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Machine Learning-based Anomaly Recognition System

NCT04897178 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2021-05-25

No results posted yet for this study

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

Conditions

  • Fetal Anomaly

Interventions

DIAGNOSTIC_TEST

Ultrasound

Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies

Sponsors & Collaborators

  • Middle-East Obstetrics and Gynecology Graduate Education (MOGGE) Foundation

    collaborator UNKNOWN

  • Assiut University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
45 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-06-01
Primary Completion
2022-05-01
Completion
2023-12-01

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04897178 on ClinicalTrials.gov