Gestational AgeNCT05433519
Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age
An observational study of Gestational Age and Machine Learning, run by University of North Carolina, Chapel Hill at 2 sites in 2 countries.
Trial summary
This is a prospective cohort study of women enrolled early in pregnancy, with randomization to determine the timing of three follow-up visits in the second and third trimester. At each of these follow-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known… Read more about this study
- Who is it for?
- Ages 18 to 59 years, women only
- Conditions the study is for
- Gestational Age, Machine Learning, Pregnancy Related
- How many people
- 400 participants
- Where
- 2 sitesin 2 countries
- Study dates
- 27 Jul 2022to 13 Nov 2023
- 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 to 59 years
- Women only
- Adult
Study population: Pregnant women at 14 weeks or earlier gestation
Who may be able to join
- 18 years of age or older
- viable intrauterine pregnancy at less than 14 0/7 weeks of gestation
- ability and willingness to provide written informed consent
- intent to remain in current geographical area of residence for the duration of study
- willingness to adhere to study procedures
Who cannot join
- maternal body mass index = 40 kg/m^2
- multiple gestation (i.e., twins or higher order)
- major fetal malformation or anomaly
- any other condition (social or medical) that, in the opinion of the study staff, would make study participation unsafe or complicate data interpretation.
Trial locations and contacts
2 sites in 2 countries.
2 sites in 2 countries
United States1 site
University of North Carolina
Chapel Hill, North Carolina, 27599
About this study
Official title: Z 32104 - Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age
This is a prospective cohort study of women enrolled early in pregnancy, with randomization to determine the timing of three follow-up visits in the second and third trimester. At each of these follow-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known gestational age established early in pregnancy.
Read the detailed description
The primary purpose of this research is to assess the diagnostic accuracy of the FAMLI Technology, a novel machine learning-based tool for gestational age assessment that can run on a smart phone or tablet. Study staff will enroll 400 pregnant volunteers prior to 14 completed gestational weeks and obtain accurate "ground truth" gestational age dating with standard ultrasound biometry, using the crown-rump length. These participants will then be asked to return for three follow-up visits, which will include a routine sonogram performed by a trained sonographer and the collection of a set of blind sweep cineloop videos using a low-cost, battery-operated device. The research will be conducted in Chapel Hill, North Carolina (at the University of North Carolina Hospital and/or sites associated with UNC OBGYN) and in Lusaka, Zambia (at the University Teaching Hospital or Kamwala District Health Centre). Approximately equal numbers of participants will be enrolled from each country.
- Conditions
- Gestational Age, Machine Learning, Pregnancy Related
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Pregnant Women | Pregnant women with gestational age established at less than 14 weeks of gestation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of FAMLI Technology
Difference in mean absolute error (MAE) of the index test and clinical reference standard in the primary evaluation window
Measured: From 14 through 27 completed weeks of gestation
Secondary outcomes (1)
Mean absolute error in the secondary evaluation window
Difference in mean absolute error (MAE) of the index test and clinical reference standard in the secondary evaluation window
Measured: From 28 through 36 completed weeks of gestation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jun 2022
- Study start27 Jul 2022
- Primary completion31 May 2023
- Study completion13 Nov 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Gestational AgeStudies of the same condition, open and under way.
- 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
- Prospective
- Enrolment
- 400 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of North Carolina, Chapel Hill
- Sponsor type
- University, hospital or other
- Collaborators
- Bill and Melinda Gates Foundation
- Responsible party
- Sponsor
- Investigators
- Jeff Stringer, MD, Principal investigator, University of North Carolina
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05433519
- Sponsor's protocol ID
- 21-3115
- Status last verified
- Dec 2023
- First submitted
- 21 Jun 2022
- Last updated
- 8 May 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Deidentified individual data that supports the results will be shared beginning 9 to 36 months following publication provided the investigator who proposes to use the data has approval from an Institutional Review Board (IRB), Independent Ethics Committee (IEC), or Research Ethics Board (REB), as applicable, and executes a data use/sharing agreement with UNC.
Publications
Stringer JSA, Pokaprakarn T, Prieto JC, Vwalika B, Chari SV, Sindano N, Freeman BL, Sikapande B, Davis NM, Sebastiao YV, Mandona NM, Stringer EM, Benabdelkader C, Mungole M, Kapilya FM, Almnini N, Diaz AN, Fecteau BA, Kosorok MR, Cole SR, Kasaro MP. Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps. JAMA. 2024 Aug 27;332(8):649-657. doi: 10.1001/jama.2024.10770.
