Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-31 @ 9:59 AM
Study NCT ID:
NCT07368504
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-01-27
First Post:
2026-01-16
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI for Newborn Metabolic Screening
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 200000}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2027-01-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2028-11-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-01-24', 'studyFirstSubmitDate': '2026-01-16', 'studyFirstSubmitQcDate': '2026-01-16', 'lastUpdatePostDateStruct': {'date': '2026-01-27', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2028-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Sensitivity of the AI interpretation system for detecting inherited metabolic disorders', 'timeFrame': 'Within 12 months after newborn screening'}, {'measure': 'Specificity of the AI interpretation system for detecting inherited metabolic disorders', 'timeFrame': 'Within 12 months after newborn screening'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Inherited Metabolic Disorders']}, 'descriptionModule': {'briefSummary': '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:\n\nWhat 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.\n\nParticipants will:\n\nHave 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'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '28 Days', 'minimumAge': '2 Days', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Newborns who underwent routine newborn screening for inherited metabolic disorders at the Zhejiang Provincial Newborn Screening Center between May 2025 and December 2027\n* Blood samples collected between 2 and 28 days of age\n* Availability of complete newborn screening test data and essential clinical information\n\nExclusion Criteria:\n\n* Missing, incomplete, or poor-quality screening data\n* Duplicate samples from the same newborn'}, 'identificationModule': {'nctId': 'NCT07368504', 'briefTitle': 'AI for Newborn Metabolic Screening', 'organization': {'class': 'OTHER', 'fullName': "The Children's Hospital of Zhejiang University School of Medicine"}, 'officialTitle': 'Development and Clinical Validation of an Artificial Intelligence-Based Interpretation System for Newborn Screening of Inherited Metabolic Disorders', 'orgStudyIdInfo': {'id': '2025-IRB-0550-P-01'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'OTHER', 'label': 'AI and Manual Interpretation of Newborn Screening Data', 'interventionNames': ['Diagnostic Test: Artificial intelligence-based interpretation system for newborn genetic metabolic disease screening']}], 'interventions': [{'name': 'Artificial intelligence-based interpretation system for newborn genetic metabolic disease screening', 'type': 'DIAGNOSTIC_TEST', 'description': "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.", 'armGroupLabels': ['AI and Manual Interpretation of Newborn Screening Data']}]}, 'contactsLocationsModule': {'locations': [{'zip': '310000', 'city': 'Hangzhou', 'state': 'Zhejiang', 'country': 'China', 'contacts': [{'name': 'Hu, PhD', 'role': 'CONTACT', 'email': 'hzz22980825@zju.edu.cn', 'phone': '+86-571-86670459'}], 'facility': "The Children's Hospital, Zhejiang University School of Medicine", 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}], 'centralContacts': [{'name': 'Hu, PhD', 'role': 'CONTACT', 'email': 'hzz22980825@zju.edu.cn', 'phone': '+86-571-86670459'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "The Children's Hospital of Zhejiang University School of Medicine", 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'huzhenzhen', 'investigatorAffiliation': "The Children's Hospital of Zhejiang University School of Medicine"}}}}