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Research ArticleArticles

Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age

Anja Kinaszczuk, Andrea Carolina Morales-Lara, Wendy Tatiana Garzon-Siatoya, Sara El-Attar, Adrianna D. Clapp, Ifeloluwa A. Olutola, Ryan Moerer, Patrick Johnson, Mikolaj A. Wieczorek, Zachi I. Attia, Francisco Lopez-Jimenez, Paul A. Friedman, Rickey E. Carter, Peter A. Noseworthy and Demilade Adedinsewo
The Annals of Family Medicine April 2025, 230627; DOI: https://doi.org/10.1370/afm.230627
Anja Kinaszczuk
Department of Family Medicine, Mayo Clinic, Jacksonville, Florida
DO
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Andrea Carolina Morales-Lara
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida
MD
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Wendy Tatiana Garzon-Siatoya
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida
MD
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Sara El-Attar
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida
MD
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Adrianna D. Clapp
Department of Family Medicine, Mayo Clinic, Jacksonville, Florida
MD
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Ifeloluwa A. Olutola
Department of Family Medicine, Mayo Clinic, Jacksonville, Florida
MD
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Ryan Moerer
Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
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Patrick Johnson
Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
MS
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Mikolaj A. Wieczorek
Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
MS
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Zachi I. Attia
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
PhD
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Francisco Lopez-Jimenez
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
MS, MD
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Paul A. Friedman
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
MD
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Rickey E. Carter
Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
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Peter A. Noseworthy
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
MD, MBA
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Demilade Adedinsewo
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida
MD, MPH
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ABSTRACT

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.

Key words:
  • artificial intelligence
  • cardiomyopathy
  • electrocardiography
  • preconception care
  • primary health care
  • Received for publication December 3, 2023.
  • Revision received January 10, 2025.
  • Accepted for publication February 4, 2025.
  • © 2025 Annals of Family Medicine, Inc.
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The Annals of Family Medicine: 23 (2)
The Annals of Family Medicine: 23 (2)
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Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age
Anja Kinaszczuk, Andrea Carolina Morales-Lara, Wendy Tatiana Garzon-Siatoya, Sara El-Attar, Adrianna D. Clapp, Ifeloluwa A. Olutola, Ryan Moerer, Patrick Johnson, Mikolaj A. Wieczorek, Zachi I. Attia, Francisco Lopez-Jimenez, Paul A. Friedman, Rickey E. Carter, Peter A. Noseworthy, Demilade Adedinsewo
The Annals of Family Medicine Apr 2025, 230627; DOI: 10.1370/afm.230627

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Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age
Anja Kinaszczuk, Andrea Carolina Morales-Lara, Wendy Tatiana Garzon-Siatoya, Sara El-Attar, Adrianna D. Clapp, Ifeloluwa A. Olutola, Ryan Moerer, Patrick Johnson, Mikolaj A. Wieczorek, Zachi I. Attia, Francisco Lopez-Jimenez, Paul A. Friedman, Rickey E. Carter, Peter A. Noseworthy, Demilade Adedinsewo
The Annals of Family Medicine Apr 2025, 230627; DOI: 10.1370/afm.230627
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Keywords

  • artificial intelligence
  • cardiomyopathy
  • electrocardiography
  • preconception care
  • primary health care

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