EP01.09: Deep learning‐based standardisation of the anatomical fetal facial axes in 3D prenatal ultrasounds

We propose an AI-driven tool designed to assist clinicians in standardising the detection of facial planes in 3D ultrasound (US) imaging. It aims to minimise variability across detections while simultaneously mitigating the effects of interobserver variability in fetal facial assessment. We used 445 fetal facial 3D US images acquired between 20 and 26 weeks of gestation. The data was split into 80% for training and 20% for validation. We defined the three-orthogonal standard planes of the fetus' facial planes using anatomical landmarks and computed the 3D ground truth (GT) transformations to achieve alignment with these planes. A deep learning architecture was trained to take as input 3 orthogonal slices from the 3D US and output the 3D translation and rotation to achieve the standard anatomical facial axes. The network is composed of 4 blocks: features extractor, translation regressor, rotation regressor, and differentiable spatial transform (see figure 1). To assess the resulting standard planes, the average error between the estimated 3D transformation by the network and the 3D GT transform was computed. Also, the PSNR and the SSIM between the estimated and the GT planes were calculated. The network accurately estimates 3D transformations for standardising the fetus facial axes, obtaining a translation error of 4.55 mm, a rotation error of 17.4° degrees, a PSNR of 18.6 dB, and a SSIM of 0.657 in the validation set. The estimated standard facial slices closely match the GT standard facial planes. Our method effectively standardises the fetal facial axes, facilitating the measurement and assessment of the fetal face to diagnose fetal abnormalities. Consequently, this tool has the potential to reduce reliance on interobserver variability and alleviate the time and burden associated with locating these planes. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.