Machine Learning-based Anomaly Recognition System
MARS
Use of Machine Learning Algorithms for Automated Detection of Fetal Anomalies
1 other identifier
observational
1,000
1 country
2
Brief 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 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
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2021
Typical duration for all trials
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
May 18, 2021
CompletedFirst Posted
Study publicly available on registry
May 21, 2021
CompletedStudy Start
First participant enrolled
June 1, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 1, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
December 1, 2023
CompletedMay 25, 2021
May 1, 2021
11 months
May 18, 2021
May 21, 2021
Conditions
Outcome Measures
Primary Outcomes (1)
Diagnostic accuracy
Diagnostic accuracy of deep learning models in identifying major fetal structural anomalies
Fetuses between 10 weeks and 32 weeks of gestation
Study Arms (2)
Fetuses with normal anatomy
Fetuses with normal anatomy scan who demonstrate no structural abnormalities of different systems (CNS, chest and heart, abdomen, skeletal system)
Fetuses with abnormal anatomy
Fetuses with abnormal anatomy scan who demonstrate any structural abnormalities that can be detected with ultrasound
Interventions
Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies
Eligibility Criteria
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
You may qualify if:
- Pregnant women between 18 and 45 years
- Available ultrasound image with clear findings
- postnatal confirmation of diagnosis
You may not qualify if:
- Absence of research authorization on medical records
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (2)
Aswan Faculty of Medicine
Aswān, 81528, Egypt
Assiut Faculty of Medicine - Women Health Hospital
Asyut, 71515, Egypt
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- CASE CONTROL
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Assistant lecturer
Study Record Dates
First Submitted
May 18, 2021
First Posted
May 21, 2021
Study Start
June 1, 2021
Primary Completion
May 1, 2022
Study Completion
December 1, 2023
Last Updated
May 25, 2021
Record last verified: 2021-05