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PERSPECTIVE article
Front. Cardiovasc. Med.
Sec. Clinical and Translational Cardiovascular Medicine
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1516088
This article is part of the Research Topic Novel Translational Advances in Artificial Intelligence for Diagnosis and Treatment of Cardiovascular Diseases View all 9 articles
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Nigeria is the most populous country in Africa with the highest gross domestic product(GDP)as of 2022. However,Nigeria is burdened by health challenges including a high maternal mortality ratio, inadequate human resources, poor healthcare, and population-level poverty rates as high as40%. Nigeria also has the highest-reported prevalence of peripartum cardiomyopathy worldwide which contributes to maternal mortality. Unfortunately,the diagnosis is often delayed and mortality rates following diagnosis are high (approximately50%). Thus,there is a unmet need for effective and accessible solutions for cardiomyopathy detection in this population. To address maternal mortality,we conducted a randomized controlled clinical trial(NCT05438576)of an artificial intelligence(AI) technology. The objective of the study was to evaluate the impact of AI-guidedscreening on cardiomyopathy detection in obstetric patients. The study found AI-guided screening doubled the detection of cardiomyopathy(defined as left ventricular ejection<50%)when compared to usual care with a number needed to screen of 47. As we explore next steps in relation to deploying this technology for clinical use in Nigeria,we sought to gather contextual information and share lessons learned from the completed trial. To that end,we convened a discussion with study site investigators aimed at identifying site-specific contextual challenges related to the development and conduct of the study. The SPEC-AINigeria study is the first published randomized controlled clinical trial of a healthAI intervention in Nigeria. Insights gained can inform futureAI intervention studies in clinical care,guide the development of implementation strategies to ensure effective interventions are successfully incorporated into clinicalcare,and provide a roadmap for keystakeholders to consider when evaluating AI-technologies in low-resourcesettings.
Keywords: artificial intelligence, cardiomyopathy, electrocardiogram, implementation science, resource-limited settings, Pregnancy, Nigeria
Received: 23 Oct 2024; Accepted: 24 Feb 2025.
Copyright: © 2025 Adedinsewo, Onietan, Morales-Lara, Moideen Sheriff, Afolabi, Kushimo, Mbakwem, Ibiyemi, Ogunmodede, Raji, Ringim, Habib, Hamza, Ogah, Obajimi, Saanu, Aborisade, Jagun, Inofomoh, Adeolu, Karaye, Gaya, Sa’ad, Alfa, Yohanna, Noseworthy and Carter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Demilade A Adedinsewo, Mayo Clinic Florida, Jacksonville, United States
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