Artificial Intelligence (AI) in Cardiotocography (CTG) Interpretation

Introduction of Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use

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.

Inclusion Criteria:

• 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

Exclusion Criteria:

• Documented refusal
• Multiple pregnancies
• CTG-registrations of planned caesarean sections