[Clinical value of AI-assisted cardiac auscultation in screening for congenital heart disease in neonates].

OBJECTIVES

To evaluate the feasibility and effectiveness of replacing manual auscultation with artificial intelligence (AI)-assisted cardiac auscultation within the dual-indicator screening strategy for early detection of congenital heart disease (CHD) in neonates in a real-world clinical setting.

METHODS

Using data from the provincial CHD treatment network led by the Children's Hospital of Zhejiang University School of Medicine, we retrospectively enrolled 41 320 neonates born between July 2020 and March 2023. All neonates underwent pulse oximetry (POX), AI-assisted auscultation, and manual auscultation. The traditional screening strategy was defined as "POX+manual auscultation," and the AI-assisted strategy as "POX+AI-assisted auscultation". A positive screening result was defined as a positive finding in either POX or the corresponding auscultation method (manual or AI). Echocardiography served as the gold standard for CHD diagnosis. True positive, false positive, true negative, and false negative results were determined, and the missed-diagnosis rate, sensitivity, specificity, Youden index, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated. To assess the consistency of screening performance across institutions, analyses were also conducted separately in the four hospitals with the highest screening volumes.

RESULTS

A total of 354 neonates were diagnosed with CHD by cardiac ultrasound. Compared with the traditional strategy, the intelligent strategy significantly reduced the missed-diagnosis rate (67.23% vs. 34.75%, P<0.01), increased sensitivity (32.77% vs. 65.25%, P<0.01), NPV (99.41% vs. 99.67%, P<0.01) and Youden index (30.04% vs. 55.15%, P<0.01). However, specificity, PPV, and diagnostic accuracy were lower (all P<0.01). Across the four high-volume institutions, the intelligent strategy consistently showed a significant reduction in missed-diagnosis rate and increased sensitivity, NPV and Youden index, along with decreased specificity, PPV and diagnostic accuracy, indicating robust and reproducible performance across diverse clinical settings.

CONCLUSIONS

In multicenter real-world practice, the intelligent screening strategy significantly reduces the missed-diagnosis rate of CHD and demonstrates stable screening performance across different institutions, suggesting AI-assisted auscultation is a feasible and clinically valuable alternative to manual auscultation in neonatal CHD screening.