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

Deepecho · 2021
ClinicalTrials.gov · Datasets · License: Public Domain · 2021
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artificial-intelligencedeep-learning
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 retr
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