Developing and Testing AI Models for Fetal Biometry and Amniotic Volume Assessment in Fetal Ultrasound Scans.

July 25, 2022 updated by: Deepecho

Developing and Testing Deep Learning Models for Fetal Biometry and Amniotic Volume Assessment in Routine Fetal Ultrasound Scans

Routine fetal ultrasound scan during the second trimester of the pregnancy is a low-cost, noninvasive screening modality that has been proven to lower fetal mortality by up to 20%. One of the critical elements of this exam is the measurement of fetal biometric parameters, which are the head circumference (HC), biparietal diameter (BPD), abdominal circumference (AC), and femur length (FL) measured on biometry standard planes. Those standard planes are taken according to quality standards first described by Salomon et al. and used as the guidelines of the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG). The biometric parameters extracted from them are essential to diagnose fetal growth restriction (FGR), the world's first cause of perinatal fetal mortality.

Such measurements and image quality assessment are time-consuming tasks that are prone to inter and intraobserver variability depending on the level of skill of the sonographer or the physician performing the exam.

Amniotic fluid (AF) volume assessment is also an essential step in routine screening scans allowing the diagnosis of oligo or hydramnios, both associated with increased fetal mortality rates.

The AF is measured by two main "semi-quantitative" techniques: Amniotic Fluid Index (AFI) and the single deepest pocket (SDP). The latter is more specific as it lowers the overdiagnosis of oligo-amnios without any impact on mortality or morbidity and is easier to perform for the sonographer (only one measurement versus four in the case of the AFI technique). However, AF assessment remains a time-consuming and poorly reproducible task.

Attempts to automate such biometric measurements and AF volume assessment have been made using Artificial Intelligence (AI) and deep learning (DL) tools. Studies showed excellent results "in silico," reaching up to 98 %, 95%, 93 % dice score coefficients for HC, AC, and FL measurements and 89 % DSC for AFI measurements. However, they were all conducted retrospectively without validation on prospectively acquired images.

Reviews and experts have stressed the need for quality peer-reviewed prospective studies to assess AI tools' performance with real-world data. Their performance is expected to be worse and to reflect better their use in the clinical workflow.

This study aims to develop DL models to automate HC, BPD, AC, and FL measurements and AF volume assessment from retrospectively acquired data and test their performances to those of clinicians and experts on prospective real-world fetal US scans.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

The DL models will be trained, validated, and tested on the retrospectively acquired data first. This data will consist of fetal US images gathered in the participating medical centers after patient-level anonymization. The ground truth for the models will consist of annotations made by radiologists and obstetricians for classification and segmentation purposes. The DL models will be trained to perform the following tasks:

  • Detection of the following standard planes as described in the ISUOG guidelines: transthalamic, transventricular, transcerebellar, abdominal, and femoral planes on video loops.
  • Image quality scoring according to the ISUOG guidelines of the transthalamic, abdominal and femoral planes.
  • Fetal cranium, abdomen, and femur segmentation to measure HC, BPD AC, and FL.
  • Detection of AF pockets.
  • Segmentation of AF pockets and extraction of pockets depth in order to evaluate the SDP measurement

Physicians will be asked to save additional images and video loops additional to their routine screening in the prospective examinations:

  • Eight images: transthalamic, abdominal, and femoral standard planes with and without calipers, SDP with and without calipers.

  • Four video loops up to five seconds each:

    • A cephalic loop encompassing the transcerebellar, transthalamic, and transventricular planes.
    • An abdominal loop going from the four-chamber view of the heart to a cross-section of the kidneys and back.
    • A femoral loop with the probe parallel to the sagittal axis of the femur sweeping from side to side.
    • A whole amniotic cavity loop, with the probe perpendicular to the ground applying as little pressure as possible on the patient's abdomen, sweeping from the uterine fundus to the cervix, once or twice depending on the volume of the amniotic cavity.

The clinicians performing the exam in "real-time"(RT clinicians), the panel of experts, and the DL models will review the prospective examinations.

The SDP measurement extracted by the AF pocket detection and segmentation models will be directly compared to the value measured by the RT clinicians.

Then, the image quality of planes selected by the RT clinicians and the model will be scored by the panel of experts.

