Key result
AI-ECG and digital auscultation accurately identify pregnancy-associated LVSD, achieving AUCs up to ~0.93.
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Why the study?
Delayed recognition of peripartum cardiomyopathy is common due to symptom overlap with normal pregnancy changes, and the diagnostic performance of AI-enabled ECG and digital auscultation has not been systematically synthesized.
Do AI-enabled ECG and digital auscultation accurately detect peripartum cardiomyopathy and pregnancy-associated left ventricular systolic dysfunction compared to echocardiography in pregnant or postpartum women?
Systematic Review (n=3,506)
Do AI-enabled ECG and digital auscultation accurately detect peripartum cardiomyopathy and pregnancy-associated left ventricular systolic dysfunction compared to echocardiography in pregnant or postpartum women?
Effect estimate: AUC 0.90 for AI-ECG; AUC 0.93 for digital auscultation
AI-enabled ECG and digital auscultation demonstrate high negative predictive value and overall accuracy for detecting peripartum left ventricular systolic dysfunction, showing promise as potential rule-out tools.
BACKGROUND: Peripartum cardiomyopathy (PPCM) is an important cause of maternal morbidity and mortality. Delayed recognition remains common because symptoms may overlap with normal pregnancy-related physiological changes. Artificial intelligence (AI)-enabled electrocardiography (ECG) and digital auscultation have emerged as scalable, noninvasive tools that may support case finding and referral for echocardiography, but their diagnostic performance in peripartum populations has not been systematically synthesized. OBJECTIVE: To evaluate the diagnostic accuracy of AI-enabled ECG and digital auscultation algorithms for detecting PPCM and pregnancy-associated left ventricular systolic dysfunction (LVSD), using echocardiography as the reference standard. DATA SOURCES: PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched from inception through January 18, 2026. STUDY ELIGIBILITY CRITERIA: Diagnostic accuracy studies evaluating AI-enabled ECG or digital auscultation models for detecting PPCM or pregnancy-associated LVSD in pregnant or postpartum women aged 18 years or older were included. RESULTS: Six studies, including 3,506 women, were included. For AI-enabled 12-lead ECG models targeting LVSD defined as left ventricular ejection fraction (LVEF) < 45%, pooled sensitivity was 82.9% and specificity was 93.6%, with an area under the curve (AUC) of 0.90 and negative predictive value (NPV) of 98.2%. Digital auscultation evaluated against LVEF < 50% showed sensitivity of 91.4%, specificity of 86.2%, AUC of 0.93, and NPV of 99.3%. Evidence was limited and heterogeneous. CONCLUSIONS: AI-enabled ECG and digital auscultation showed promising rule-out performance for peripartum LVSD, but current evidence remains limited and heterogeneous and does not yet support routine implementation.
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Silva et al. (2026) conducted a systematic review in Peripartum cardiomyopathy and pregnancy-associated left ventricular systolic dysfunction (n=3,506). AI-enabled electrocardiography and digital auscultation vs. Echocardiography was evaluated on Detection of left ventricular systolic dysfunction (LVSD) (AUC 0.90 for AI-ECG; AUC 0.93 for digital auscultation). AI-enabled 12-lead ECG and digital auscultation showed high diagnostic accuracy for detecting pregnancy-associated LVSD, with AUCs of 0.90 and 0.93, respectively.
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