Fetal membranes have important mechanical and antimicrobial roles in maintaining pregnancy. However, compared to other pregnancy tissues (e.g., uterus, cervix, placenta), they are understudied. Their low thickness (<800 µm) places them outside the resolution limits of most ultrasound and magnetic resonance scanners. As such, optical imaging methods like OCT have the potential to fill this technical gap. Here, an application of OCT imaging and machine learning for studying (ex vivo) the mechanical properties of the multilayered fetal membranes and correlating them with gestation and birth condition (i.e., labored vs. unlabored), and anatomy (i.e., near vs. far from cervix) is presented.
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