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NCT Number: NCT07368504

AI for Newborn Metabolic Screening

The goal of this clinical trial is to evaluate whether an artificial intelligence (AI)-based interpretation system can accurately diagnose inherited metabolic disorders in newborns undergoing routine screening. The main questions it aims to answer are:

What is the sensitivity and specificity of the AI system compared to standard manual interpretation? Does the AI system reduce variability in screening results? Researchers will compare the AI interpretation results with those from standard manual review by trained laboratory staff to assess diagnostic performance.

Participants will:

Have their routine newborn screening blood samples analyzed using both the AI system and standard manual interpretation Be followed according to national newborn screening guidelines if either method indicates a positive result

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Key information

Conditions

Inherited Metabolic Disorders

Age range

2 day–28 day

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Newborns who underwent routine newborn screening for inherited metabolic disorders at the Zhejiang Provincial Newborn Screening Center between May 2025 and December 2027
  • Blood samples collected between 2 and 28 days of age
  • Availability of complete newborn screening test data and essential clinical information

Exclusion criteria

  • Missing, incomplete, or poor-quality screening data
  • Duplicate samples from the same newborn

Treatment and study plan

Artificial intelligence-based interpretation system for newborn genetic metabolic disease screening

Diagnostic Test

This intervention is a deep learning-based software algorithm designed specifically for the interpretation of tandem mass spectrometry (MS/MS) data from routine newborn screening in Chinese neonates. It integrates clinical covariates-including gestational age, birth weight, and blood collection time-to perform multiple-of-the-median (MOM) normalization and simultaneously evaluates 42 inherited metabolic disorders. Unlike existing AI tools developed for older-generation screening panels (e.g., those covering only 29 analytes), this system is trained and validated on over 300,000 real-world Chinese newborn samples, making it the first AI diagnostic tool tailored to China's current expanded newborn screening program.

Primary outcomes

  1. Sensitivity of the AI interpretation system for detecting inherited metabolic disorders

    Time frame: Within 12 months after newborn screening

  2. Specificity of the AI interpretation system for detecting inherited metabolic disorders

    Time frame: Within 12 months after newborn screening

Study contacts

Contact information is provided by the study sponsor or research team.

Hu, PhD

CONTACT

[email protected]

+86-571-86670459

Sponsors and collaborators

Lead sponsor

The Children's Hospital of Zhejiang University School of Medicine

Other

Registry information

Official study title

Development and Clinical Validation of an Artificial Intelligence-Based Interpretation System for Newborn Screening of Inherited Metabolic Disorders

Important dates

Study start
2027
Primary completion
2028
Study completion
2028
First posted
Jan 26, 2026
Registry last updated
Jan 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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