AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes

FetalFirst Limited

Status: Not yet enrolling

Conditions: Perinatal Outcomes of the Mother and Fetus; Congenital Anomalies; Neonatal Complications; Perinatal Outcomes

Study type: Observational

Funder types: Industry

Identifiers

NCT07064356

FF-NN-AI-001

IRAS ID: 358793 (Other Identifier)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 50,000 estimated patients

Sex: All

Ages: 1 to 1 year old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

50,000 participants in 1 patient group

Retrospective Neonatal Data Cohort

Description:

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.