AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 50,000
- 主要终点
- Accuracy of AI Model Predictions for Neonatal Risk
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 1 Year 至 1 Year（Child）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.
排除标准
- •Cases with incomplete or missing critical data elements required for AI model analysis.
结局指标
主要结局
Accuracy of AI Model Predictions for Neonatal Risk
时间窗: 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.
次要结局
未报告次要终点