AI-Powered pulse check could spot hidden heart defects in newborns

NCT ID NCT05637814

Recruiting now Diagnosis Sponsor: University of California, Davis Source: ClinicalTrials.gov ↗

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study tests whether a machine learning algorithm can better detect critical congenital heart disease (CCHD) in newborns by combining standard oxygen saturation measurements with perfusion index data. Researchers will enroll up to 320 infants, including those with suspected or confirmed heart conditions and healthy babies. The goal is to see if the AI approach improves screening accuracy compared to current methods.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance: SpO2/PIx measurement and ML algorithm
What this could lead to: If successful, this could make newborn screening for critical heart defects more accurate, catching more cases early and reducing missed diagnoses.
What could go wrong: This is a validation study, not a treatment trial. The algorithm may not perform better than current screening methods, and results may not apply to all hospitals or populations.

This is an AI summary of the original study and may miss details. Read our disclaimer.

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Conditions

The condition(s) this trial relates to.

congenital heart disease

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

CONGENITAL HEART DISEASE

Contacts and locations

Locations

Cohen Children's Medical Center
NOT_YET_RECRUITING

Queens, New York, 11040, United States
UC Davis Medical Center
RECRUITING

Davis, California, 95616, United States
University of Utah Health Care
NOT_YET_RECRUITING

Salt Lake City, Utah, 84102, United States

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