Artificial Intelligence-Enabled ECG for Left Ventricular Diastolic Function and Filling Pressure
Open accessAbstract
Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645–1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298–1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.
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AI-ECG has been used to predict left ventricular diastolic dysfunction and increased filling pressures.
“More recent studies have extended this concept by using AI-ECG to predict left ventricular diastolic dysfunction and increased filling pressures [25] and HFpEF-related risk [26].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [25]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Empagliflozin in heart failure with a preserved ejection fraction
Anker, S. D., Butler, J., Filippatos, G., Ferreira, J. P., Bocchi, E., Böhm, M., Brunner–La Rocca, H.-P., Choi, D.-J., Chopra, V., Chuquiure-Valenzuela, E., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2107038 · Cited by 4,874 · see citing works
Trends in Prevalence and Outcome of Heart Failure with Preserved Ejection Fraction
Owan, T., Hodge, D. O., Herges, R. M., Jacobsen, S. J., Roger, V. L., & Redfield, M. M. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa052256 · Cited by 4,191 · see citing works
Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction
Solomon, S. D., McMurray, J. J. V., Claggett, B., de Boer, R. A., DeMets, D., Hernandez, A. F., Inzucchi, S. E., Kosiborod, M. N., Lam, C. S. P., Martinez, F., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2206286 · Cited by 2,884 · see citing works
Nagueh, S. F., Smiseth, O. A., Appleton, C. P., Byrd, B. F., Dokainish, H., Edvardsen, T., Flachskampf, F. A., Gillebert, T. C., Klein, A. L., Lancellotti, P., et al. (2016) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jew082 · Cited by 2,471 · see citing works
Self-citationPieske, B. M., Tschöpe, C., de Boer, R. A., Fraser, A. G., Anker, S. D., Donal, E., Edelmann, F., Fu, M. L. X., Guazzi, M., Lam, C. S. P., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz641 · Cited by 1,906 · see citing works
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-citationThe Lancet women and cardiovascular disease Commission: reducing the global burden by 2030
Vogel, B., Mónica Acevedo, B., Appelman, Y., Merz, C. N. B., Chieffo, A., Figtree, G. A., Guerrero, M. E., Kunadian, V., Lam, C. S. P., Maas, A. H. E. M., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)00684-x · Cited by 1,536 · see citing works
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., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389 · see citing works
Self-citationThe pathophysiology of heart failure with preserved ejection fraction
Borlaug, B. A. (2014) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2014.83 · Cited by 726 · see citing works
Self-citationEvaluation and management of heart failure with preserved ejection fraction
Borlaug, B. A. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0363-2 · Cited by 675 · see citing works
Self-citationHeart Failure With Preserved Ejection Fraction: A Review
Redfield, M. M., & Borlaug, B. A. (2023) · JAMA · doi:10.1001/jama.2023.2020 · Cited by 662 · see citing works
Self-citationObokata, M., Kane, G. C., Reddy, Y. N. V., Olson, T. P., Melenovsky, V., & Borlaug, B. A. (2017) · Circulation · doi:10.1161/circulationaha.116.024822 · Cited by 559 · 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-citationSmiseth, O. A., Morris, D. A., Cardim, N. M., Čikeš, M., Delgado, V., Donal, E., Flachskampf, F. A., Galderisi, M., Gerber, B. L., Gimelli, A., et al. (2021) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeab154 · Cited by 355 · see citing works
