A Prospective Silent Trial of Artificial Intelligence for Fetal Weight Estimation
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 283
- 试验地点
- 1
- 主要终点
- Comparing the accuracy of the Hadlock formula and the AI model
研究概览
简要总结
The primary aim of this observational study is to compare the accuracy of two artificial intelligence (AI) models with the traditional Hadlock formula for estimating fetal weight from ultrasound scans performed in pregnant women between 24 and 42 weeks of gestation. The secondary aim is to investigate potential demographic bias in the AI models. The demographic factors examined include body mass index (BMI), parity, gestational age, maternal age, fetal sex, and the presence of preeclampsia.
Participants' ultrasound scans will be pseudonymized and securely stored on password-protected removable drives to ensure the protection of their identity and privacy. The ultrasound data will subsequently be transferred to the Technical University of Denmark (DTU), where the AI models will analyze the images to estimate fetal weight.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- Female
- 接受健康志愿者
- 是
入选标准
- •Women with gestational age between 24-42 weeks undergoing a third-trimester growth scan.
排除标准
- •Women with multiple pregnancies.
研究组 & 干预措施
Pregnant women between 24-42 weeks of gestation
No interventions
结局指标
主要结局
Comparing the accuracy of the Hadlock formula and the AI model
时间窗: From enrollment to the birth of the child
The primary objective is to compare the accuracy of fetal weight estimation between the Hadlock formula and two deep learning models in clinical practice
次要结局
- Demographic biases(From enrollment to the birth of the child)
研究者
Julie Leth-Petersen
Principal Investigator
Copenhagen Academy for Medical Education and Simulation