First-Trimester Non-Invasive Prediction of Preterm Birth Using Cell-Free DNA Fragmentomics
This study demonstrates that a machine learning classifier analyzing 4-mer end motif profiles of cell-free DNA from routine first-trimester NIPT samples can accurately predict spontaneous preterm birth with high sensitivity and specificity, offering a non-invasive method to identify at-risk pregnancies without additional testing.