Self-citationLeft Ventricular Diastolic Function
Nagueh, S. F. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.10.038 · Cited by 308 · see citing works
Ha, J., Oh, J. K., Pellikka, P. A., Ommen, S. R., Stussy, V. L., Bailey, K. R., Seward, J. B., & Tajik, A. J. (2005) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2004.08.033 · Cited by 292 · see citing works
Self-citationShah, A. M., Čikeš, M., Prasad, N., Li, G., Getchevski, S., Claggett, B. L., Rizkala, A. R., Lukashevich, I., O’Meara, E., Ryan, J. J., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.09.063 · Cited by 260 · see citing works
Tromp, J., Seekings, P. J., Hung, C.-L., Iversen, M. B., Frost, M. J., Ouwerkerk, W., Jiang, Z., Eisenhaber, F., Goh, R. S. M., Zhao, H., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00235-1 · Cited by 210 · see citing works
Oh, J. K., Ding, Z. P., Gersh, B. J., Bailey, K. R., & Tajik, A. J. (1992) · Journal of the American Society of Echocardiography · doi:10.1016/s0894-7317(14)80041-2 · Cited by 173 · see citing works
Self-citationAlmeida, J. G., Fontes‐Carvalho, R., Sampaio, F., Ribeiro, J., Bettencourt, P., Flachskampf, F. A., Leite-Moreira, A. F., & Azevedo, A. (2017) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jex252 · Cited by 169 · see citing works
Selvaraj, S., Myhre, P. L., Vaduganathan, M., Claggett, B. L., Matsushita, K., Kitzman, D. W., Borlaug, B. A., Shah, A. M., & Solomon, S. D. (2020) · JACC Heart Failure · doi:10.1016/j.jchf.2020.03.013 · Cited by 150 · see citing works
Self-citationDeep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction
Pandey, A., Kagiyama, N., Yanamala, N. V. K., Segar, M. W., Cho, J. S., Tokodi, M., & Sengupta, P. P. (2021) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2021.04.010 · Cited by 133 · see citing works
Kagiyama, N., Piccirilli, M., Yanamala, N. V. K., Shrestha, S., Farjo, P. D., Casaclang‐Verzosa, G., Tarhuni, W. M., Nezarat, N., Budoff, M. J., Narula, J., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.06.061 · Cited by 108 · see citing works
The 2016 Diastolic Function Guideline
Oh, J. K., Miranda, W. R., Bird, J. G., Kane, G. C., & Nagueh, S. F. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.12.004 · Cited by 76 · see citing works
Self-citationChao, C., Kato, N., Scott, C. G., López-Jiménez, F. A., Lin, G., Kane, G. C., & Pellikka, P. A. (2022) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2022.06.013 · Cited by 40 · see citing works
Self-citationMachine Learning Augmented Echocardiography for Diastolic Function Assessment
Fletcher, A. J., Lapidaire, W., & Leeson, P. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.711611 · Cited by 29 · see citing works
Lu, D., Haileselassie, B., Ventoulis, I., Liu, H., Liang, H.-Y., Pozios, I., Canepa, M., Phillip, S., Abraham, M. R., & Abraham, T. P. (2017) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jex134 · Cited by 29 · see citing works
Tsai, D., Lou, Y.-S., Lin, C., Fang, W., Lee, C., Ho, C., Wang, C., & Lin, C. (2023) · Digital Health · doi:10.1177/20552076231187247 · Cited by 26 · see citing works
Gruca, M. M., Slivnick, J. A., Singh, A., Cotella, J. I., Subashchandran, V., Prabhu, D. S., Asch, F. M., Siddiki, M., Gupta, N., Mor‐Avi, V., et al. (2023) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jead231 · Cited by 23 · see citing works
Giudicatti, L. C., Burrows, S., Playford, D. A., Strange, G. A., & Hillis, G. S. (2020) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2020.12.017 · Cited by 15 · see citing works
