Document Type

Conference Proceeding

Publication Date

1-2025

Publication Title

Pregnancy

Abstract

Objective: Congenital heart defects (CHDs) are a leading cause of infant morbidity and mortality partly due to low prenatal detection rates. We evaluated whether an artificial intelligence (AI) system can improve the detection of CHDs on fetal ultrasound exams among both general OBGYNs and MFM specialists. Study Design: The AI system analyzes all grayscale 2D ultrasound cines of an exam and detects 8 morphological findings associated with severe CHDs. The presence of any such finding should justify patient referral for further examination. The AI system identifies each finding as present, absent or inconclusive, and highlights frames where findings can be assessed. A dataset of 200 ultrasound exams from 11 centers in 2 countries was collected (single pregnancy, obstetric or detailed anatomic ultrasounds, or fetal echocardiograms, at 18-24 weeks gestation), with 100 exams having at least one suspicious finding. The ground truth for presence or absence of each finding was determined by a panel of expert fetal cardiologists. These exams were not used for the training of the AI system. Fourteen physicians (OBGYNs and MFMs, 1-30+ years’ experience) reviewed each exam both aided and unaided by the AI system, in randomized order, and annotated them for the presence or absence of any such finding and of each individual finding, along with confidence scores. Receiver operator characteristics (ROC) area under the curve (AUC), sensitivity and specificity were computed by comparing reviews to the ground truth. Results: ROC AUC for detection of any finding was significantly higher for aided than unaided reviews: 0.97 (95% CI 0.96-0.99) vs 0.83 (0.74-0.91), p=0.002 (DBM-OR method). Similar results held for sensitivity: 0.94 (0.89-0.98) aided vs 0.78 (0.69-0.88) unaided and specificity: 0.97 (0.95-0.99) aided vs 0.76 (0.63-0.89) unaided. Mean reading time was shorter for aided (226 ± 218 s) than unaided (274 ± 241 s) reviews (p < 0.001). Conclusion: Assistance by the AI system significantly improved detection of studies suspicious for CHD by OBGYNS and MFMs. AI may play a pivotal role in improving prenatal detection of CHD.

Volume

1

Issue

S1

Comments

Society for Maternal-Fetal Medicine (SMFM) 2025 Pregnancy Meeting, January 27 - February 1, 2025, Aurora, CO

DOI

10.1002/pmf2.12002

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