Integrating AI-ECG and point-of-care cardiac ultrasound for screening structural heart disease: A proof-of-concept study
Open accessAbstract
Background Early structural heart disease (SHD) detection is crucial for improving prognostic outcomes, but widely accessible screening methods are lacking. The advent of artificial intelligence-enabled electrocardiograms (AI-ECG) and point-of-care cardiac-ultrasonography (POCCUS) offers promising new approaches for patient screening. We explored the feasibility and potential of integrating these innovative technologies into a practical SHD screening framework. Methods Outpatients undergoing ECG at the Mayo Clinic ECG laboratory between November 2023 and February 2024 were pragmatically offered POCCUS, performed by novice operators and reviewed by expert echocardiographers. AI-ECG and POCCUS findings were integrated to assess SHD, including reduced left ventricular systolic function (ejection-fraction<50%), aortic stenosis, and increased left ventricular wall thickness suggestive of cardiac amyloidosis or hypertrophic cardiomyopathy. Operators were blinded to patients' comorbidities and formal echocardiogram results. Results Of 486 patients (median-age 64 years; 49% women), 286 had available formal echocardiography, with 17.5% having SHD. AI-ECG had a 32% positive predictive value (PPV) and a 94% negative predictive value (NPV) to detect any SHD. Adding POCCUS increased the overall PPV to 64% with an NPV of 93%, with an increase in diagnostic accuracy from 67% to 88%. Notably, 89.5% (17/19) of the "false positives" by AI-ECG + POCCUS had less-than-moderate-SHD. Applying the AI-ECG + POCCUS screening workflow on the entire cohort resulted in a number-needed-to-screen of 8 to identify 1 patient requiring formal echocardiography. Conclusions The integration of AI-ECG and POCCUS holds promise as a potentially effective screening method for SHD, facilitating improved patient selection for formal echocardiography.
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Alexandrino et al 2026 found that AI-ECG followed by point-of-care cardiac ultrasound had PPV 32% and NPV 94%; adding POCUS increased PPV to 64%, maintained NPV at 93%, and improved accuracy from 67% to 88%.
“AI-ECG PPV 32% and NPV 94%; adding POCUS increased PPV to 64%, maintained NPV at 93%, and improved accuracy from 67% to 88%”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [35]
In Alexandrino et al., among 286 patients with available reference echocardiography, a two-tier workflow of AI-ECG followed by focused point-of-care cardiac ultrasound increased positive predictive value from 32% to 64%, maintained a negative predictive value of 93%, improved overall diagnostic accuracy from 67% to 88%, and yielded a number-needed-to-screen of 8 to identify 1 patient requiring正式?
“Among 286 patients with available reference echocardiography, the combined strategy increased positive predictive value from 32% to 64% while maintaining a negative predictive value of 93% and improving overall diagnostic accuracy from 67% to 88%, supporting referral enrichment rather than definitive rule-out. Applied to the full pragmatic cohort, this workflow yielded a number-needed-to-screen of”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [35]
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Cites
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-citationZhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034338 · Cited by 1,006 · see citing works
Castaño, A., Narotsky, D. L., Hamid, N. B., Khalique, O. K., Morgenstern, R., DeLuca, A. J., Rubin, J. N., Chiuzan, C. C., Nazif, T. M., Vahl, T. P., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx350 · Cited by 728 · see citing works
d'Arcy, J. L., Coffey, S., Loudon, M. A., Kennedy, A., Pearson-Stuttard, J., Birks, J., Frangou, E., Farmer, A. J., Mant, D., Wilson, J., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw229 · Cited by 724 · see citing works
A wearable cardiac ultrasound imager
Hu, H., Huang, H., Li, M., Gao, X., Yin, L., Qi, R., Wu, R. S., Chen, X., Ma, Y., Shi, K., et al. (2023) · Nature · doi:10.1038/s41586-022-05498-z · Cited by 644 · see citing works
Diagnosis and Evaluation of Hypertrophic Cardiomyopathy
Maron, B. J., Desai, M. Y., Nishimura, R. A., Spirito, P., Rakowski, H., Towbin, J. A., Rowin, E. J., Maron, M. S., & Sherrid, M. V. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.12.002 · Cited by 628 · see citing works
Díaz-Gómez, J. L., Mayo, P. H., & Koenig, S. J. (2021) · New England Journal of Medicine · doi:10.1056/nejmra1916062 · Cited by 593 · see citing works
Vahanian, A. S., Beyersdorf, F., Praz, F., Milojevic, M., Baldus, S. E., Bauersachs, J. K., Capodanno, D., Conradi, L., Bonis, M. D., Paulis, R. D., et al. (2022) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2022.05.006 · Cited by 544 · 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-citationKobal, S. L., Trento, L. U., Baharami, S., Tolstrup, K., Naqvi, T. Z., Cercek, B., Neuman, Y., Mirocha, J., Kar, S., Forrester, J. S., et al. (2005) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2005.05.060 · Cited by 388 · see citing works
Patisiran Treatment in Patients with Transthyretin Cardiac Amyloidosis
Maurer, M. S., Kale, P. P., Fontana, M., Berk, J. L., Grogan, M. A., Gustafsson, F., Hung, R. R., Gottlieb, R., Damy, T., González‐Duarte, A., et al. (2023) · New England Journal of Medicine · doi:10.1056/nejmoa2300757 · Cited by 352 · see citing works
Généreux, P., Stone, G. W., O’Gara, P. T., Marquis‐Gravel, G., Redfors, B., Giustino, G., Pîbarot, P., Bax, J. J., Bonow, R. O., & Leon, M. B. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.02.057 · Cited by 299 · see citing works
Electrocardiogram screening for aortic valve stenosis using artificial intelligence
Cohen-Shelly, M., Attia, Z. I., Friedman, P. A., Ito, S., Essayagh, B. A., Ko, W.-Y., Murphree, D. H., Michelena, H. I., Enriquez-Sarano, M., Carter, R. E., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab153 · Cited by 252 · see citing works
Self-citationThe Epidemiology of Asymptomatic Left Ventricular Systolic Dysfunction: Implications for Screening
Wang, T. J., Levy, D., Benjamin, E. J., & Vasan, R. S. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-11-200306030-00012 · Cited by 229 · 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-citationHand-carried Ultrasound Performed by Hospitalists: Does It Improve the Cardiac Physical Examination?
