NCT04584281

Brief 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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
15,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Oct 2020

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

October 1, 2020

Completed
4 days until next milestone

First Submitted

Initial submission to the registry

October 5, 2020

Completed
7 days until next milestone

First Posted

Study publicly available on registry

October 12, 2020

Completed
8 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2021

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

June 1, 2021

Completed
Last Updated

October 12, 2020

Status Verified

October 1, 2020

Enrollment Period

8 months

First QC Date

October 5, 2020

Last Update Submit

October 5, 2020

Conditions

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

Outcome Measures

Primary Outcomes (1)

  • Superior prediction of fetal morbidity through the self-learning CDS system than if performed by obstetricians alone, especially in regards to specificity.

    3 months

Eligibility Criteria

Age18 Years+
Sexfemale(Gender-based eligibility)
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients with singleton pregnancies and CTG-registrations during labour from 01.01.2006 to 31.12.2019

You may qualify if:

  • 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

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Frauenklinik Inselspital Bern

Bern, 3010, Switzerland

RECRUITING

Central Study Contacts

Anda Radan

CONTACT

0316321010anda-petronela.radan@insel.ch

Karin Strahm

CONTACT

0316321010karin.strahm@insel.ch

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

October 5, 2020

First Posted

October 12, 2020

Study Start

October 1, 2020

Primary Completion

June 1, 2021

Study Completion

June 1, 2021

Last Updated

October 12, 2020

Record last verified: 2020-10

Locations

CH(1)