Using machine learning to improve screening for serious heart defects in newborns

Machine Learning for CCHD Screening using Dynamic Data

['FUNDING_OTHER'] · UNIVERSITY OF CALIFORNIA AT DAVIS · NIH-11042185

This study is working on a smart computer program that helps doctors find serious heart problems in newborns by using information from a simple test that measures oxygen levels, aiming to catch more cases that might be missed with current methods.

Quick facts

Phase['FUNDING_OTHER']
Study typeNih_funding
SexAll
SponsorUNIVERSITY OF CALIFORNIA AT DAVIS (nih funded)
Locations1 site (DAVIS, UNITED STATES)
Trial IDNIH-11042185 on ClinicalTrials.gov

What this research studies

This research aims to develop a machine learning algorithm that enhances the screening process for critical congenital heart disease (CCHD) in newborns by utilizing dynamic data from pulse oximetry. Current screening methods often miss many cases of CCHD, particularly those without obvious symptoms. By analyzing various pulse oximetry features, such as oxygen saturation and perfusion index, the study seeks to improve detection rates significantly. The approach involves creating a model that can adapt its predictions based on real-time data, potentially leading to better outcomes for affected infants.

Who could benefit from this research

Good fit: Ideal candidates for this research are newborns aged 0-4 weeks who are undergoing screening for congenital heart defects.

Not a fit: Patients who are older than 4 weeks or those without any congenital heart disease may not benefit from this research.

Why it matters

Potential benefit: If successful, this research could lead to earlier and more accurate detection of critical congenital heart disease in newborns, improving treatment outcomes.

How similar studies have performed: Previous research has shown promising results using machine learning for similar screening approaches, indicating potential for success in this novel application.

Where this research is happening

DAVIS, UNITED STATES

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.

View on NIH RePORTER →

Last reviewed 2026-05-15 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.