AIMC Topic: Pregnancy

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Digital maternity care in Germany: a cross-sectional web-based survey on midwives' perceptions.

Archives of gynecology and obstetrics
PURPOSE: Maternity care is a central component of any healthcare system and is largely provided by midwives. Considering increasing cost pressures and growing demand for efficiency within the German healthcare system, the development of efficient, di...

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

Archives of gynecology and obstetrics
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. METHOD...

FetCAT: Cross-attention fusion of transformer-CNN architecture for fetal brain plane classification with explainability using motion-degraded MRI.

PloS one
Fetal brain magnetic resonance imaging (MRI) has been recognized as a vital diagnostic tool for identifying neurological anomalies during pregnancy. Accurate classification of fetal MRI planes is essential for effective prenatal neurological assessme...

Insights into long-acting reversible contraceptive practices in Sub-Saharan Africa: A machine learning perspective.

PloS one
INTRODUCTION: Long-acting reversible contraceptives (LARCs) are critical for reducing maternal mortality and unintended pregnancies, yet adoption remains low in Sub-Saharan Africa (SSA) due to systemic inequities, cultural barriers, and fragmented he...

Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Preeclampsia is a severe hypertensive disorder with rising global prevalence. While machine learning (ML) models for predicting preeclampsia are increasingly published, existing evidence shows high heterogeneity, and the distinction betwe...

Identification and validation of PANX1 as an inflammasome-related biomarker in gestational diabetes mellitus: insights from machine learning and experimental approaches.

Mammalian genome : official journal of the International Mammalian Genome Society
Gestational diabetes mellitus (GDM) is characterized by glucose intolerance during pregnancy, resulting from insulin resistance, and is associated with increased maternal and neonatal risks. Inflammasomes play a critical role in GDM pathophysiology b...

Identification of cell senescence-related genes in spontaneous preterm birth based on bioinformatics analysis and machine learning.

PloS one
Spontaneous premature birth (SPTB) is a common pregnancy complication; however, few studies have explored cell senescence-related markers in SPTB. Bioinformatics and machine learning approaches were used to predict potential biomarkers associated wit...

Low-complexity fetal heart rate monitoring from carbon-based single-channel dry electrodes maternal electrocardiogram.

Physiological measurement
. Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-channel dry electrode maternal abdominal ECG () to extract fetal heart rate () using a low-complexity algorith...

Deep source separation for single-channel fetal ECG extraction.

Physiological measurement
the fetal electrocardiogram (FECG) is critical for monitoring fetal health, however, its extraction remains technically challenging due to strong interference from the maternal electrocardiogram (MECG) in abdominal electrocardiogram (AECG). Therefore...

AI for Detecting and Predicting Postpartum Depression: Scoping Review.

Journal of medical Internet research
BACKGROUND: Postpartum depression (PPD) affects up to 20% of mothers globally. Early detection is vital for better outcomes, yet screening lacks scalability and predictive power. Artificial intelligence (AI)-through machine learning, deep learning, a...