Icon Legend

Presentation Icons

Additional registration fee required

CME Credit Offered

Abstract Award

Poster Icons

Additional registration fee required

Faculty have requested this content not be shared on social media

CME Credit Offered

Abstract Award

Back

Oral Concurrent Session 3 - Ultrasound and Genetics

Oral Concurrent Sessions

Oral Concurrent Session 3 - Ultrasound and Genetics

(30) Artificial Intelligence System Accurately Detects Fetal Ultrasound Findings Suspicious For Major Congenital Heart Defects

Thursday, January 30, 2025

1:45 PM – 2:00 PM

Location: Aurora Ballroom CD, Lobby Level

Presenting Author(s)

Carolyn M. Zelop, MD

Director of Fetal Echocardiography and Perinatal Research
Valley Health System and NYU School of Medicine
Paramus, New Jersey, United States

Coauthor(s)

Jennifer Lam-Rachlin, MD

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

Alisa Arunamata, MD

Pediatrics - Cardiology, Stanford University School of Medicine
Stanford, California, United States
RP

Rajesh Punn, MD

Pediatrics - Cardiology, Stanford University School of Medicine
Stanford, California, United States
SB

Sarina K. Behera, MD

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

Matthias Lachaud, MD

University of Grenoble Alpes
Grenoble, Rhone-Alpes, France
ND

Nadine David, MD

Medical Training Center
Rouen, Haute-Normandie, France
Greggory R. DeVore, MD

Clinical Professor
The Fetal Diagnostic Center of Pasadena
Pasadena, California, United States
Andrei Rebarber, MD

Clinical Professor, Ichan School of Medicine at Mount Sinai
Icahn School of Medicine at Mount Sinai Hospital
New York, New York, United States
Nathan S. Fox, MD

Clinical Professor
Icahn School of Medicine at Mount Sinai Hospital
New York, New York, United States
MG

Marjorie Gayanilo, MD

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

Sara Garmel, MD

Michigan Perinatal Associates and Corewell Health
Dearborn, Michigan, United States
PB

Philippe Boukobza, MD

CEDEF - Centre Européen de Diagnostic et d’Exploration de la Femme
Le Chesnay, Ile-de-France, France
PU

Pierre Uzan, MD

Groupe IMEF - Imagerie Médicale de l'Est Francilien
Rosny-sous-Bois, Ile-de-France, France
HJ

Hervé Joly, MD

CARPEDIOL - Cardiologie Pédiatrique, fœtale et congénitale adulte de L’Ouest Lyonnais
Ecully, Rhone-Alpes, France
RG

Romain Girardot, MD

Unité de Dépistage de Cardiopathies Foetales et Néonatales
Bordeaux, Aquitaine, France
LC

Laurence Cohen, MD

ETCC - Exploration et Traitement des Cardiopathies Congénitales
Massy, 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
MD

Malo De Boisredon, MSc

BrightHeart
Paris, Ile-de-France, France
EA

Eric Askinazi, MSc

BrightHeart
Paris, Ile-de-France, France
VT

Valentin Thorey, MSc

BrightHeart
Paris, Ile-de-France, France

Submitting Author(s)

Christophe Gardella, PhD

BrightHeart
Paris, Ile-de-France, France

Coauthor(s)

MG

Miwa Geiger, MD

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

Objective:

Prenatal detection of congenital heart defects (CHD) improves patient outcomes. Despite advances in imaging techniques and equipment, prenatal diagnosis remains low. We evaluated the accuracy of an artificial intelligence (AI) system to detect 2nd trimester (2T) ultrasound studies suspicious for CHD.

Study Design:

The AI system analyzes all grayscale 2D ultrasound cines of a study and detects 8 morphological findings suspicious for CHD. The findings were selected such that most major forms of CHD would feature at least one such finding, and the presence of any of these would flag the study for referral for further examination.

We retrospectively included 877 exams (obstetric or detailed anatomic ultrasounds, or fetal echocardiograms), in singleton pregnancies, between 18-24 weeks gestation from 11 centers, including 311 exams with a major CHD, cardiomegaly or levo/dextrocardia. Each exam included 4-chamber, LVOT and RVOT views documented in cines, and was not used for training of the AI system.

For each exam, 3 expert fetal cardiologists annotated the presence or absence of each finding and majority voting was used as the ground truth. The AI system predicted the presence, absence or inconclusiveness (in case of low confidence) of each finding. An exam was predicted positive if at least one finding was present, negative if all findings were absent and inconclusive otherwise.

Results:

Regarding the detection of any finding, the AI system had a conclusive output for 98.8% (95%CI 97.8-99.3) of exams. On exams with a conclusive output, sensitivity was 98.7% (96.7-99.5), and specificity was 97.7% (96.1-98.6). Performance per individual finding is presented in the Figure. To better represent the general low risk population despite the artificially high prevalence of exams with at least one suspicious finding (311/877), per-finding specificity was computed on exams negative to all findings.

Conclusion:

The AI system accurately detected 2T ultrasound exams suspicious for CHD consistent with experts in most cases. The AI system may improve the prenatal ultrasound detection of CHD and thereby improve outcomes.