An observational study in Small for Gestational Age Infant, Fetal Growth Restriction and Oligohydramnios, sponsored by Deepecho. Completed at 6 sites in Morocco. Open to female participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2022-07-27.
Sponsored by Deepecho · Observational
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.
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:
Physicians will be asked to save additional images and video loops additional to their routine screening in the prospective examinations:
Four video loops up to five seconds each:
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.
262 studies on the registry are indexed under Fetal Growth Retardation; 69 are open to participants now.
This study's enrollment of 122 is below the median of 200 across 145 observational studies indexed under Fetal Growth Retardation.
Browse Fetal Growth Retardation studies →This is the only study on the registry with Deepecho as lead sponsor.
Counted across the registry records on this site, refreshed daily.
Pregnant women from 18 years onwards scheduled for a routine fetal ultrasound scan
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Models that will be trained on retrospectively acquired data and run on the prospectively acquired data to extract biometric parameters and amniotic volume estimation.
Overall accuracy for the biometric parameters measurement and amniotic fluid volume assessment
Mean Absolute Error between the model's HC, BPD, AC, FL, and SDP measurements (in mm), the RT clinician's, and the panel's
Time frame: up to 20 weeks
Image quality
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
Time frame: Up to 20 weeks
Small-for-Gestational-Age fetus detection accuracy, sensitivity and specificity
Overall models' diagnostic accuracy, sensitivity, and specificity at detecting Small-for-Gestational-Age fetuses compared to RT clinicians
Time frame: Up to 20 weeks
Oligohydramnios and polyhydramnios detection accuracy, sensitivity, and specificity
Overall models' diagnostic accuracy, sensitivity, and specificity at detecting oligohydramnios and polyhydramnios compared to RT clinicians
Time frame: Up to 20 weeks
Plan to share: Yes — 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.
Supporting information: Study protocol, Statistical analysis plan, Informed consent form, Clinical study report
Time frame: Immediately the following publication, 12 months following article publication
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.
This study is completed, as verified in Jul 2022. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.