Background & Significance

Invasive prenatal procedures (IPPs) are defined as procedures performed on pregnant mothers to assess the wellbeing and condition of the growing fetus where a needle is inserted into the uterus to withdraw sample material. The three most common forms of these procedures, and those being considered in the scope of this project, are amniocentesis, percutaneous umbilical blood sampling (PUBS), and chorionic villus sampling (CVS). These procedures take samples of the amniotic fluid, umbilical blood, and the chorionic villi found on the surface of the placenta respectively. These samples are then used for genetic testing to confirm suspected genetic disorders, such as Down's syndrome, with up to 99% accuracy. All 3 procedures are performed under live ultrasound imaging, and as a result benefit from proper tactile training to build hand-eye coordination and experience in the procedures.

IPPs are relatively rare during the development of pregnancy, and occur in less than 7% of pregnancies in the U.S. However, this results in poor training practices for resident and fellow OB/GYNs. The risk associated with these procedures is also quite high, with miscarriage of the fetus occurring in up to 3% of procedures. Other adverse effects following these procedures are possible, including internal bleeding of the mother or fetus and infection.

High-fidelity training models for IPPs are mass-produced. One such example is the CAE Blue Phantom™ amniocentesis training model. This model accurately replicates both mechanical and acoustic properties of human tissue to give the model accurate resistance to needle insertion and appearance under ultrasound. However, this model retails for $7,700 and is only directly applicable to amniocentesis. This places it outside of the typical training budget for the department considering the rarity of the training. Low-fidelity models are a more common choice for OB/GYNs. These models often use single-use materials to mimic human tissue, including chicken breast, tofu, gelatin, and elastic sheets. However, while cheap, these models are unsanitary, messy, and not applicable for repeated use. Our goal with this project is to bridge the gap in technology between low- and high-fidelity models to create a trainer which is accurate and reusable at a much lower cost.