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
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
SCREENING
Masking
NONE
Enrollment
200,000
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.
The Children's Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang, China
Sensitivity of the AI interpretation system for detecting inherited metabolic disorders
Time frame: Within 12 months after newborn screening
Specificity of the AI interpretation system for detecting inherited metabolic disorders
Time frame: Within 12 months after newborn screening
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