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Nagueh, S. F., Sanborn, D. M. Y., Oh, J. K., Anderson, B. A., Billick, K., Dérumeaux, G., Klein, A. L., Koulogiannis, K. P., Mitchell, C., Shah, A. M., et al. (2025) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2025.03.011 · Cited by 405 · see citing works
Self-citationSvennberg, E., Han, J. K., Caiani, E. G., Engelhardt, S., Ernst, S., Friedman, P. A., Garcia, R., Ghanbari, H., Hindricks, G., Man, S. H., et al. (2025) · EP Europace · doi:10.1093/europace/euaf071 · Cited by 92 · see citing works
Self-citationRecent Advances in Wearable Healthcare Devices: From Material to Application
Luo, X., Tan, H., & Wen, W. (2024) · Bioengineering · doi:10.3390/bioengineering11040358 · Cited by 57 · see citing works
Kolk, M. Z. H., Ruipérez-Campillo, S., Wilde, A. A. M., Knops, R. E., Narayan, S. M., & Tjong, F. V. Y. (2024) · Heart Rhythm · doi:10.1016/j.hrthm.2024.09.003 · Cited by 53 · see citing works
Artificial intelligence-enhanced patient evaluation: bridging art and science
Oikonomou, E. K., & Khera, R. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae415 · Cited by 34 · see citing works
Sathi, T. A., Jany, R., Ela, R. Z., Azad, A., Alyami, S. A., Hossain, M. A., & Hussain, I. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103381 · Cited by 34 · see citing works
Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography
Vrudhula, A., Vukadinovic, M., Haeffele, C. L., Kwan, A. C., Berman, D. S., Liang, D. H., Siegel, R., Cheng, S. C., & Ouyang, D. (2025) · JAMA Cardiology · doi:10.1001/jamacardio.2025.0498 · Cited by 30 · see citing works
Echocardiographic Diastolic Function Grading in HFpEF
Harada, T., Sorimachi, H., Obokata, M., Naser, J. A., Ibe, T., Tada, A., Doi, S., Kazui, S., Murakami, T., Kagami, K., et al. (2026) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.11.024 · Cited by 30 · see citing works
Self-citationRevolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges
Doolub, G., Khurshid, S. S., Thériault-Lauzier, P., Lapalme, A. N., Tastet, O., So, D. Y. F., Langlais, É. L., Corbin, D., & Avram, R. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.06.011 · Cited by 57 · see citing works
Lehmann, D. H., Gomes, B., Vetter, N., Braun, O., Amr, A., Hilbel, T., Müller, J., Köthe, U., Reich, C., Kayvanpour, E., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00063-3 · Cited by 21 · see citing works
Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review
Leone, D. M., O’Sullivan, D. M., & Jaimes, K. B. (2024) · Children · doi:10.3390/children12010025 · Cited by 23 · see citing works
Predicting Heart Failure From 12-Lead ECGs Using AI
Desai, A. S., Pandey, A., Suratekar, R., Ahmad, F. S., Alger, H. M., Anto, A. G., Nujum G, S., Lopez-Jimenez, F., Oh, J. K., Asfahan, S., et al. (2026) · JACC · doi:10.1016/j.jacc.2025.10.065 · Cited by 16 · see citing works
Self-citationUsing Electrocardiogram to Assess Diastolic Function and Prognosis in Mitral Regurgitation
Tsaban, G., Lee, E., Wopperer, S., Abbasi, M. A., Yu, H. T., Kane, G. C., López-Jiménez, F. A., Pislaru, S. V., Nkomo, V. T., Deshmukh, A. J., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.06.054 · Cited by 16 · see citing works
Self-citationArtificial intelligence-enabled electrocardiography from scientific research to clinical application
Lin, C.-S., Liu, W.-T., Chen, Y.-H., Lin, S.-H., & Lin, C. (2025) · EMBO Molecular Medicine · doi:10.1038/s44321-025-00351-y · Cited by 15 · see citing works
Imaging-based method to quantify left ventricular diastolic pressures
Smiseth, O. A., Fernandes, J. F., Ohte, N., Wakami, K., Donal, E., Remme, E. W., & Lamata, P. (2025) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeaf017 · Cited by 15 · see citing works
