Machine Learning-based Anomaly Recognition System

NCT ID: NCT04897178

Last Updated: 2021-05-25

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-06-01
• Study Completion Date: 2023-12-01

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

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.

Conditions

Fetal Anomaly

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: RETROSPECTIVE

Study Groups

Fetuses with normal anatomy

Fetuses with normal anatomy scan who demonstrate no structural abnormalities of different systems (CNS, chest and heart, abdomen, skeletal system)

Ultrasound

Intervention Type: DIAGNOSTIC_TEST

Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies

Fetuses with abnormal anatomy

Fetuses with abnormal anatomy scan who demonstrate any structural abnormalities that can be detected with ultrasound

Ultrasound

Intervention Type: DIAGNOSTIC_TEST

Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies

Interventions

Ultrasound

Routine 2 dimensional Ultrasound used to screen fetuses for congenital anomalies

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 45 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

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

UNKNOWN

Sponsor Role: collaborator

Assiut University

OTHER

Sponsor Role: lead

Responsible Party

Sherif Abdelkarim Mohammed Shazly

Assistant lecturer

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Aswan Faculty of Medicine

Aswān, , Egypt

Assiut Faculty of Medicine - Women Health Hospital

Asyut, , Egypt

Countries

Egypt

Other Identifiers

OBG-AI21-P1

Identifier Type: -

Identifier Source: org_study_id