Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans.

This study aims at suggesting an end-to-end algorithm based on a U-net-optimized generative adversarial network to predict anterior neck lower jaw angles (ANLJA), which are employed to define fetal head posture (FHP) during nuchal translucency (NT) measurement. We prospectively collected 720 FHP images (half hyperextension and half normal posture) and regarded manual measurement as the gold standard. Seventy percent of the FHP images (half hyperextension and half normal posture) were used to fit models, and the rest to evaluate them in the hyperextension group, normal posture group (NPG), and total group.