Wireless bioelectronic devices for next-generation electrotherapy
Chen, L., Chen, S., Sun, B., Chen, J., & Zhang, Y. (2025) · Cell Biomaterials · doi:10.1016/j.celbio.2025.100054 · Cited by 15 · see citing works
Hong, D., Song, S.-H., Shin, H., Bak, M., Kim, J., Kim, D., Kim, J. Y., Yang, J. H., Park, S.-J., Choi, J.-O., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf080 · Cited by 14 · see citing works
Scalia, I. G., Pathangey, G., Abdelnabi, M. H., Ibrahim, O., Abdelfattah, F. E., Pereyra, M., Ibrahim, R., Farina, J., Banerjee, I., Tamarappoo, B. K., et al. (2025) · Cancers · doi:10.3390/cancers17040605 · Cited by 12 · see citing works
Jentzer, J. C., Lee, E., Attia, Z. I., Hillerson, D., Kane, G. C., López-Jiménez, F. A., Noseworthy, P. A., Friedman, P. A., & Oh, J. K. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.037839 · Cited by 11 · see citing works
Self-citationAI 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
Sawma, T., Arghami, A., Schaff, H. V., Aslahishahri, M., Mangold, K. E., Dearani, J. A., Stulak, J. M., Bagameri, G., Villavicencio, M. A., Greason, K. L., et al. (2025) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2025.06.037 · Cited by 9 · see citing works
Self-citationArtificial intelligence in HFpEF: Diagnosis, prognosis, and management strategies
Yi, J., & Cho, J. S. (2025) · Journal of Cardiology · doi:10.1016/j.jjcc.2025.08.018 · Cited by 9 · see citing works
Symbiopersonal intelligence towards symbiotic and personalized digital medicine
Mensah, A., Bao, Q., Zhang, Z., Ya, C., Jiang, Q., & Cai, P. (2025) · Fundamental Research · doi:10.1016/j.fmre.2025.01.009 · Cited by 8 · see citing works
Zhang, C. H., Yang, Z., & Tang, Y.-D. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004000 · Cited by 8 · see citing works
Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications
Islam, M. S., Kalmady, S. V., Hindle, A., Sandhu, R. K., Sun, W., Sepehrvand, N., Greiner, R., & Kaul, P. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.003 · Cited by 9 · see citing works
A Noninvasive Framework for Heart Function Assessment by Multitask Learning
Mo, H., Liang, J., Li, B., Hu, Z., Yi, M., Wu, H., Rong, Q., & Xu, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3553885 · Cited by 6 · see citing works
Borlaug, B. A., Chirinos, J. A., Lewis, G. D., Redfield, M. M., Bertoni, A. G., Cadeiras, M., Chiamvimonvat, N., Desai, A. S., Givertz, M. M., Kass, D. A., et al. (2025) · JACC Heart Failure · doi:10.1016/j.jchf.2025.102714 · Cited by 6 · see citing works
Self-citationNamjouyan, K., Sejdić, E., Link, M. S., Pelliccia, A., Glicksberg, B. N., Trayanova, N. A., & Krittanawong, C. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm47007 · Cited by 6 · see citing works
Artificial Intelligence-Driven Wearable Sensors for Cardiovascular Health Monitoring
Deng, H. T., Ma, B., Chen, G., Chi, J., Tong, J., Yu, F., Lü, B., Chen, Y., Zhao, C., Zhang, C., et al. (2026) · ACS Sensors · doi:10.1021/acssensors.5c04840 · Cited by 6 · see citing works
Tsai, C., Naser, J. A., Tsaban, G., Lee, E., Wood, J., Luis, A. S., Thaden, J. J., Kane, G. C., Pislaru, S. V., Simard, T. J., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.046558 · Cited by 5 · see citing works
Self-citationHarada, T., Kagami, K., Ishii, H., & Obokata, M. (2026) · Journal of Cardiology · doi:10.1016/j.jjcc.2026.01.014 · Cited by 6 · see citing works
Great debate: artificial intelligence will replace much of what cardiologists do
Averbuch, T., Asselbergs, F. W., Vardas, P. E., & Spall, H. G. C. V. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf305 · Cited by 5 · see citing works
Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review
