AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes

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.

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Key information

Conditions

Congenital Anomalies Congenital Abnormalities Congenital, Hereditary, and Neonatal Diseases and Abnormalities Neonatal Complications Perinatal Outcomes Perinatal Outcomes of the Mother and Fetus

Age range

1 year–1 year

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

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.

Study contacts

Contact information is provided by the study sponsor or research team.

Nawal (Nina) Abide, EMBA, MA, BA

CONTACT

[email protected]

+447392477747

Sponsors and collaborators

Lead sponsor

FetalFirst Limited

Industry

Registry information

Important dates

Study start: 2025
Primary completion: 2026
Study completion: 2026
First posted: Jul 14, 2025
Registry last updated: Jul 14, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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