Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya
Open accessAbstract
Importance Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited access to echocardiography. Artificial intelligence electrocardiogram (AI-ECG) algorithms have demonstrated promise for identifying left ventricular systolic dysfunction (LVSD), but their feasibility in resource-constrained settings remains unknown. Objective To determine the frequency of patients in Kenya with a high probability of LVSD by AI-ECG and assess AI-ECG algorithm performance against the gold standard of echocardiography. Design, setting, and participants This was a cross-sectional study with enrollment from June to December 2024. Participants underwent baseline assessment and 12-lead ECG, and a subset completed echocardiography within 7 days. The echocardiography subset included participants from 3 prespecified risk strata: those with prior cardiovascular disease, those at high cardiovascular risk (Framingham Risk Score [FRS] ≥10%), and those at low risk (FRS Exposure Risk of LVSD was identified using a validated convolutional neural network AI-ECG algorithm (AiTiALVSD). Main outcomes and measures Key outcomes were the diagnostic performance (sensitivity, specificity, and positive and negative predictive values) of the AI-ECG algorithm for detecting LVSD (LVEF Results Among 1444 participants (mean [SD] age, 59.0 [16.7] years; 907 [62.8%] female; 1118 [77.4%] at high risk), LVSD was identified in 204 (14.1%). The AI-ECG…
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 highly sensitive AI-ECG could help prioritize limited imaging resources for patients most likely to benefit from confirmatory echocardiography.
“This type of performance is clinically relevant because a highly sensitive AI-ECG could help prioritize limited imaging resources for patients most likely to benefit from confirmatory echocardiography.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [23]
An AI-ECG algorithm for detecting left ventricular systolic dysfunction in Kenyan outpatient facilities had high sensitivity and negative predictive value compared with echocardiography.
“A recent study in Kenyan outpatient facilities evaluated an AI-ECG algorithm for detecting left ventricular systolic dysfunction and reported high sensitivity and negative predictive value compared with echocardiography [23].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [23]
Validation in Kenyan outpatient facilities illustrates that AI-ECG could help prioritize scarce echocardiography and specialist resources and may extend cardiovascular screening beyond tertiary centers in settings where treatable disease is frequently recognized late.
“Validation in Kenyan outpatient facilities illustrates how AI-ECG could help prioritize scarce echocardiography and specialist resources, and it may extend cardiovascular screening beyond tertiary centers in settings where treatable disease is frequently recognized late [23].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [23]
Cites
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019
Roth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., Barengo, N. C., Beaton, A. Z., Benjamin, E. J., Benziger, C. P., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.010 · Cited by 11,449 · see citing works
Lang, R., Badano, L., Mor-Avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F., Foster, E., Goldstein, S., Kuznetsova, T., et al. (2015) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2014.10.003 · Cited by 10,093 · see citing works
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491 · see citing works
Global burden of heart failure: a comprehensive and updated review of epidemiology
Savarese, G., Becher, P. M., Lund, L. H., Seferovic, P. M., Rosano, G. M. C., & Coats, A. J. S. (2022) · Cardiovascular Research · doi:10.1093/cvr/cvac013 · Cited by 3,482 · see citing works
2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure
Heidenreich, P. A., Bozkurt, B., Aguilar, D., Allen, L. A., Byun, J. J., Colvin, M. M., Deswal, A., Drazner, M. H., Dunlay, S. M., Evers, L. R., et al. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.12.012 · Cited by 2,862 · see citing works
Mitchell, C., Rahko, P. S., Blauwet, L. A., Canaday, B., Finstuen, J. A., Foster, M., Horton, K. D., Ogunyankin, K. O., Palma, R. A., & Velazquez, E. J. (2018) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2018.06.004 · Cited by 2,571 · 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