Lee, H. S., Croon, P. M., Lee, M. S., Poterucha, T. J., Lin, C. H., Son, J. M., Jeon, K. H., Tarabanis, C., Lee, S., Kim, K.-H., et al. (2026) · Korean Circulation Journal · doi:10.4070/kcj.2025.0486 · Cited by 4 · see citing works
Ma, Z., Caldwell, R., Attia, Z. I., Friedman, P. A., Lerman, A., Ng, C. T., & Herrmann, J. (2026) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2026.01.006 · Cited by 4 · see citing works
Self-citationArtificial intelligence and mortality prediction in acute coronary syndromes
Attia, Z. I., & Friedman, P. A. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehae475 · Cited by 4 · see citing works
Self-citationSynthetic generation of cardiac tissue motion from surface electrocardiograms
Radhakrishnan, A., Yanamala, N. V. K., Jamthikar, A. D., Wang, Y., East, S.-A., Hamirani, Y. S., Maganti, K., & Sengupta, P. P. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00629-x · Cited by 5 · see citing works
Vrudhula, A., Vukadinovic, M., Haeffle, C., Kwan, A. C., Berman, D. S., Liang, D. H., Siegel, R., Cheng, S. C., & Ouyang, D. (2024) · medRxiv · doi:10.1101/2024.06.22.24309332 · Cited by 4 · see citing works
Individual hearts: computational models for improved management of cardiovascular disease
van Osta, N., van Loon, T., & Lumens, J. (2025) · Heart · doi:10.1136/heartjnl-2024-324177 · Cited by 3 · see citing works
White, R. D., Demirer, M., Sebro, R., Cortopassi, I. O., Stowell, J. T., McCann, M. R., Barry, T., Appleton, C. P., Helgeson, S. A., & Erdal, B. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-22026-x · Cited by 4 · see citing works
Lim, J., Lee, M. S., Suh, J. H., Kang, S., Lee, H. S., Jang, J.-H., Son, J. M., Kwon, J., Kim, Y.-J., Kim, K., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.046989 · Cited by 3 · see citing works
Xue, Z.-K., Geng, S., Guo, S., Mu, G., Yu, B., Wang, P., Hu, S., Zhang, D., Xu, W., Liu, Y., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02764-0 · Cited by 2 · see citing works
Tran, H. H.-V., Thu, A., Fuertes, A., Twayana, A. R., Mahadevaiah, A., Mehta, K. A., James, M., Basta, M., Weissman, S. I., Frishman, W. H., et al. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000000975 · Cited by 4 · see citing works
Kim, D., Lee, E., Kim, J., Kim, E. K., Chang, S., Park, S. J., Choi, J.-O., On, Y. K., Attia, Z. I., Friedman, P. A., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf067 · Cited by 2 · see citing works
Self-citationYu, H. T., Walker, L. E., Lee, E., Abbasi, M. A., Wopperer, S. B., Tsaban, G., Kopecky, K. F., López-Jiménez, F. A., Friedman, P., Attia, Z. I., et al. (2025) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2025.100652 · Cited by 2 · see citing works
Self-citationGoings, D., Haq, I. U., Arghami, A., Attia, Z. I., Bagameri, G., Brandt, M., Munoz, F. D., Friedman, P. A., Holst, K. A., Noseworthy, P. A., et al. (2025) · Heart Rhythm O2 · doi:10.1016/j.hroo.2025.11.001 · Cited by 2 · see citing works
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Niu, H. (2025) · American Journal of Translational Research · doi:10.62347/gwcj5941 · Cited by 2 · see citing works
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Chao, C., Luis, S. A., Arsanjani, R., & Oh, J. K. (2025) · Current Cardiology Reports · doi:10.1007/s11886-025-02222-x · Cited by 2 · see citing works
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Otto, M. E. B. (2024) · ARQUIVOS BRASILEIROS DE CARDIOLOGIA - IMAGEM CARDIOVASCULAR · doi:10.36660/abcimg.20240095 · Cited by 1 · see citing works
