AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Congenital Anomalies, Neonatal Complications, Perinatal Outcomes, Perinatal Outcomes of the Mother and Fetus. Basic parameters: 1 year — 1 year · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Detailed description
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.
Primary outcome measures
- Accuracy of AI Model Predictions for Neonatal Risk [Time frame: 12 Months]
Eligibility criteria
Inclusion criteria
- Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.
Exclusion criteria
- Cases with incomplete or missing critical data elements required for AI model analysis.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07064356 · FF-NN-AI-001 · IRAS ID: 358793