AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07064356

Enrollment

50000

Registered

2025-07-14

Start date

2025-07-31

Completion date

2026-06-30

Last updated

2025-07-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

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

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.

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

1 Years to 1 Years

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame	Description
Accuracy of AI Model Predictions for Neonatal Risk	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.

Contacts

Primary ContactNawal (Nina) Abide, EMBA, MA, BA

nina@fetalfirst.com+447392477747

Outcome results