Debertin, J. G., Schaff, H. V., Sawma, T., Aslahishahri, M., Dearani, J. A., Friedman, P. A., Geske, J. B., Ommen, S. R., Attia, Z. I., Lee, E., et al. (2025) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2025.05.015 · Cited by 1 · see citing works
Self-citationECG Report Generation with Diagnostic Knowledge Enhanced Prompt Learning
Fan, P., Yue, X., Chen, Y., Wei, Z., Shi, J., & Xu, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05479-1_2 · Cited by 1 · see citing works
Fujita, W., Sakamoto, A., Sato, E., KANEKO, T., & Kagiyama, N. (2025) · Juntendō Igaku/Juntendo igaku · doi:10.14789/ejmj.jmj25-0019-r · Cited by 1 · see citing works
Predicting decline in left ventricular function after new-onset left bundle branch block
Goings, D., Carey, P. M., L’Esperance, T., Attia, Z. I., Tsaban, G., Noseworthy, P. A., Gersh, B. J., Friedman, P. A., Cha, Y., Deshmukh, A. J., et al. (2025) · Heart Rhythm · doi:10.1016/j.hrthm.2025.11.026 · Cited by 1 · see citing works
Self-citationLao, Y., Ren, H., Ma, Z., Sun, Y., Li, D., Tan, N., Cheng, W., & Jin, W. (2025) · iScience · doi:10.1016/j.isci.2025.114197 · Cited by 1 · see citing works
Hillerson, D., Harmon, D., Lee, E., Oh, J. K., & Jentzer, J. C. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102406 · Cited by 1 · see citing works
Self-citationArtificial Intelligence–Enabled ECG for Diastolic Dysfunction in Congenital Heart Disease
O’Sullivan, D. M., Madhavan, M., Samimi, S., Anjewierden, S., Miranda, W. R., Attia, Z. I., Connolly, H. M., Bravo‐Jaimes, K., Jain, C. C., Friedman, P. A., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102413 · Cited by 1 · see citing works
Self-citationArtificial intelligence in heart failure
Li, X., Liu, Y., Zhang, X., Yang, N., Xu, T. W., Cui, X. W., & Chen, G. (2026) · The Egyptian Heart Journal · doi:10.1186/s43044-026-00723-w · Cited by 1 · see citing works
Lin, W.-C., Li, Y., Chen, Y., Hsiao, T. A., Li, W., Tsai, W., Liu, P. H., & Huang, M. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.045851 · Cited by 1 · see citing works
Ansar, F., Waheed, M. A., Zafar, U., Kolapo, A., SARFARAZ, W., & Rashid, K. (2026) · Health Science Reports · doi:10.1002/hsr2.71855 · Cited by 2 · see citing works
Artificial Intelligence in Heart Failure with Preserved Ejection Fraction
Li, X., Xu, C., Deng, W., Zhang, Y., Liu, C. X., Gao, L. L., Ji, M., He, Q., Wu, Z., Qin, S., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16111597 · Cited by 1 · see citing works
Understanding HFpEF Through Imaging
Harada, T., Naser, J. A., Ghanbari, F., & Borlaug, B. A. (2026) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2026.05.017 · Cited by 2 · see citing works
Self-citationKarabayir, İ., Gilbert, O. N., Valika, A. A., Celık, T., Krishnan, A., Chinthala, L. K., Pandey, A., Soliman, E. Z., Jefferies, J. L., Kitzman, D. W., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.124.041948 · Cited by 1 · see citing works
EDJIMBI, J. A. C., Shah, N. B., Donisi, L., Gajjar, A., See, C., Grimshaw, A. A., Diop, H. B., Ribeiro, A. L. P., Marijon, É., Clifford, G. D., et al. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag130 · Cited by 1 · see citing works
Choi, 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
Zhang, Y., Zhao, C., Zhang, W., Lai, J., Wang, J., Guo, J., Shi, Y., Zhu, X., Feng, Q., & Yang, W. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127039 · Cited by 1 · see citing works
Pitfalls and Opportunities for the Growing Role of AI in Heart Failure
Grewal, J. S., & Sengupta, P. P. (2024) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2024.03.001 · Cited by 1 · see citing works
Tokodi, M., & Kovács, A. D. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.07.064 · Cited by 1 · see citing works
What is the Future of Diagnostics in Heart Failure?
