Congenital AnomaliesNCT07064356
AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes
An observational study of Congenital Anomalies and Neonatal Complications, run by FetalFirst Limited.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 1 to 1 yearHealthy volunteers can take part
- Conditions the study is for
- Congenital Anomalies, Neonatal Complications, Perinatal Outcomes
- How many people
- 50,000 participants
- Where
- No sites listed yet
- Study dates
- Jul 2025to Jun 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 to 1 year
- All sexes
- Healthy volunteers accepted
- Child
Study population: The population includes retrospective, anonymized neonatal records obtained from clinical datasets, focusing on neonates previously diagnosed or assessed for congenital anomalies and neonatal risks.
Who may be able to join
- Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.
Who cannot join
- Cases with incomplete or missing critical data elements required for AI model analysis.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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.
Read the 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.
- Conditions
- Congenital Anomalies, Neonatal Complications, Perinatal Outcomes, Perinatal Outcomes of the Mother and Fetus
- Keywords
- Artificial Intelligence, Predictive Diagnostics, Machine Learning, Fetal Medicine, Neonatal Risk Assessment
- Medical subject headings
- Congenital Abnormalities
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Retrospective Neonatal Data Cohort | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI Model Predictions for Neonatal Risk
Evaluate the accuracy of DenseNet121-based AI models in predicting neonatal risks and congenital anomalies, measured by sensitivity, specificity, and overall prediction accuracy.
Measured: 12 Months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jul 2025
- Study startJul 2025 (estimated)
- Primary completionJun 2026 (estimated)
- Study completionJun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Congenital AnomaliesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 50,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- FetalFirst Limited
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Nawal (Nina) Abide, EMBA, MA, BA, Principal investigator, FetalFirst Limited
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07064356
- Sponsor's protocol ID
- FF-NN-AI-001
- Other IDs
- IRAS ID: 358793
- Status last verified
- Jul 2025
- First submitted
- 26 Jun 2025
- Last updated
- 14 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 14 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes
- ClinicalTrials.gov ID
- NCT07064356
- Acronym / sponsor ID
- FF-NN-AI-001
- Conditions
- Congenital Anomalies, Neonatal Complications, Perinatal Outcomes, Perinatal Outcomes of the Mother and Fetus
- Keywords
- Artificial Intelligence, Predictive Diagnostics, Machine Learning, Fetal Medicine, Neonatal Risk Assessment
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 50,000 participants (estimated)
- Lead sponsor
- FetalFirst LimitedIndustry (pharma, biotech, devices)
- Study start
- Jul 2025 (estimated)
- Primary completion
- Jun 2026 (estimated)
- Study completion
- Jun 2026 (estimated)
- First posted
- 14 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retrospective Neonatal Data Cohort | – | –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. |
Who may qualify
- Ages
- 1 to 1 year
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- Study population
- The population includes retrospective, anonymized neonatal records obtained from clinical datasets, focusing on neonates previously diagnosed or assessed for congenital anomalies and neonatal risks.
Key inclusion criteria (1 listed)
- Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.
Key exclusion criteria (1 listed)
- Cases with incomplete or missing critical data elements required for AI model analysis.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of AI Model Predictions for Neonatal Risk
Time frame: 12 Months
Evaluate the accuracy of DenseNet121-based AI models in predicting neonatal risks and congenital anomalies, measured by sensitivity, specificity, and overall prediction accuracy.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Jul 2025.
- FetalFirst Project WebsiteLink provided by the sponsor.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Nawal (Nina) Abide, EMBA, MA, BA principal investigator
FetalFirst Limited
Questions about this trial
What is this clinical trial studying?
An observational study of Congenital Anomalies and Neonatal Complications, run by FetalFirst Limited.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 14 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nawal (Nina) Abide, EMBA, MA, BA (+447392477747, nina@fetalfirst.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI Model Predictions for Neonatal Risk (time frame: 12 Months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Jul 2025.