works
Artificial intelligence-powered electronic skin
Xu, C., Solomon, S. A., & Gao, W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00760-z · Cited by 259 · see citing works
The Integration of Artificial Intelligence into Clinical Practice
Karalis, V. D. (2024) · Applied Biosciences · doi:10.3390/applbiosci3010002 · Cited by 255 · see citing works
Noseworthy, P. A., Attia, Z. I., Brewer, L. C., Hayes, S. N., Yao, X., Kapa, S., Friedman, P. A., & Lopez-Jimenez, F. (2020) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007988 · Cited by 220 · 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
Bachtiger, P., Petri, C. F., Scott, F. E., Ri Park, S., Kelshiker, M. A., Sahemey, H. K., Dumea, B., Alquero, R., Padam, P. S., Hatrick, I. R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00256-9 · Cited by 167 · see citing works
Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives
Niel, O., & Bastard, P. (2019) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2019.05.020 · Cited by 165 · see citing works
Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
Sangha, V., Nargesi, A. A., Dhingra, L. S., Khunte, A., Mortazavi, B. J., Ribeiro, A. H., Banina, E., Adeola, O., Garg, N., Brandt, C. A., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.062646 · Cited by 150 · see citing works
Machine learning in the electrocardiogram
Mincholé, A., Camps, J., Lyon, A., & Rodríguez, B. (2019) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2019.08.008 · Cited by 147 · see citing works
Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography
Kwon, J.-M., Kim, K.-H., Medina-Inojosa, J., Jeon, K.-H., Park, J., & Oh, B.-H. (2020) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2020.04.009 · Cited by 108 · see citing works
Artificial Intelligence-Enabled ECG for Left Ventricular Diastolic Function and Filling Pressure
Lee, E., Ito, S., Miranda, W. R., Lopez-Jimenez, F., Kane, G. C., Asirvatham, S. J., Noseworthy, P. A., Friedman, P. A., Carter, R. E., Borlaug, B. A., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00993-7 · Cited by 106 · see citing works
Self-citationYao, X., McCoy, R. G., Friedman, P. A., Shah, N. D., Barry, B. A., Behnken, E. M., Inselman, J. W., Attia, Z. I., & Noseworthy, P. A. (2020) · American Heart Journal · doi:10.1016/j.ahj.2019.10.007 · Cited by 98 · see citing works
Self-citationNielsen, J. C., Lin, Y.-J., de Oliveira Figueiredo, M. J., Sepehri Shamloo, A., Alfie, A., Boveda, S., Dagres, N., Di Toro, D., Eckhardt, L. L., Ellenbogen, K., et al. (2020) · EP Europace · doi:10.1093/europace/euaa065 · Cited by 98 · see citing works
Khunte, A., Sangha, V., Oikonomou, E. K., Dhingra, L. S., Aminorroaya, A., Mortazavi, B. J., Coppi, A., Brandt, C. A., Krumholz, H. M., & Khera, R. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00869-w · Cited by 95 · see citing works
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y.-F., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009204 · Cited by 74 · see citing works
Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
Dhingra, L. S., Aminorroaya, A., Pedroso, A. F., Khunte, A., Sangha, V., McIntyre, D., Chow, C. K., Asselbergs, F. W., Brant, L. C. C., Barreto, S. M., et al. (2025) · JAMA Cardiology · doi:10.1001/jamacardio.2025.0492 · Cited by 65 · see citing works
Ouyang, D., Theurer, J., Stein, N. R., Hughes, J. W., Elias, P., He, B., Yuan, N., Duffy, G., Sandhu, R. K., Ebinger, J., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00220-0 · Cited by 64 · see citing works
How will machine learning inform the clinical care of atrial fibrillation?
Siontis, K. C., Yao, X., Pirruccello, J. P., Philippakis, A. A., & Noseworthy, P. A. (2020) · Circulation Research · doi:10.1161/circresaha.120.316401 · Cited by 61 · see citing works
Self-citationAdedinsewo, 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-citationPediatric 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
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
Bjerkén, L. V., Rønborg, S. N., Jensen, M. T., Ørting, S. N., & Nielsen, O. W. (2022) · Heart Failure Reviews · doi:10.1007/s10741-022-10283-1 · Cited by 50 · see citing works
Muse, E. D., & Topol, E. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.02.002 · Cited by 50 · see citing works
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
Wagner, P., Mehari, T., Haverkamp, W., & Strodthoff, N. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108525 · Cited by 48 · see citing works
Harmon, D. M., Mangold, K., Baez Suarez, A., Scott, C. G., Murphree, D. H., Malik, A., Attia, Z. I., Lopez-Jimenez, F., Friedman, P. A., Dispenzieri, A., et al. (2023) · JACC: Advances · doi:10.1016/j.jacadv.2023.100612 · Cited by 47 · see citing works
Self-citationTao, L., Zhang, C., Zeng, L., Zhu, S., Li, N., Li, W., Zhang, H., Zhao, Y., Zhan, S., & Ji, H. (2020) · JMIR Medical Informatics · doi:10.2196/16912 · Cited by 42 · see citing works
An 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-citationMarvasti, T. B., Gao, Y., Murray, K. R., Hershman, S., McIntosh, C., & Moayedi, Y. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.009 · Cited by 35 · see citing works
Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms
Chiu, I.-M., Wu, P.-J., Zhang, H., Hughes, J. W., Rogers, A. J., Jalilian, L., Perez, M., Lin, C.-H. R., Lee, C.-T., Zou, J., et al. (2024) · JACC: Clinical Electrophysiology · doi:10.1016/j.jacep.2024.07.023 · Cited by 31 · see citing works
Deep-Learning Model for Screening Sepsis Using Electrocardiography
Kwon, J.-M., Lee, Y. R., Jung, M.-S., Lee, Y.-J., Jo, Y.-Y., Kang, D.-Y., Lee, S. Y., Cho, Y.-H., Shin, J.-H., Ban, J.-H., et al. (2021) · Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine · doi:10.1186/s13049-021-00953-8 · Cited by 28 · see citing works
Mitigating bias in clinical machine learning models
Perez-Downes, J. C., Tseng, A. S., McConn, K. A., Elattar, S. M., Sokumbi, O., Sebro, R. A., Allyse, M. A., Dangott, B. J., Carter, R. E., & Adedinsewo, D. (2024) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-023-01032-0 · Cited by 23 · see citing works
Self-citationA comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)
Ayyub, A., Politis, C., & Usman, M. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110594 · Cited by 18 · see citing works
Toward a Patient-Centered, Data-Driven Cardiology
Ribeiro, A. L., & Oliveira, G. M. M. D. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20190069 · Cited by 18 · see citing works
Tao, R., Zhang, S., Zhang, R., Shen, C., Ma, J., Cui, J., Chen, Y., Wang, B., Li, H., Xie, X., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-90615-x · Cited by 14 · see citing works
Hsieh, P.-H., Lin, C., Lin, C.-S., Liu, W.-T., Lin, T.-K., Tsai, D.-J., Hung, Y.-J., Chen, Y.-H., Lin, C.-Y., Lin, S.-H., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01735-7 · Cited by 11 · see citing works
Lin, C., Kuo, F.-C., Chau, T., Shih, J.-H., Lin, C.-S., Chen, C.-C., Lee, C.-C., & Lin, S.-H. (2024) · Communications Medicine · doi:10.1038/s43856-024-00472-4 · Cited by 8 · see citing works
Naser, J. A., Lee, E., Lopez-Jimenez, F., Noseworthy, P. A., Latif, O. S., Friedman, P. A., Lin, G., Oh, J. K., Scott, C. G., Pislaru, S. V., et al. (2024) · JACC: Advances · doi:10.1016/j.jacadv.2024.101179 · Cited by 8 · see citing works
Self-citationArtificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya
Pandey, A., Keshvani, N., Segar, M. W., Kwon, J.-M., Lee, H. S., Bhograj, C., Mashilane, K. I., Jain, N., Mwiti, W., Wambari, E., et al. (2026) · JAMA Cardiology · doi:10.1001/jamacardio.2026.0908 · Cited by 7 · see citing works
Lopez-Jimenez, F., Alger, H. M., Attia, Z. I., Barry, B., Chatterjee, R., Dolor, R., Friedman, P. A., Greene, S. J., Greenwood, J., Gundurao, V., et al. (2025) · American Heart Journal Plus: Cardiology Research and Practice · doi:10.1016/j.ahjo.2025.100528 · Cited by 6 · see citing works
Self-citationAlexandrino, F. B., Schlesinger, R., Bird, J., Lee, E., Deshmukh, A. J., Nkomo, V. T., Oh, J. K., Noseworthy, P. A., Pellikka, P. A., Attia, Z., et al. (2026) · American Heart Journal · doi:10.1016/j.ahj.2025.107337 · Cited by 5 · see citing works
Self-citationArtificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure
Khurshid, S., Friedman, S. F., Kany, S., D’Souza, V., Roshandelpoor, A., Kosyakovsky, L. B., Chitsazan, M., Cunningham, J. W., Singh, P., Lau, E. S., et al. (2026) · Circulation: Heart Failure · doi:10.1161/circheartfailure.125.013927 · Cited by 2 · see citing works
Tang, Q., Deng, K., Cui, Y., Liu, H., & Qian, B. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s610298
“12,14,16,37,38 By contrast, composite SHD screening and rarer-disease applications such as cardiac amyloidosis remain at an earlier stage, with the literature still dominated by model development, retrospective validation, and limited prospective implementation evidence.”
Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
- Authors
- Attia, 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., Asirvatham, S. J., Scott, C. G., Carter, R. E., & Friedman, P. A.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Friedman, Paul A.
- ORCID
- Venue
- Nature Medicine
- Published
- 2019-01
- Volume
- 25
- Issue
- 1
- Pages
- 70-74
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2901226889
- Cited by
- 1,389
- References
- 42
- Citations by year
- 2026: 245 · 2025: 244 · 2024: 225 · 2023: 196 · 2022: 158 · 2021: 146 · 2020: 124 · 2019: 47 · 2018: 1
- FWCI
- 54.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Mayo Clinic
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- asymptomatic left ventricular dysfunction, cardiac contractile dysfunction, electrocardiogram, artificial intelligence, convolutional neural networks, left ventricular ejection fraction, deep learning, cardiovascular screening, risk stratification, echocardiography
- MeSH terms
- Adult, Aged, Aged, 80 and over, Artificial Intelligence, Electrocardiography, Female, Heart, Humans, Male, Mass Screening, Middle Aged, Myocardial Contraction, ROC Curve, Sensitivity and Specificity, Stroke Volume, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
