Validating ECG-Based Artificial Intelligence Models for Cardiomyopathy Detection in Obstetric Patients
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Cardiomyopathy significantly impacts maternal health worldwide. Artificial intelligence-enabled electrocardiograms (AI-ECG) have shown effectiveness in detecting left ventricular systolic dysfunction (LVSD) in adults and limited obstetric cohorts; however, broad validation in diverse obstetric populations remains unexplored. OBJECTIVES: Our primary objective was to validate the performance of 2 versions of an AI-ECG model for detecting cardiomyopathy in an international obstetric cohort. METHODS: We conducted a retrospective, multisite study of pregnant or postpartum women (≤12 months) across 11 institutions in 4 countries (Israel, Nigeria, Pakistan, and the United States). Two AI-ECG models (Food and Drug Administration-cleared v2.5.0 and refined v3.0.0) were evaluated against echocardiography. LVSD was defined as left ventricular ejection fraction ≤40% in keeping with the model's training target. Model performance was assessed by area under the receiver-operating characteristic curve (AUROC). RESULTS: We included 1,820 women with paired ECG-echocardiogram data, of whom 7.3% had LVSD. Model 1 (v2.5.0) demonstrated strong discriminative performance with an AUROC of 0.865, sensitivity of 46.1%, and negative predictive value of 95.8%. Model 2 (v3.0.0) showed significantly improved performance, with an AUROC of 0.925, sensitivity of 64.1%, and negative predictive value of 97.2% (P < 0.001) for LVSD detection. CONCLUSIONS: AI-ECG models demonstrated strong external validity in a global obstetric cohort, with significantly improved performance in the refined model trained on expanded data. These findings support the potential role of AI-ECG for a scalable LVSD screening tool in obstetric populations, while underscoring the importance of population-specific validation prior to clinical implementation.
Source: JACC Advances
Authors: Demilade Adedinsewo, Patrick W. Johnson, Carolina Morales-Lara, Maria Vitoria Coutinho, Sabrina D. Phillips, Marc A. Eidelhoch, Michal Cohen-Shelly, Elad Maor, Ariel Furer, Abraham Tsur, Shali Mazaki-Tovi, Kamilu M. Karaye, Bosede Bukola Afolabi, Olusoji Edward Jagun, Sadiq Hassan Ringim, Kehinde Femi Ibiyemi, Okechukwu Samuel Ogah, Sabha Bhatti, Shakeela Naz, Haleema Yasmin, Maedeh Ganji, Jose Ruiz, Khadeeja Esmail, Jason Wheatley, Anekwe Onwuanyi, Eric Chang, Myah M. Griffin, Oludamilola Oluleye, Stefanie Gefroh, Mohan Krishna Ranganathan, Samir Awasthi, Yvonne Butler Tobah, Francisco Lopez-Jimenez, Zachi I. Attia, Paul A. Friedman, Peter A. Noseworthy, Rickey E. Carter
Institutions: Mayo Clinic in Florida, Mayo Clinic, Emory University, Sheba Medical Center, Tel Aviv University, Hebrew University of Jerusalem, Reichman University, Aminu Kano Teaching Hospital, Bayero University Kano, University of Lagos, Olabisi Onabanjo University Teaching Hospital, Federal University Dutse, University of Ilorin Teaching Hospital, University of Ibadan, Rutgers, The State University of New Jersey, Robert Wood Johnson University Hospital, National Institute of Cardiovascular Diseases, Jinnah Postgraduate Medical Center, Jinnah Sindh Medical University, Baptist Medical Center Beaches, University of Florida, University of Florida Health Science Center, Morehouse School of Medicine, Essentia Health, Nference (United States)
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,936
OpenAlex ID: W7214050982