Martin, L. D., Howell, E. E., Ziegelstein, R. C., Martire, C., Whiting-O'Keefe, Q. E., Shapiro, E. P., & Hellmann, D. B. (2008) · The American Journal of Medicine · doi:10.1016/j.amjmed.2008.07.022 · Cited by 145 · see citing works
Detecting structural heart disease from electrocardiograms using AI
Poterucha, T. J., Jing, L., Ricart, R. P., Adjei-Mosi, M., Finer, J., Hartzel, D., Kelsey, C., Long, A., Rocha, D., Ruhl, J. A., et al. (2025) · Nature · doi:10.1038/s41586-025-09227-0 · Cited by 116 · see citing works
The direct health-care burden of valvular heart disease: evidence from US national survey data
Moore, M., Chen, J., Mallow, P. J., & Rizzo, J. A. (2016) · ClinicoEconomics and Outcomes Research · doi:10.2147/ceor.s112691 · Cited by 99 · see citing works
Kashou, A. H., Medina-Inojosa, J. R., Noseworthy, P. A., Rodeheffer, R. J., Lopez-Jimenez, F., Attia, I. Z., Kapa, S., Scott, C. G., Lee, A. T., Friedman, P. A., et al. (2021) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2021.02.029 · Cited by 66 · see citing works
Self-citationThoenes, M., Bramlage, P., Zamorano, P., Messika–Zeitoun, D., Wendt, D., Kasel, M., Kurucova, J., & Steeds, R. P. (2018) · Journal of Thoracic Disease · doi:10.21037/jtd.2018.09.02 · Cited by 61 · see citing works
Echocardiographic Screening of the General Population and Long-term Survival
Lindekleiv, H., Løchen, M., Mathiesen, E. B., Njølstad, I., Wilsgaard, T., & Schirmer, H. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.8412 · Cited by 53 · see citing works
Gaspari, R., Teran, F., Kamilaris, A., & Gleeson, T. (2021) · Resuscitation Plus · doi:10.1016/j.resplu.2021.100097 · Cited by 9 · see citing works
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Varzideh, F., Pande, S., Jankauskas, S. S., Mone, P., Kansakar, U., & Santulli, G. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1788653 · Cited by 4 · see citing works
Tang, Q., Deng, K., Cui, Y., Liu, H., & Qian, B. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s610298
Artificial Intelligence–Enabled Acquisition and Interpretation for Screening Aortic Stenosis
Lee, E., Naser, J. A., Kane, C. J., Kovac, J. L., Greason, C. M., Killalea, M. M., Gulati, M. A., Jackson, J. I., Borgeson, J., Schonfeld, D. A., et al. (2026) · JAMA Cardiology · doi:10.1001/jamacardio.2026.3829
Self-citationStelmakh, O. A., & Kravchenko, А. М. (2026) · Ukrainian Scientific Medical Youth Journal · doi:10.32345/usmyj.3(164).2026.128-138
Hernandez, M. A. M., López-Jiménez, F. A., Poterucha, T. J., Liu, K., Medina‐Inojosa, J. R., Tsaban, G., Attia, Z. I., Enriquez-Sarano, M. L., Oh, J. K., Asirvatham, S. J., et al. (2026) · Journal of the Heart Valve Society · doi:10.1177/30494826261488198
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- Authors
- Alexandrino, 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., Lopez-Jimenez, F., Friedman, P. A., Kane, G. C., Pislaru, S. V., & Tsaban, G.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Tsaban, Gal
- ORCID
- Venue
- American Heart Journal
- Publisher
- Elsevier BV
- Published
- 2026-04
- Volume
- 294
- Pages
- 107337
- ISSN
- 0002-8703 (ISSN-L)
- OpenAlex
- W7117763061
- Cited by
- 5
- References
- 23
- Citations by year
- 2026: 5
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- American Heart Journal (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- AI-ECG, aortic stenosis, left ventricular systolic dysfunction, left ventricular ejection fraction, cardiac amyloidosis, hypertrophic cardiomyopathy, left ventricular wall thickness, number needed to screen, diagnostic accuracy, outpatients
- MeSH terms
- Proof of Concept Study, Aged, Artificial Intelligence, Echocardiography, Electrocardiography, Feasibility Studies, Female, Heart Diseases, Humans, Male, Mass Screening, Middle Aged, Predictive Value of Tests, Point-of-Care Systems
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