McCoubrey, A., & Campbell, R. T. (2025) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0797 · Cited by 1 · see citing works
Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic FunctionPreprint
Yuan, V., Sahashi, Y., Ieki, H., Vukadinovic, M., Binder, C., Pieszko, K., Ambrosy, A. P., Cheng, P. P., Cheng, S. C., & Ouyang, D. (2025) · medRxiv · doi:10.1101/2025.04.29.25326683 · Cited by 1 · see citing works
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
McColl, H., Kuhasri, A., Chowdhury, S., Wen, Y., Loewenstein, D. E., Schlegel, T. T., Maanja, M., Yang, K. X., Al-Falahi, Z. S., Lindow, T., et al. (2023) · medRxiv · doi:10.1101/2023.04.19.23288666
Zou, R., Li, A., Chen, D., & Li, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4823408/v1
Artificial Intelligence in Echocardiography: The Future of Precision Diagnosis
Otto, M. E. B. (2024) · ARQUIVOS BRASILEIROS DE CARDIOLOGIA - IMAGEM CARDIOVASCULAR · doi:10.36660/abcimg.20240095i
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Fiorina, L., & Cinq‐Mars, A. (2025) · Archives des Maladies du Coeur et des Vaisseaux - Pratique · doi:10.1016/j.amcp.2025.01.008
Lessons from Diastolic Function Assessment After Myocardial Infarction
Oh, J. K., & Lee, E. (2025) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2025.02.016
Self-citationVerlyck, M. A., Zhao, D., Ferdian, E., Creamer, S. A., Quill, G. M., Poppe, K., Gamage, T. P. B., Young, A. A., & Nash, M. P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-94559-5_21
Bismee, N. N., Scalia, I. G., Abbas, M. T., Farina, J., Pereyra, M., Awad, K., Ali, N. B., Javadi, N., Esfahani, S. A., Sheashaa, H., et al. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15080371
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Hiddemann, M., Köhler, K., Haverkamp, W., Köhler, J., Bauser, M., & Köhler, F. (2025) · DMW - Deutsche Medizinische Wochenschrift · doi:10.1055/a-2593-7851
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Wang, Y., Xu, Y., Hu, R., Shen, F., Jiang, H., Wang, J., Chen, J., Chen, D. Z., Wu, J., & Ying, H. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3639986
Heart Failure and Aortic Stenosis: Intertwined Journeys
Pislaru, S. V., & Nkomo, V. T. (2025) · Springer eBooks · doi:10.1007/978-3-031-96562-3_7
Razzaq, U., Docherty, K. F., Ajmi, A. A., Alebraheem, A., Burns, R., Davie, A. P., Gan, J., Hogg, K. J., Jackson, C. E., Mathioudaki, E., et al. (2026) · Open Heart · doi:10.1136/openhrt-2025-003785
Lv, C., Liao, K., Liu, X., & Guo, X. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae3ed4
Artificial intelligence-enabled personalized medicine: strategies and challenges
Chavda, V. P., Vyas, A., Rathore, N., Sheth, T. J., Shah, N., & Shah, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00017-8
Shahzad, H., Sulaiman, H., Roheel, A., Tahir, H. N., Tahir, M., Tariq, M., & Sulaiman, H. (2026) · Exploration of Cardiology · doi:10.37349/ec.2026.101293
Sushma, M. B., Akanksha, K., Rahul., S., Sandhya, T., & Ajay, V. (2026) · International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) · doi:10.1109/icauc68182.2026.11441096
Liu, E., Liu, Y., Wang, Z., Zhou, H., & Wang, X. F. (2026) · Physiological Measurement · doi:10.1088/1361-6579/ae6750
