RT Journal Article
SR Electronic
T1 Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age
JF The Annals of Family Medicine
JO Ann Fam Med
FD American Academy of Family Physicians
SP 230627
DO 10.1370/afm.230627
A1 Kinaszczuk, Anja
A1 Morales-Lara, Andrea Carolina
A1 Garzon-Siatoya, Wendy Tatiana
A1 El-Attar, Sara
A1 Clapp, Adrianna D.
A1 Olutola, Ifeloluwa A.
A1 Moerer, Ryan
A1 Johnson, Patrick
A1 Wieczorek, Mikolaj A.
A1 Attia, Zachi I.
A1 Lopez-Jimenez, Francisco
A1 Friedman, Paul A.
A1 Carter, Rickey E.
A1 Noseworthy, Peter A.
A1 Adedinsewo, Demilade
YR 2025
UL http://www.annfammed.org/content/early/2025/04/21/afm.230627.abstract
AB PURPOSE Identifying cardiovascular disease before conception and in early pregnancy can better inform obstetric cardiovascular care. Our main objective was to evaluate the diagnostic performance of artificial intelligence (AI)-enabled digital tools for detecting left ventricular systolic dysfunction (LVSD) among women of reproductive age.METHODS In a pilot cross-sectional study, we enrolled an initial cohort of 100 consecutive women aged 18-49 years who had a primary care physician and a scheduled echocardiography at Mayo Clinic Florida (Jacksonville) (cohort 1). Twelve-lead electrocardiography (ECG) and digital stethoscope recordings (single-lead ECG + phonocardiography) were performed on the date of echocardiography. We used deep learning to generate prediction probabilities for LVSD (defined as left ventricular ejection fraction <50%) for the 12-lead ECG (AI-ECG) and stethoscope (AI-stethoscope) recordings. In a second cohort of 100 participants, we enrolled consecutive women seen in primary care to estimate the prevalence of positive AI screening results when deployed for routine use (cohort 2).RESULTS The median age of participants was 38.6 years (quartile 1: 30.3 years, quartile 3: 45.5 years), and 71.9% identified as part of the non-Hispanic White population. Among cohort 1, 5% had LVSD. The AI-ECG had an area under the curve of 0.94, and the AI-stethoscope (maximum prediction across all chest locations) had an area under the curve of 0.98. Among cohort 2, the prevalence of a positive AI screen was 1% and 3.2% for AI-ECG and the AI-stethoscope, respectively.CONCLUSION We found these AI tools to be effective for the detection of cardiomyopathy associated with LVSD among women of reproductive age. These tools could potentially be useful for preconception cardiovascular evaluations.