AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes

NCT07064356 | Not Yet Recruiting

• 2 other identifiers: FF-NN-AI-001IRAS ID: 358793
• Study Type: observational
• Target: 50,000
• Locations: 0 countries
• Sites: N/A

Brief 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. The objective is to empower clinicians and provide clear, compassionate support for families navigating complex prenatal diagnoses.

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

Outcome Measures

Primary Outcomes (1)

• 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.

  12 Months

Study Arms (1)

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.

Eligibility Criteria

• Age: 1 Year - 1 Year
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.

You may not qualify if:

• Cases with incomplete or missing critical data elements required for AI model analysis.

Sponsors & Collaborators

• FetalFirst Limited: lead

Related Links

• FetalFirst Project Website

MeSH Terms

Conditions

Congenital Abnormalities

Condition Hierarchy (Ancestors)

Congenital, Hereditary, and Neonatal Diseases and Abnormalities

Study Officials

• Nawal (Nina) Abide, EMBA, MA, BA

  FetalFirst Limited

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Nawal (Nina) Abide, EMBA, MA, BA

CONTACT

+447392477747; nina@fetalfirst.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 26, 2025
• First Posted: July 14, 2025
• Study Start: July 1, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: July 14, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share