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Updated: Jan 9, 2026

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

Published on: May 5, 2018

An interpretable deep learning model for first-trimester fetal cardiac screening.

Wenjia Lei¹, Chi Wen², He Li²

¹Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China.

NPJ Digital Medicine

|December 8, 2025

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This summary is machine-generated.

A new interpretable deep learning model accurately screens for congenital heart disease (CHD) in the first trimester. This AI tool aids clinicians, enabling earlier intervention for better patient outcomes.

Area of Science:

Cardiology
Artificial Intelligence
Medical Imaging

Background:

Effective first-trimester screening for congenital heart disease (CHD) is limited by technical challenges and a lack of validated diagnostic tools.
Early detection of CHD is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

To develop and validate an interpretable deep learning (DL) model for accurate and explainable CHD diagnosis during first-trimester screening.
To assess the model's performance against experienced clinicians and its potential to enhance diagnostic capabilities.

Main Methods:

A large cohort of 108,521 first-trimester cardiac screenings was analyzed, with 8062 Doppler flow four-chamber view images curated for model development.
An interpretable DL model was developed, focusing on diastolic flow patterns to mimic clinical reasoning for CHD diagnosis.

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Last Updated: Jan 9, 2026

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

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Published on: May 5, 2018

Murine Fetal Echocardiography

Murine Fetal Echocardiography

Published on: February 15, 2013

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

The model underwent rigorous validation using multiple external datasets and comparisons with experienced clinicians.

Main Results:

The interpretable DL model demonstrated high accuracy in diagnosing CHD from first-trimester cardiac screenings.
Interpretability analyses confirmed the model's diagnostic logic aligns with established clinical expertise.
The model's performance matched or exceeded that of experienced clinicians, showing potential for augmenting their diagnostic abilities.

Conclusions:

This study presents the first validated interpretable DL system for first-trimester CHD screening.
The developed AI tool offers accurate and explainable CHD diagnosis, potentially enabling earlier intervention through an advanced diagnostic window.