Artificial Intelligence (AI) in Cardiotocography (CTG) Interpretation

To Introduce Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04584281
• Other study ID #: 2020-00501
• Status: Recruiting
• Start date: October 2020
• Est. completion date: June 2021

Study information

• Verified date: October 2020
• Source: University Hospital Inselspital, Berne
• Contact: Anda Radan
• Phone: 0316321010
• Email: anda-petronela.radan[@]insel.ch
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 15000
• Est. completion date: June 2021
• Est. primary completion date: June 2021
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• To Introduce Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use

Locations

Country	Name	City	State
Switzerland	Frauenklinik Inselspital Bern	Bern

Sponsors (2)

Lead Sponsor	Collaborator
University Hospital Inselspital, Berne	CSEM Centre Suisse d'Electronique et de Microtechnique SA - Recherche et Developpement

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Superior prediction of fetal morbidity through the self-learning CDS system than if performed by obstetricians alone, especially in regards to specificity.		3 months