Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.

ABSTRACT: Multiple Cervical Length (CL) measurements are typically acquired throughout the course of twin pregnancy to detect the early stages of labour and identify pregnancies at a high risk of preterm delivery. This study uses Machine-Learning (ML) approaches to determine the optimal timing of repeated CL measurements when used for predicting spontaneous preterm birth (sPTB) in twin pregnancies. Serial CL measurements from ultrasounds performed between 16 and 28 weeks of gestation were retrospectively acquired from 2,095 patients carrying twin pregnancies. These measurements were used for creating several CL feature sets, which were subsequently evaluated for their utility in predicting PTB < 37, sPTB < 37, sPTB < 34, and sPTB < 32 weeks. The highest accuracies for predicting sPTB < 37, sPTB < 34

SUBMITTER: Costanzo A 

PROVIDER: S-EPMC12521658 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature