A machine learning model for assessing fetal health during pregnancy.

ABSTRACT: There is a global imperative to end stillbirths, particularly in low middle income countries (LMICs), which suffer from disproportionate incidence. Sudden changes in fetal movement (FM) patterns often precede a crisis, which, if flagged, can trigger life-saving intervention. Existing means of FM tracking, however, are based on outdated understanding which have remained unchanged for decades. The current standard for monitoring FM out-of-clinic remains maternal perception, which suffers from subjectivity and has little impact in reducing stillbirth or poor perinatal outcomes. Ultrasound can trace FM and trigger intervention but is administered sporadically over the course of pregnancy and demands clinician expertise and resources; frequent use is not feasible, particularly in LMICs. Wearabl

SUBMITTER: Ashik AK 

PROVIDER: S-EPMC12754730 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature