Introduction of Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use
试验速览
- 阶段
- 不适用
- 入组人数
- 15,000
- 试验地点
- 1
- 主要终点
- Superior prediction of fetal morbidity through the self-learning CDS system than if performed by obstetricians alone, especially in regards to specificity.
研究概览
简要总结
The project leaders plan to create a clinical decision support (CDS) system by programming a self-learning software to analyze the cardiotocography (CTG) traces in the - already existing - database from the maternity department of the Inselspital Berne. The project leaders will process and analyze all clinical outcomes of the estimated 10000-15000 eligible patient records. CSEM will design, develop, and validate several AI architectures with the intend to create the CDS system. The AI would learn to assist on this task by training machine learning (ML) algorithms. The main purpose of the AI-CDS will be to determine the best fetal extraction moment during labor, based on a self-learning approach, as a "superhuman" support tool for obstetricians in decision making during labor.
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
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 是
入选标准
- •CTG-registrations of patients with singleton pregnancies during labour from 01.01.2006 to 31.12.2019
- •Gestational age ≥ 24+0 weeks
- •Age ≥ 18 years
- •Written informed consent
排除标准
- •Documented refusal
- •Multiple pregnancies
- •CTG-registrations of planned caesarean sections
结局指标
主要结局
Superior prediction of fetal morbidity through the self-learning CDS system than if performed by obstetricians alone, especially in regards to specificity.
时间窗: 3 months
次要结局
未报告次要终点