Machine Learning-based Anomaly Recognition System (MARS)
Use of Machine Learning Algorithms for Automated Detection of Fetal Anomalies
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
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 Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Locations
-
-
-
Assiut, Egypt, 71515
- Assiut Faculty of Medicine - Women Health Hospital
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Aswan, Egypt, 81528
- Aswan faculty of medicine
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Pregnant women between 18 and 45 years
- Available ultrasound image with clear findings
- postnatal confirmation of diagnosis
Exclusion Criteria:
- Absence of research authorization on medical records
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Fetuses with normal anatomy
Fetuses with normal anatomy scan who demonstrate no structural abnormalities of different systems (CNS, chest and heart, abdomen, skeletal system)
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Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies
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|
Fetuses with abnormal anatomy
Fetuses with abnormal anatomy scan who demonstrate any structural abnormalities that can be detected with ultrasound
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Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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
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Fetuses between 10 weeks and 32 weeks of gestation
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Anticipated)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- OBG-AI21-P1
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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