First trimester prediction of gestational diabetes mellitus by machine learning in twin pregnancies.

ABSTRACT:

Introduction

We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies using a prospective international, multi-center cohort and identify useful predictive markers.

Methods

Pregnant women with two live fetuses were enrolled at 11 + 0 to 13 + 6 weeks' gestation and followed until delivery. GDM was diagnosed at 24-28 weeks' gestation using the two-stage GCT and OGTT tests. Biochemical, biophysical, and blood assessments were conducted at three periods during pregnancy. Multiple machine learning models evaluated demographic, clinical, and laboratory parameters, including maternal factors (BMI, age, medical history), sonographic markers (crown rump length, estimated fetal weight, uterine artery pulsati

SUBMITTER: Louzoun Y 

PROVIDER: S-EPMC12819435 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature