A machine learning model for assessing fetal health during pregnancy
File(s)
Published version
Author(s)
Type
Journal Article
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 Existing means of FM tracking, however, are based on outdated understanding which have remained unchanged for decades.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. Wearable FM monitors have been proposed for FM empirical movement monitoring, however, clinical impact remains negligible due to homogeneous sensing modalities and lack of clinical validation. Herein, a multimodal vibrational-acoustic sensor array consisting of piezoelectric and acoustic sensing modalities is validated for use in a wearable FM. 25 pregnant participants were recruited to record vibrational-acoustic data from the array in parallel with ultrasound scanning. Categorised fetal movements were recorded by a clinician, and several machine learning models were investigated to validate the sensors to track FM. An ensemble RUSBoost model combined with concatenated sensor data inputs was implemented, yielding FM prediction with precision and recall of 0.44 and 0.61, demonstrating the feasibility of the vibroacoustic sensor array to monitor FM. Inexpensive off-the-shelf sensors comprising the array provide a basis for the development of a fully wearable FM monitor that can be used in LMICs.
Date Issued
2025-12-17
Date Acceptance
2025-12-04
Citation
Frontiers in Bioengineering and Biotechnology, 2025, 13
ISSN
2296-4185
Publisher
Frontiers Media S.A.
Journal / Book Title
Frontiers in Bioengineering and Biotechnology
Volume
13
Copyright Statement
© 2025 Ashik, Gutierrez, Ashraf, Adjei, Patel, Abati, Yu, Dhawan, Li, Mamun, Reddyhoff, Dini, Lees and Vaidyanathan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Identifier
10.3389/fbioe.2025.1691064
Subjects
acoustic sensing modalities
fetal movement
FM
piezoelectric
rusboost model
vibrational-acoustic sensor array
vibroacoustic sensor array
Publication Status
Published