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Poster Session 3

Category: Ultrasound/Imaging

Poster Session 3

(816) AI Enhanced Prenatal Ultrasound Improves Detection of Congenital Heart Defects in Patients with High-BMI

Thursday, February 12, 2026

10:30 AM - 12:00 PM

Submitting Author and Presenting Author(s)

Jessica Spiegelman, MD

Icahn School of Medicine, Mount Sinai West
New York, New York, United States

Coauthor(s)

Jennifer Lam-Rachlin, MD

Icahn School of Medicine at Mount Sinai
Icahn School of Medicine at Mount Sinai, New York, United States
RP

Rajesh Punn, MD

Stanford University School of Medicine
Palo Alto, California, United States
SB

Sarina K. K. Behera, MD

Palo Alto Medical Foundation, Sutter Health
Palo Alto, California, United States
MG

Miwa Geiger, MD

Icahn School of Medicine at Mount Sinai Hospital
New York, New York, United States
ML

Matthias Lachaud, MD

Univ. Grenoble Alpes, CHU Grenoble Alpes
Grenoble, Rhone-Alpes, France
SG

Sara Garmel, MD

Michigan Perinatal Associates and Corewell Health
Dearborn, Michigan, United States
Matthew K. Janssen, MD (he/him/his)

Assistant Professor, Maternal Fetal Medicine
Perelman School of Medicine, University of Pennsylvania
Philadelphia, Pennsylvania, United States
KS

Kendra R. Sylvester-Armstrong, MD

Perinatal Specialists of the Palm Beaches
West Palm Beach, Florida, United States
MH

Mia Heiligenstein, MD

Icahn School of Medicine, Mount Sinai West
New York City, New York, United States
Nathan S. Fox, MD

Clinical professor
Icahn School of Medicine, Mount Sinai West
New York, New York, United States
Andrei Rebarber, MD

Clinical Professor, Ichan School of Medicine at Mount Sinai
Icahn School of Medicine, Mount Sinai West
Mount Sinai West, New York, United States
Greggory R. R. Devore, MD

Clinical Professor
The Fetal Diagnostic Center of Pasadena
Pasadena, California, United States
CZ

Carolyn M. Zelop, MD

Valley Health System
Paramus, New Jersey, United States
RB

Roger Bessis, MD

Centre d’Echographie de l’Odéon
Paris, Ile-de-France, France
ML

Marilyne Levy, MD

UE3C - Unité d’explorations cardiologiques - Cardiopathies Congénitales
Paris, Ile-de-France, France
BS

Bertrand Stos, MD

UE3C - Unité d’explorations cardiologiques - Cardiopathies Congénitales
Paris, Ile-de-France, France
AA

Alisa Arunamata, MD

Stanford University School of Medicine
Palo Alto, California, United States

Objective:
Prenatal detection of congenital heart defects (CHDs) is critical for improving neonatal outcomes, yet remains challenging particularly in patients with high body mass index (BMI), where poor acoustic windows can hinder standard ultrasound assessments. We evaluate whether an AI-aided approach to prenatal ultrasound improves CHD detection in this population.

Study Design:
An AI software developed by BrightHeart analyzed grayscale 2D ultrasound cines from 200 fetal exams (18–24 weeks gestation) collected from 11 centers across 2 countries. The dataset included obstetric, detailed anatomic, and fetal echocardiograms from singleton pregnancies. The AI detects 8 morphological markers associated with severe CHDs and highlights frames for assessment. Ground truth was established by a panel of expert fetal cardiologists. Fourteen physicians (OBGYNs/MFMs, 1–30+ years’ experience) reviewed each case in randomized order, both with and without AI support. ROC AUC, sensitivity and specificity of readers in identifying the presence of any finding at the examination level were evaluated.

Results:
Overall, AI assistance significantly improved diagnostic performance, increasing ROC AUC from 0.825 (95%CI: 0.741-0.908) to 0.974 (95%CI: 0.957-0.990). Importantly, AI-aided performance was consistent across BMI categories: ROC AUC was 0.963 (95%CI: 0.926-0.999), 0.990 (95%CI: 0.977-1.000), 0.979 (95% CI: 0.957-1.000) and 0.996 (95%CI: 0.986-1.000) in the < 25 kg/m2, 25-30 kg/m2, 30-35 kg/m2 and ≥ 35 kg/m2 patient BMI subgroups, respectively. Physician reading performance improved similarly in all BMI subgroups when aided by AI (Figure 1), suggesting that the tool effectively mitigates the known imaging limitations associated with elevated maternal BMI.

Conclusion:
AI-aided analysis of prenatal ultrasound significantly enhances the detection of CHD-associated findings in a challenging high-BMI population. This tool may help reduce missed or late diagnoses and enable earlier referral for fetal echocardiography, particularly in cases where imaging quality is compromised.