Machine Learning-based Anomaly Recognition System

Fetal Anomaly Clinical Trial

— MARS  

Official title:

Use of Machine Learning Algorithms for Automated Detection of Fetal Anomalies

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT04897178
• Other study ID #: OBG-AI21-P1
• Status: Not yet recruiting
• Start date: June 1, 2021
• Est. completion date: December 1, 2023

Study information

• Verified date: May 2021
• Source: Assiut University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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 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

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 1000
• Est. completion date: December 1, 2023
• Est. primary completion date: May 1, 2022
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years to 45 Years

Eligibility

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

Related Conditions & MeSH terms

• Congenital Abnormalities
• Fetal Anomaly

Intervention

Diagnostic Test:
• Ultrasound
Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies

Locations

Country	Name	City	State
Egypt	Assiut Faculty of Medicine - Women Health Hospital	Assiut
Egypt	Aswan Faculty of Medicine	Aswan

Sponsors (2)

Lead Sponsor	Collaborator
Assiut University	Middle-East Obstetrics and Gynecology Graduate Education (MOGGE) Foundation

Country where clinical trial is conducted

Egypt, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic accuracy	Diagnostic accuracy of deep learning models in identifying major fetal structural anomalies	Fetuses between 10 weeks and 32 weeks of gestation