PubMed 39088200
Study at a glance
From the registry record, last updated by the sponsor on 8 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Z 32104 - Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age
- ClinicalTrials.gov ID
- NCT05433519
- Acronym / sponsor ID
- 21-3115
- Conditions
- Gestational Age, Machine Learning, Pregnancy Related
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 400 participants (actual)
- Lead sponsor
- University of North Carolina, Chapel HillUniversity, hospital or other
- Collaborators
- Bill and Melinda Gates Foundation
- Study start
- 27 Jul 2022 (actual)
- Primary completion
- 31 May 2023 (actual)
- Study completion
- 13 Nov 2023 (actual)
- First posted
- 27 Jun 2022 (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.
This is a prospective cohort study of women enrolled early in pregnancy, with randomization to determine the timing of three follow-up visits in the second and third trimester. At each of these follow-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known gestational age established early in pregnancy.
Detailed description
The primary purpose of this research is to assess the diagnostic accuracy of the FAMLI Technology, a novel machine learning-based tool for gestational age assessment that can run on a smart phone or tablet. Study staff will enroll 400 pregnant volunteers prior to 14 completed gestational weeks and obtain accurate "ground truth" gestational age dating with standard ultrasound biometry, using the crown-rump length. These participants will then be asked to return for three follow-up visits, which will include a routine sonogram performed by a trained sonographer and the collection of a set of blind sweep cineloop videos using a low-cost, battery-operated device. The research will be conducted in Chapel Hill, North Carolina (at the University of North Carolina Hospital and/or sites associated with UNC OBGYN) and in Lusaka, Zambia (at the University Teaching Hospital or Kamwala District Health Centre). Approximately equal numbers of participants will be enrolled from each country.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pregnant Women | – | –Pregnant women with gestational age established at less than 14 weeks of gestation |
Who may qualify
- Ages
- 18 to 59 years
- Sex
- Female only
- Age groups
- Adult
- Study population
- Pregnant women at 14 weeks or earlier gestation
Key inclusion criteria (5 listed)
- 18 years of age or older
- viable intrauterine pregnancy at less than 14 0/7 weeks of gestation
- ability and willingness to provide written informed consent
- intent to remain in current geographical area of residence for the duration of study
- willingness to adhere to study procedures
Key exclusion criteria (4 listed)
- maternal body mass index = 40 kg/m^2
- multiple gestation (i.e., twins or higher order)
- major fetal malformation or anomaly
- any other condition (social or medical) that, in the opinion of the study staff, would make study participation unsafe or complicate data interpretation.
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
Diagnostic accuracy of FAMLI Technology
Time frame: From 14 through 27 completed weeks of gestation
Difference in mean absolute error (MAE) of the index test and clinical reference standard in the primary evaluation window
1 secondary outcome
Mean absolute error in the secondary evaluation window
Time frame: From 28 through 36 completed weeks of gestation
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 May 2024.
- Derived from this study: PubMed 39088200Stringer JSA, Pokaprakarn T, Prieto JC, Vwalika B, Chari SV, Sindano N, Freeman BL, Sikapande B, Davis NM, Sebastiao YV, Mandona NM, Stringer EM, Benabdelkader C, Mungole M, Kapilya FM, Almnini N, Diaz AN, Fecteau BA, Kosorok MR, Cole SR, Kasaro MP. Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps. JAMA. 2024 Aug 27;332(8):649-657. doi: 10.1001/jama.2024.10770.
- Individual participant dataSharing planned: Yes. Deidentified individual data that supports the results will be shared beginning 9 to 36 months following publication provided the investigator who proposes to use the data has approval from an Institutional Review Board (IRB), Independent Ethics Committee (IEC), or Research Ethics Board (REB), as applicable, and executes a data use/sharing agreement with UNC.
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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.
Jeff Stringer, MD principal investigator
University of North Carolina
2 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gestational Age and Machine Learning, run by University of North Carolina, Chapel Hill at 2 sites in 2 countries.
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 5 inclusion and 4 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: finished, as of the sponsor's last update on 8 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States, Zambia. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of FAMLI Technology (time frame: From 14 through 27 completed weeks of gestation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05433519, which also lists 1 linked publication.
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 8 May 2024.