Ledwidge, M., Gallagher, J., Conlon, C., Tallon, E., O’Connell, E., Dawkins, I., Watson, C., O’Hanlon, R., Bermingham, M., Patle, A., et al. (2013) · JAMA · doi:10.1001/jama.2013.7588 · Cited by 664 · see citing works
Natural history of asymptomatic left ventricular systolic dysfunction in the community
Wang, T. J., Evans, J. C., Benjamin, E. J., Levy, D., LeRoy, E. C., & Vasan, R. S. (2003) · Circulation · doi:10.1161/01.cir.0000085166.44904.79 · Cited by 633 · 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
Dokainish, H., Teo, K. K., Zhu, J., Roy, A., AlHabib, K. F., ElSayed, A., Palileo-Villaneuva, L., López-Jaramillo, P. A., Karaye, K. M., Yusoff, K., et al. (2017) · The Lancet Global Health · doi:10.1016/s2214-109x(17)30196-1 · Cited by 411 · see citing works
Huelsmann, M., Neuhold, S., Resl, M., Strunk, G., Brath, H., Francesconi, C., Adlbrecht, C., Prager, R., Luger, A., Pacher, R., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.05.069 · Cited by 385 · see citing works
Tromp, J., Bamadhaj, S., Cleland, J. G. F., Angermann, C. E., Dahlström, U., Ouwerkerk, W., Tay, W. T., Dickstein, K., Ertl, G., Hassanein, M., et al. (2020) · The Lancet Global Health · doi:10.1016/s2214-109x(20)30004-8 · Cited by 219 · see citing works
Heart failure in sub-Saharan Africa: A contemporaneous systematic review and meta-analysis
Agbor, V. N., Essouma, M., Ntusi, N., Nyaga, U. F., Bigna, J. J., & Noubiap, J. J. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2017.12.048 · Cited by 200 · see citing works
Heart Failure in Africa, Asia, the Middle East and South America: The INTER-CHF study
Dokainish, H., Teo, K. K., Zhu, J., Roy, A., AlHabib, K. F., ElSayed, A., Palileo‐Villanueva, L. M., López-Jaramillo, P. A., Karaye, K. M., Yusoff, K., et al. (2015) · International Journal of Cardiology · doi:10.1016/j.ijcard.2015.11.183 · Cited by 190 · see citing works
Cardiovascular diseases in Africa in the twenty-first century: Gaps and priorities going forward
Minja, N. W., Nakagaayi, D., Aliku, T. O., Zhang, W., Ssinabulya, I., Nabbaale, J., Amutuhaire, W., de Loizaga, S. R., Ndagire, E., Rwebembera, J., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1008335 · Cited by 183 · 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
Epidemiology of heart failure in sub-Saharan Africa
Ntusi, N., & Mayosi, B. M. (2009) · Expert Review of Cardiovascular Therapy · doi:10.1586/14779072.7.2.169 · Cited by 138 · see citing works
Kwon, J., Kim, K., Jeon, K., Kim, H. M., Kim, M. J., Lim, S., Song, P. S., Park, J., Choi, R. K., & Oh, B. (2019) · Korean Circulation Journal · doi:10.4070/kcj.2018.0446 · Cited by 133 · see citing works
Self-citationSliwa, K. S., Davison, B. A., Mayosi, B. M., Damasceno, A., Sani, M. U., Ogah, O. S., Mondo, C. K., Ojji, D. B., Dzudié, A., Kouam, C. K., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht393 · Cited by 122 · see citing works
Vaid, A., Johnson, K. W., Badgeley, M. A., Somani, S. S., Bicak, M., Landi, I., Russak, A. J., Zhao, S., Levin, M. A., Freeman, R., et al. (2021) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2021.08.004 · Cited by 120 · see citing works
Cho, J., Lee, B., Kwon, J., Lee, Y., Park, H., Oh, B., Jeon, K., Park, J., & Kim, K. (2020) · ASAIO Journal · doi:10.1097/mat.0000000000001218 · Cited by 92 · see citing works
Self-citationKashou, 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
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
Tackling the Growing Burden of Cardiovascular Diseases in Sub-Saharan Africa
Amegah, A. K. (2018) · Circulation · doi:10.1161/circulationaha.118.037367 · Cited by 60 · see citing works
Capacity for diagnosis and treatment of heart failure in sub-Saharan Africa
Carlson, S., Duber, H. C., Achan, J., Ikilezi, G., Mokdad, A. H., Stergachis, A. S., Wollum, A., Bukhman, G., & Roth, G. A. (2017) · Heart · doi:10.1136/heartjnl-2016-310913 · Cited by 59 · 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
Patel, K. V., Segar, M. W., Klonoff, D. C., Khan, M. S., Usman, M. S., Lam, C. S. P., Verma, S., DeFilippis, A. P., Nasir, K., Bakker, S. J. L., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.067530 · Cited by 38 · see citing works
Self-citationHarmon, D. M., Carter, R. E., Cohen-Shelly, M., Svatikova, A., Adedinsewo, D. A., Noseworthy, P. A., Kapa, S., Lopez-Jimenez, F., Friedman, P. A., & Attia, Z. I. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac028 · Cited by 35 · see citing works
Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy
Jung, Y. M., Kang, S., Son, J. M., Lee, H. S., Han, G. I., Yoo, A.-H., Kwon, J., Park, C., Park, J. S., Jun, J. K., et al. (2023) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2023.101184 · Cited by 27 · see citing works
Self-citationWekesah, F. M., Mutua, M. K., Boateng, D., Grobbee, D. E., Asiki, G., Kyobutungi, C. K., & Klipstein‐Grobusch, K. (2020) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2020.100521 · Cited by 26 · see citing works
Wagner, R. G., Crowther, N. J., Micklesfield, L. K., Boua, P. R., Nonterah, E. A., Mashinya, F., Mohamed, S. F., Asiki, G., Tollman, S. M., Ramsay, M., et al. (2021) · BMJ Global Health · doi:10.1136/bmjgh-2020-003499 · Cited by 24 · see citing works
Lakshmanan, S., & Mbanze, I. (2023) · European Heart Journal - Imaging Methods and Practice · doi:10.1093/ehjimp/qyad005 · Cited by 21 · 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-citationSurendra, K., Nürnberg, S., Bremer, J. P., Knorr, M. S., Ückert, F., Wenzel, J., der Kellen, R. B., Westermann, D., Schnabel, R. B., Twerenbold, R., et al. (2022) · ESC Heart Failure · doi:10.1002/ehf2.14263 · Cited by 9 · see citing works
Park, S., Lee, H. J., Song, S., Woo, K., Kim, J., Kim, J., Kim, J. Y., Park, S., On, Y. K., & Park, K. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14124257 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Lee, M.-J., & Jung, H. (2026) · Diagnostics · doi:10.3390/diagnostics16162587 · 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
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
Self-citationWhen Discrimination Transports But the Operating Point Does Not
Lin, Y.-C., Huang, W., Lavie, C. J., & Lin, G. (2026) · JACC Advances · doi:10.1016/j.jacadv.2026.103143
Lee, H. S., Kwon, J.-M., Kang, S., Rhee, T., & Lee, H. (2026) · JACC Advances · doi:10.1016/j.jacadv.2026.103136
Self-citationKim, K. S., Son, J. M., Lee, H. S., Seol, S., Kang, S., Kwon, J., Lee, M. S., & Kim, K. (2026) · Diagnostics · doi:10.3390/diagnostics16182887
Self-citationLee, H. S., Segar, M. W., Usman, M. S., Lee, M. S., Kwon, J., Kim, K., Lee, S.-P., de Oliveira, C. R. A., & Pandey, A. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.102574
Self-citation
- Authors
- Pandey, A., Keshvani, N., Segar, M. W., Kwon, J.-M., Lee, H. S., Bhograj, C., Mashilane, K. I., Jain, N., Mwiti, W., Wambari, E., Nguchu, H., Wagana-Muriithi, L. N., Anyira, E., Namasaka, P., Mbau, L., Wairagu, A., Muthui-Mutua, B., Bikoro, M., Mwita, M. C. R., Njeri, I., Gituma, B., Mbogo, D., Ngolobe, A., Nabiswa, H., & Samia, B.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Heart Hospital Baylor Plano (US)
- Department of Cardiology, Texas Heart Institute, Houston, Texas
- Medical AI Co, Seoul, Korea
- Tricog Health Inc, Bengaluru, India
- AstraZeneca (United Kingdom) (GB)
- Kenyatta National Hospital (KE)
- The Mountain Springs Medical Centre, Nyeri, Kenya
- Masinde Muliro University of Science and Technology (KE)
- Jomo Kenyatta University of Agriculture and Technology (KE)
- Kenya Cardiac Society, Nairobi, Kenya
- Gatundu Referral Hospital, Gatundu, Kenya
- Mama Lucy Referral Hospital, Nairobi, Kenya
- Chogoria Mission Hospital, Chogoria, Kenya
- Kiambu Referral Hospital, Kiambu, Kenya
- Mbagathi Referral Hospital, Nairobi, Kenya
- Thika Referral Hospital, Thika, Kenya
- M.P. Shah Hospital Nairobi, Kenya
- ORCID
- Venue
- JAMA Cardiology
- Publisher
- American Medical Association (AMA)
- Published
- 2026-06-01
- Volume
- 11
- Issue
- 6
- Pages
- 554
- ISSN
- 2380-6583 (ISSN-L)
- OpenAlex
- W7160404391
- Cited by
- 7
- References
- 37
- Citations by year
- 2026: 7
- FWCI
- 34.71
- Type
- journal article
- Language
- en
- Open access
- Locations
- JAMA Cardiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- 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
- left ventricular systolic dysfunction, heart failure with reduced ejection fraction, convolutional neural networks, echocardiography, diagnostic accuracy, resource-limited settings, cardiovascular risk, Framingham risk score, Kenya, early detection of heart failure, positive predictive value, sensitivity and specificity, high cardiovascular risk
- MeSH terms
- Adult, Algorithms, Artificial Intelligence, Cross-Sectional Studies, Echocardiography, Electrocardiography, Female, Humans, Kenya, Male, Middle Aged, ROC Curve, Ventricular Dysfunction, Left
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
