Artificial Intelligence (AI) in Cardiotocography (CTG) In... | NCT04584281

Studies
Artificial Intelligence (AI) in Cardiotocography (CTG) Interpretation

NCT04584281

Sponsor

Insel Gruppe AG, University Hospital Bern

Status

Unknown status

Last Update Posted

Oct 12, 2020Actual

Enrollment

15,000Estimated

Phase

Not provided

Conditions

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

Interventions

Not provided

Countries

Switzerland

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Oct 2020

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Oct 2020Estimated

Primary Completion Date

Jun 2021Estimated

Completion Date

Jun 2021Estimated

First Submitted Date

Oct 5, 2020

First Submission Date that Met QC Criteria

Oct 5, 2020

First Posted Date

Oct 12, 2020Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Oct 5, 2020

Last Update Posted Date

Oct 12, 2020Actual

Name	Class
CSEM Centre Suisse d'Electronique et de Microtechnique SA - Recherche et Developpement	INDUSTRY

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

15,000Estimated

Outcomes Module

Primary Outcomes

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

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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

Accepts Healthy Volunteers

Yes

Sex

Female

Sex/Gender Based

Yes

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

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

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Anda Radan	Contact	0316321010	0316321010	anda-petronela.radan@insel.ch
Karin Strahm	Contact	0316321010	0316321010	karin.strahm@insel.ch

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Frauenklinik Inselspital Bern	Recruiting	Bern		3010	Switzerland