The segmentation task will be evaluated in a tripartite fashion: the model, the RT clinicians, and the panel will all segment the same images.

To assess inter-observer agreement, 10% of the images will be randomly selected and reviewed by two independent reviewers from the panel.

Study Type

Observational

Enrollment (Actual)

122

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Morocco
      • Casablanca, Morocco, 20100
        • Centre de Radiologie Abou Madi
      • Casablanca, Morocco, 20100
        • Centre Hospitalier Cheikh Khalifa
      • Casablanca, Morocco, 20100
        • Centre Hospitalier Universitaire Ibn Rochd
      • Casablanca, Morocco, 27182
        • Mohamed VI University International Hospital
      • Fes, Morocco
        • Centre Hospitalier Universitaire Hassan II Fes
      • Oujda, Morocco
        • Centre Hospitalier Universitaire Mohammed VI Oujda

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

Female

Sampling Method

Probability Sample

Study Population

Pregnant women from 18 years onwards scheduled for a routine fetal ultrasound scan

Description

Inclusion Criteria:

  • Single or multiple viable pregnancies with a gestational age of 14 weeks or more as dated on a first trimester US scan with the crown-rump length (CRL) measurement or grossly estimated from the last menstrual period (LMP).
  • Routine programmed US scan.
  • Patient's consent is obtained.
  • Patient over 18 years old.

Exclusion Criteria:

  • Emergency indication for the fetal ultrasound
  • Major morphological malformations that do not allow proper measurement of the cranium, abdominal or lower limb, for example, anencephaly, omphalocele, lower limb phocomelia.
  • Fetal death.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Other
  • Time Perspectives: Cross-Sectional

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall accuracy for the biometric parameters measurement and amniotic fluid volume assessment
Time Frame: up to 20 weeks
Mean Absolute Error between the model's HC, BPD, AC, FL, and SDP measurements (in mm), the RT clinician's, and the panel's
up to 20 weeks

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Image quality
Time Frame: Up to 20 weeks
Overall model's and RT clinician's image quality score assessed by the panel following the ISUOG standards on fetal ultrasound assessment of Biometry and Growth
Up to 20 weeks
Small-for-Gestational-Age fetus detection accuracy, sensitivity and specificity
Time Frame: Up to 20 weeks
Overall models' diagnostic accuracy, sensitivity, and specificity at detecting Small-for-Gestational-Age fetuses compared to RT clinicians
Up to 20 weeks
Oligohydramnios and polyhydramnios detection accuracy, sensitivity, and specificity
Time Frame: Up to 20 weeks
Overall models' diagnostic accuracy, sensitivity, and specificity at detecting oligohydramnios and polyhydramnios compared to RT clinicians
Up to 20 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Deepecho

Collaborators

Centre Hospitalier Universitaire Ibn Rochd
Mohammed VI University Hospital
Hassan II University
Mohammed V Souissi University

Investigators

  • Principal Investigator: Saad Slimani, M.D., Centre Hospitalier Universitaire Ibn Rochd de Casablanca

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 25, 2021

Primary Completion (Actual)

April 1, 2022

Study Completion (Actual)

April 1, 2022

Study Registration Dates

First Submitted

September 7, 2021

First Submitted That Met QC Criteria

September 16, 2021

First Posted (Actual)

September 28, 2021

Study Record Updates

Last Update Posted (Actual)

July 27, 2022

Last Update Submitted That Met QC Criteria

July 25, 2022

Last Verified

July 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • U1111-1268-5186

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Yes

IPD Plan Description

Investigators plan to communicate findings primarily through original research papers and through participation in scientific meetings. In addition, the investigators will communicate with the general public through the media. The inclusion of co-authors will follow the ICMJE recommendations for scientific publications. Access to the study data might be granted to academic researchers but not to the general public.

IPD Sharing Time Frame

Immediately the following publication, 12 months following article publication

IPD Sharing Access Criteria

Review purposes only. Proposal should be directed to saad.slimani@deepecho.io. To gain access, data requestors will need to sign a data access agreement.

IPD Sharing Supporting Information Type

  • Study Protocol
  • Statistical Analysis Plan (SAP)
  • Informed Consent Form (ICF)
  • Clinical Study Report (CSR)

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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