Status:

UNKNOWN

Machine Learning-based Anomaly Recognition System

Lead Sponsor:

Assiut University

Collaborating Sponsors:

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

Conditions:

Fetal Anomaly

Eligibility:

FEMALE

18-45 years

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 cre...

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 ar...

Eligibility Criteria

Inclusion

  • Pregnant women between 18 and 45 years
  • Available ultrasound image with clear findings
  • postnatal confirmation of diagnosis

Exclusion

  • Absence of research authorization on medical records

Key Trial Info

Start Date :

June 1 2021

Trial Type :

OBSERVATIONAL

Allocation :

ESTIMATED

End Date :

December 1 2023

Estimated Enrollment :

1000 Patients enrolled

Trial Details

Trial ID

NCT04897178

Start Date

June 1 2021

End Date

December 1 2023

Last Update

May 25 2021

Active Locations (2)

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Page 1 of 1 (2 locations)

1

Aswan Faculty of Medicine

Aswān, Egypt, 81528

2

Assiut Faculty of Medicine - Women Health Hospital

Asyut, Egypt, 71515

Machine Learning-based Anomaly Recognition System | DecenTrialz