Fetal AnomalyMARSNCT04897178
Machine Learning-based Anomaly Recognition System
An observational study of Fetal Anomaly, run by Assiut University at 2 sites in Egypt.
Trial summary
MARS is an artificial intelligence-powered system that aims at detecting common fetal anomalies during real-time obstetrics ultrasound. The current study comprises 2 stages: (1) The stage of model creation which will include retrospective collection of images from fetal anatomy scans with known diagnoses to train… Read more about this study
- Who is it for?
- Ages 18 to 45 years, women only
- Conditions the study is for
- Fetal Anomaly
- What is being tested?
- Ultrasound
- How many people
- 1,000 participants
- Where
- 2 sitesEgypt
- Study dates
- 1 Jun 2021to 1 Dec 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 45 years
- Women only
- No healthy volunteers
- Adult
Study population: Pregnant women who underwent fetal mid-trimester anatomy scan (between 18 and 22 weeks) with or without first trimester fetal anatomy scan (11-14 weeks) with documented ultrasound results and recorded images with are consistent with postnatal diagnosis
Who may be able to join
- Pregnant women between 18 and 45 years
- Available ultrasound image with clear findings
- postnatal confirmation of diagnosis
Who cannot join
- Absence of research authorization on medical records
Trial locations and contacts
2 sites in 1 country.
Egypt2 sites
Aswan Faculty of Medicine
Aswān, 81528
Assiut Faculty of Medicine - Women Health Hospital
Asyut, 71515
About this study
Official title: Use of Machine Learning Algorithms for Automated Detection of Fetal Anomalies
MARS is an artificial intelligence-powered system that aims at detecting common fetal anomalies during real-time obstetrics ultrasound. The current study comprises 2 stages: (1) The stage of model creation which will include retrospective collection of images from fetal anatomy scans with known diagnoses to train these model and test their diagnostic accuracy. (2) The stage of model validation through prospective application of this model to collected videos with known normal and abnormal diagnoses
Read the detailed description
Routine second trimester anomaly scan has become a routine part of antenatal care. Early detection of fetal anomalies permits patient counselling, consideration of termination if detected anomalies are considerable, and arrangement of delivery and immediate neonatal care if indicated. Furthermore, with the expanding role of fetal interventions, early detection of fetal anomalies may expand management options, some of which may lead superior outcomes compared to postnatal interventions.
However, fetal anatomy scan necessitates a particular level of training and expertise, either by sonographers or obstetricians. Unfortunately, availability of experienced personals may be globally limited. Furthermore, first trimester anatomy scan has been evolving rapidly as ultrasound machine continues to develop and clinical research yields more information on first trimester normal standards and abnormal ranges. Accordingly, first trimester scan is anticipated to be a part of routine care in the near future. Although this tool should provide substantial benefits to obstetric patients, this would require more providers with specific training, which is unlikely to be readily available.
Artificial intelligence has been incorporated in the medical field for more than 20 years. With the advancement of deep learning algorithms, deep learning has yielded exceptional accuracy in image recognition. In the last decade, deep learning exhibits high quality performance that may exceed human performance at times. One of the earliest and most prevalent applications of deep learning in medicine are radiology-related.
In the current study, the investigators will create a series of deep learning models that appraise and identify common fetal anomalies in a series of frames including recorded videos or real time ultrasound. Deep learning algorithms will be fed by labelled images of known normal and abnormal findings representing common fetal anomalies for both training and validation. These images will be collected retrospectively through medical records of contributing centers. Their diagnostic performance will be tested on retrospectively collected videos including normal and abnormal findings. In the second stage of the study, These models will be applied to prospectively collected videos of fetal anatomy scan for further validation.
