Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial
Open accessAbstract
Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05-4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85-3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576.
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Current African evidence does not yet provide a generalizable external-validation evidence base for stand-alone AI-ECG across rural primary care, community screening, or heterogeneous low-resource delivery systems.
“Current African evidence therefore supports the feasibility of AI-assisted screening in facility-based workflows, but does not yet provide a generalizable external-validation evidence base for stand-alone AI-ECG across rural primary care, community screening, or heterogeneous low-resource delivery systems.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [15]
The Discussion (page not available) says the trial enrolled participants at teaching hospitals with cardiologists and echocardiography capabilities, and cautions that LVSD prevalence at these centers may not reflect Nigeria’s general obstetric population. The Methods and Results describe AI-assisted screening in this facility-based obstetric trial, not stand-alone deployment in rural primary care or community screening. This paper supports a limit on generalizing its own findings, but cannot establish the broader claim that current African evidence lacks such an external-validation evidence base. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn the Nigerian trial, the diagnostic gain was primarily driven by the AI-enabled digital-stethoscope pathway rather than the stand-alone 12-lead AI-ECG.
“importantly, as noted earlier, the diagnostic gain in this trial was primarily driven by the AI-enabled digital-stethoscope pathway rather than the stand-alone 12-lead AI-ECG.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [15]
The strongest prospective African data come from a pragmatic randomized trial in Nigeria, which showed that AI-assisted screening for peripartum cardiomyopathy can be operationalized in hospital-based care.
“The strongest prospective African data come from a pragmatic randomized trial in Nigeria, which showed that AI-assisted screening for peripartum cardiomyopathy can be operationalized in hospital-based care;”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [15]
Current African evidence supports the feasibility of AI-assisted screening in facility-based workflows.
“Current African evidence therefore supports the feasibility of AI-assisted screening in facility-based workflows, but does not yet provide a generalizable external-validation evidence base for stand-alone AI-ECG across rural primary care, community screening, or heterogeneous low-resource delivery systems.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [15]
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- Authors
- 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., Hamza, S. M., Ogah, O. S., Obajimi, G., Saanu, O. O., Jagun, O. E., Inofomoh, F. O., Adeolu, T., Karaye, K. M., Gaya, S. A., Alfa, I., Yohanna, C., Venkatachalam, K. L., Dugan, J., Yao, X., Sledge, H. J., Johnson, P. W., Wieczorek, M. A., Attia, Z. I., Phillips, S. D., Yamani, M. H., Tobah, Y. B., Rose, C. H., Sharpe, E. E., Lopez-Jimenez, F., Friedman, P. A., Noseworthy, P. A., Carter, R. E., & on behalf of the SPEC-AI Nigeria Investigators
- Affiliations
- Mayo Clinic (US)
- University of Lagos (NG)
- Lagos University Teaching Hospital (NG)
- University of Ilorin Teaching Hospital (NG)
- University of Ilorin (NG)
- University of Ibadan (NG)
- University College Hospital, Ibadan (NG)
- Olabisi Onabanjo University Teaching Hospital (NG)
- Aminu Kano Teaching Hospital (NG)
- Corresponding author
- Adedinsewo, Demilade A.
- ORCID
- Adedinsewo, Demilade A.
- Morales-Lara, Andrea Carolina
- Afolabi, Bosede B.
- Kushimo, Oyewole A.
- Mbakwem, Amam C.
- Ibiyemi, Kehinde F.
- Ogunmodede, James Ayodele
- Raji, Hadijat Olaide
- Ringim, Sadiq H.
- Hamza, Sabiu M.
- Ogah, Okechukwu S.
- Obajimi, Gbolahan
- Saanu, Olugbenga Oluseun
- Inofomoh, Francisca O.
- Karaye, Kamilu M.
- Gaya, Sule A.
- Dugan, Jennifer
- Yao, Xiaoxi
- Johnson, Patrick W.
- Wieczorek, Mikolaj A.
- Attia, Zachi I.
- Phillips, Sabrina D.
- Yamani, Mohamad H.
- Tobah, Yvonne Butler
- Sharpe, Emily E.
- Friedman, Paul A.
- Noseworthy, Peter A.
- Carter, Rickey E.
- Venue
- Nature Medicine
- Published
- 2024-09-02
- Volume
- 30
- Issue
- 10
- Pages
- 2897-2906
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11485252
- OpenAlex
- W4402118602
- Cited by
- 63
- References
- 36
- Citations by year
- 2026: 37 · 2025: 26
- FWCI
- 18.95
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-02-04)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Mayo Clinic | Center for Clinical and Translational Science, Mayo Clinic (UL1 TR002377)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Issues in Pregnancy
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- peripartum cardiomyopathy, artificial intelligence, left ventricular systolic dysfunction, digital stethoscope, 12-lead ECG, pregnancy, postpartum women, Nigeria
- MeSH terms
- Adult, Artificial Intelligence, Echocardiography, Electrocardiography, Female, Humans, Mass Screening, Cardiomyopathies, Nigeria, Pregnancy, Pregnancy Complications, Cardiovascular, Ventricular Dysfunction, Left
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