Kalhor, P., Manzato, M., Hellou, E., Nogami, K., Nardi, V., Borlaug, B. A., Lerman, L. O., & Lerman, A. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.047946
Self-citationAwad, K., Farina, J. M., Nicolau, J. N., Lyle, M. A., Rosenbaum, A. N., Boilson, B. A., AbouEzzeddine, O. F., López-Jiménez, F. A., Dispenzieri, A. A., Grogan, M. A., et al. (2026) · Amyloid · doi:10.1080/13506129.2026.2669753
Stenhede, E., Orstad, E. B., Omland, T., Schirmer, H., & Ranjbar, A. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag141
Hsieh, P. N., Agrawal, P., Alok, A., Kumar, S., Ramanathan, C., Murthy, V. L., Varma, N., Nagarajan, V., Ambrosy, A. P., Ramsis, M., et al. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag080
Akkoca, A., Şen, A., & Kutlu, S. (2026) · Archives of Current Medical Research · doi:10.47482/acmr.1790299
Aortic Stenosis Progression: Lessons From Multimodality Imaging
Jain, R., Padang, R., Postalian, A., Dal‐Bianco, J. P., Zweig, B. M., Ito, S., Hodara, R., Levine, R. A., & Oh, J. K. (2026) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2026.06.008
Self-citationTsaban, G., Harari, A., Shiloh, A., Shamia, D., Rokach, L., Gordon, M. M., Haim, M., & Katz, G. (2026) · JACC Advances · doi:10.1016/j.jacadv.2026.102875
Agentic Autodiscovery of Diastolic Dysfunction Phenotypes from Surface ElectrocardiogramPreprint
Jamthikar, A. D., Shanmugham, A., Singh, S., Radhakrishnan, A., Dong, J., Maganti, K., Yanamala, N. V. K., & Sengupta, P. P. (2026) · medRxiv · doi:10.64898/2026.06.17.26355897
Zhao, C., Chan, P. J., Dougherty, S., Fan, Y., & Lee, A. P. (2026) · Open Heart · doi:10.1136/openhrt-2026-004127
Lee, H. S., Kang, S., Lee, M. S., Pandey, A., Kim, M. U., Jang, J.-H., Jo, Y., Lim, J., Son, J. M., Kim, K. S., et al. (2026) · medRxiv · doi:10.64898/2026.07.21.26358539
Jenks, C. (2026) · Artificial intelligence · doi:10.5772/intechopen.1015699
Artificial intelligence for the assessment of diastolic function
Tsang, T. S. M., & Yeung, D. F. (2026) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000001324
Özderya, M., & Gülçebi, F. (2026) · Immunologic Research · doi:10.1007/s12026-026-09818-7
Sawma, T., Schaff, H. V., Danesh, S., Arghami, A., Lee, E., Aslahishahri, M., Stulak, J. M., Greason, K. L., Dearani, J. A., López-Jiménez, F. A., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.126.050492
Self-citation
- Authors
- 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., Attia, Z. I., & Oh, J. K.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Oh, Jae K.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2024-01-06
- Volume
- 7
- Issue
- 1
- Pages
- 4-4
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10770308
- OpenAlex
- W4390630205
- Cited by
- 106
- References
- 32
- Citations by year
- 2026: 42 · 2025: 45 · 2024: 18 · 2023: 1
- FWCI
- 31.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- left ventricular diastolic function, diastolic dysfunction, filling pressure, heart failure with preserved ejection fraction, echocardiography, mortality prediction, prognostic value, heart disease diagnosis
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