Paper-Based Surface-Enhanced Raman Spectroscopy for Diagnosing Prenatal Diseases in Women

with an excellent reproducibility of <6%. We show that we can measure trace amounts of human amniotic fluids of patients with subclinical intra-amniotic infection (IAI) and preterm delivery (PTD) using the chip in combination with a multivariate statistics-derived machine-learning-trained bioclassification method. We can detect the presence of prenatal diseases and identify the types of diseases from amniotic fluids with >92% clinical sensitivity and specificity. Our technology has the potential to be used for the early detection of prenatal diseases and can be adapted for point-of-care applications.