- Conditions
- Fetal Anomaly
- Being tested
- Ultrasound
- Medical subject headings
- Congenital Abnormalities
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
UltrasoundDiagnostic test
Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies
| Study group | Role | What they receive |
|---|---|---|
| Fetuses with normal anatomy | Fetuses with normal anatomy scan who demonstrate no structural abnormalities of different systems (CNS, chest and heart, abdomen, skeletal system) Receives: Diagnostic Test: Ultrasound | |
| Fetuses with abnormal anatomy | Fetuses with abnormal anatomy scan who demonstrate any structural abnormalities that can be detected with ultrasound Receives: Diagnostic Test: Ultrasound |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy
Diagnostic accuracy of deep learning models in identifying major fetal structural anomalies
Measured: Fetuses between 10 weeks and 32 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 posted21 May 2021
- Study start1 Jun 2021 (estimated)
- Primary completion1 May 2022 (estimated)
- Study completion1 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Fetal AnomalyStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Collaborators
- Middle-East Obstetrics and Gynecology Graduate Education (MOGGE) Foundation
- Responsible party
- Principal investigator, Sherif Abdelkarim Mohammed Shazly, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04897178
- Sponsor's protocol ID
- OBG-AI21-P1
- Acronym
- MARS
- Status last verified
- May 2021
- First submitted
- 18 May 2021
- Last updated
- 25 May 2021
- 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 25 May 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Use of Machine Learning Algorithms for Automated Detection of Fetal Anomalies
- ClinicalTrials.gov ID
- NCT04897178
- Acronym / sponsor ID
- MARS / OBG-AI21-P1
- Conditions
- Fetal Anomaly
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Collaborators
- Middle-East Obstetrics and Gynecology Graduate Education (MOGGE) Foundation
- Study start
- 1 Jun 2021 (estimated)
- Primary completion
- 1 May 2022 (estimated)
- Study completion
- 1 Dec 2023 (estimated)
- First posted
- 21 May 2021 (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.
MARS is an artificial intelligence-powered system that aims at detecting common fetal anomalies during real-time obstetrics ultrasound. The current study comprises 2 stages: (1) The stage of model creation which will include retrospective collection of images from fetal anatomy scans with known diagnoses to train these model and test their diagnostic accuracy. (2) The stage of model validation through prospective application of this model to collected videos with known normal and abnormal diagnoses
Detailed description
Routine second trimester anomaly scan has become a routine part of antenatal care. Early detection of fetal anomalies permits patient counselling, consideration of termination if detected anomalies are considerable, and arrangement of delivery and immediate neonatal care if indicated. Furthermore, with the expanding role of fetal interventions, early detection of fetal anomalies may expand management options, some of which may lead superior outcomes compared to postnatal interventions.
However, fetal anatomy scan necessitates a particular level of training and expertise, either by sonographers or obstetricians. Unfortunately, availability of experienced personals may be globally limited. Furthermore, first trimester anatomy scan has been evolving rapidly as ultrasound machine continues to develop and clinical research yields more information on first trimester normal standards and abnormal ranges. Accordingly, first trimester scan is anticipated to be a part of routine care in the near future. Although this tool should provide substantial benefits to obstetric patients, this would require more providers with specific training, which is unlikely to be readily available.
Artificial intelligence has been incorporated in the medical field for more than 20 years. With the advancement of deep learning algorithms, deep learning has yielded exceptional accuracy in image recognition. In the last decade, deep learning exhibits high quality performance that may exceed human performance at times. One of the earliest and most prevalent applications of deep learning in medicine are radiology-related.
In the current study, the investigators will create a series of deep learning models that appraise and identify common fetal anomalies in a series of frames including recorded videos or real time ultrasound. Deep learning algorithms will be fed by labelled images of known normal and abnormal findings representing common fetal anomalies for both training and validation. These images will be collected retrospectively through medical records of contributing centers. Their diagnostic performance will be tested on retrospectively collected videos including normal and abnormal findings. In the second stage of the study, These models will be applied to prospectively collected videos of fetal anatomy scan for further validation.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Fetuses with normal anatomy | – | Diagnostic Test: UltrasoundFetuses with normal anatomy scan who demonstrate no structural abnormalities of different systems (CNS, chest and heart, abdomen, skeletal system) |
| Fetuses with abnormal anatomy | – | Diagnostic Test: UltrasoundFetuses with abnormal anatomy scan who demonstrate any structural abnormalities that can be detected with ultrasound |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- Pregnant women who underwent fetal mid-trimester anatomy scan (between 18 and 22 weeks) with or without first trimester fetal anatomy scan (11-14 weeks) with documented ultrasound results and recorded images with are consistent with postnatal diagnosis
Key inclusion criteria (3 listed)
- Pregnant women between 18 and 45 years
- Available ultrasound image with clear findings
- postnatal confirmation of diagnosis
Key exclusion criteria (1 listed)
- Absence of research authorization on medical records
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| UltrasoundRoutine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies | Diagnostic test | Fetuses with abnormal anatomy, Fetuses with normal anatomy |
Primary outcome
Diagnostic accuracy
Time frame: Fetuses between 10 weeks and 32 weeks of gestation
Diagnostic accuracy of deep learning models in identifying major fetal structural anomalies
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 May 2021.
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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.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Fetal Anomaly, run by Assiut University at 2 sites in Egypt.
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 3 inclusion and 1 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 25 May 2021. 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 Egypt. 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?
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 (time frame: Fetuses between 10 weeks and 32 weeks of gestation). 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 25 May 2021.