To Introduce Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical UseNCT04584281
Artificial Intelligence (AI) in Cardiotocography (CTG) Interpretation
An observational study of To Introduce Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
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. Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- To Introduce Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use
- How many people
- 15,000 participants
- Where
- 1 siteSwitzerland
- Study dates
- Oct 2020to Jun 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients with singleton pregnancies and CTG-registrations during labour from 01.01.2006 to 31.12.2019
Who may be able to join
- 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
Who cannot join
- Documented refusal
- Multiple pregnancies
- CTG-registrations of planned caesarean sections
Trial locations and contacts
1 site in 1 country.
Switzerland1 site, 1 recruiting
- Recruiting
Frauenklinik Inselspital Bern
Bern, 3010
Anda I Radan, Dr. med., 0316321010 ext. 0316321010, anda-petronela.radan@insel.ch
Karin I Strahm, Dr. med., 0316321010 ext. 0316321010, karin.strahm@insel.ch
About this study
Official title: 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.
What is being measured
The primary outcome is the main question the study is designed to answer.
Superior prediction of fetal morbidity through the self-learning CDS system than if performed by obstetricians alone, especially in regards to specificity.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Oct 2020
- Study startOct 2020 (estimated)
- Primary completionJun 2021 (estimated)
- Study completionJun 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for To Introduce Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical UseStudies of the same condition, open and under way.
- More trials run by Insel Gruppe AG, University Hospital BernThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 15,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
- Sponsor type
- University, hospital or other
- Collaborators
- CSEM Centre Suisse d'Electronique et de Microtechnique SA - Recherche et Developpement
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04584281
- Sponsor's protocol ID
- 2020-00501
- Status last verified
- Oct 2020
- First submitted
- 5 Oct 2020
- Last updated
- 12 Oct 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Oct 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Introduction of Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use
- ClinicalTrials.gov ID
- NCT04584281
- Acronym / sponsor ID
- 2020-00501
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 15,000 participants (estimated)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Collaborators
- CSEM Centre Suisse d'Electronique et de Microtechnique SA - Recherche et Developpement
- Study start
- Oct 2020 (estimated)
- Primary completion
- Jun 2021 (estimated)
- Study completion
- Jun 2021 (estimated)
- First posted
- 12 Oct 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with singleton pregnancies and CTG-registrations during labour from 01.01.2006 to 31.12.2019
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- Documented refusal
- Multiple pregnancies
- CTG-registrations of planned caesarean sections
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Superior prediction of fetal morbidity through the self-learning CDS system than if performed by obstetricians alone, especially in regards to specificity.
Time frame: 3 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Oct 2020.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of To Introduce Artificial Intelligence (AI) and Machine Learning in Cardiotocography (CTG) Interpretation to Improve Clinical Use, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Oct 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Anda Radan (0316321010 ext. 0316321010, anda-petronela.radan@insel.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Superior prediction of fetal morbidity through the self-learning CDS system than if performed by obstetricians alone, especially in regards to specificity. (time frame: 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Oct 2020; AzTrials last refreshed its copy on 3 Nov 2026.