Ignite Creation Date:
2025-12-24 @ 11:29 PM
Ignite Modification Date:
2025-12-30 @ 1:25 PM
Study NCT ID:
NCT07064356
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-07-14
First Post:
2025-06-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000013', 'term': 'Congenital Abnormalities'}], 'ancestors': [{'id': 'D009358', 'term': 'Congenital, Hereditary, and Neonatal Diseases and Abnormalities'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 50000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-07', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-07', 'completionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-07-03', 'studyFirstSubmitDate': '2025-06-26', 'studyFirstSubmitQcDate': '2025-07-03', 'lastUpdatePostDateStruct': {'date': '2025-07-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-07-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of AI Model Predictions for Neonatal Risk', 'timeFrame': '12 Months', 'description': 'Evaluate the accuracy of DenseNet121-based AI models in predicting neonatal risks and congenital anomalies, measured by sensitivity, specificity, and overall prediction accuracy.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence', 'Predictive Diagnostics', 'Machine Learning', 'Fetal Medicine', 'Neonatal Risk Assessment'], 'conditions': ['Congenital Anomalies', 'Neonatal Complications', 'Perinatal Outcomes', 'Perinatal Outcomes of the Mother and Fetus']}, 'referencesModule': {'seeAlsoLinks': [{'url': 'https://www.fetalfirst.com', 'label': 'FetalFirst Project Website'}]}, 'descriptionModule': {'briefSummary': 'This study aims to develop advanced artificial intelligence (AI) models that predict neonatal risks and complications based on historical multimodal health data, including ultrasound and MRI scans. The objective is to empower clinicians and provide clear, compassionate support for families navigating complex prenatal diagnoses.', 'detailedDescription': 'The FetalFirst study employs observational, retrospective analysis utilizing DenseNet121 neural networks. It analyzes de-identified retrospective data comprising ultrasound images, MRI scans, and clinical documentation from existing medical records. This research has received ethical approval from Wales Research Ethics Committee (REC ref: 25/WA/0168, IRAS ID: 358793). Outcomes from this study are expected to significantly enhance clinical intervention strategies, offering healthcare professionals robust tools for earlier detection and improved management of congenital anomalies and neonatal risks. Additionally, the insights gained will provide critical support to parents facing high-risk pregnancies, assisting them in making informed decisions.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '1 Year', 'minimumAge': '1 Year', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The population includes retrospective, anonymized neonatal records obtained from clinical datasets, focusing on neonates previously diagnosed or assessed for congenital anomalies and neonatal risks.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.\n\nExclusion Criteria:\n\n* Cases with incomplete or missing critical data elements required for AI model analysis.'}, 'identificationModule': {'nctId': 'NCT07064356', 'briefTitle': 'AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes', 'organization': {'class': 'INDUSTRY', 'fullName': 'FetalFirst Limited'}, 'officialTitle': 'AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes', 'orgStudyIdInfo': {'id': 'FF-NN-AI-001'}, 'secondaryIdInfos': [{'id': 'IRAS ID: 358793', 'type': 'OTHER', 'domain': 'Health Research Authority (HRA) - Wales REC'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Retrospective Neonatal Data Cohort', 'description': 'This cohort consists of retrospective, anonymized neonatal health records, including ultrasound, MRI scans, and clinical documentation from previous cases, used to develop predictive AI models.'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Nawal (Nina) Abide, EMBA, MA, BA', 'role': 'CONTACT', 'email': 'nina@fetalfirst.com', 'phone': '+447392477747'}], 'overallOfficials': [{'name': 'Nawal (Nina) Abide, EMBA, MA, BA', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'FetalFirst Limited'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Individual participant data (IPD) will not be shared due to ethical and privacy considerations. All data is pseudonymised and governed strictly by the approved Data Sharing Agreement, which restricts external sharing to protect participant confidentiality and comply with UK GDPR.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'FetalFirst Limited', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}