AIMC Topic: Pregnancy
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Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries: a 2024 analysis.
BMC public health
Apr 23, 2025
INTRODUCTION: Tobacco use during pregnancy is a significant public health concern, associated with adverse maternal and neonatal outcomes. Despite its critical importance, comprehensive data on tobacco use among pregnant women in sub-Saharan Africa i...
ULK2 deficiency stratifies autophagy-driven molecular subtypes and exacerbates trophoblasts apoptosis in preeclampsia.
Placenta
Apr 18, 2025
INTRODUCTION: Preeclampsia (PE), a placenta-originated hypertensive disorder of pregnancy, lacks targeted therapies despite its significant contribution to maternal and fetal morbidity. Emerging evidence implicates autophagy dysregulation in PE patho...
A machine learning-based framework for predicting postpartum chronic pain: a retrospective study.
BMC medical informatics and decision making
Apr 17, 2025
BACKGROUND: Postpartum chronic pain is prevalent, affecting many women after delivery. Machine learning algorithms have been widely used in predicting postoperative conditions. We investigated the prevalence of and risk factors for postpartum chronic...
Machine learning center-specific models show improved IVF live birth predictions over US national registry-based model.
Nature communications
Apr 17, 2025
Expanding in vitro fertilization (IVF) access requires improved patient counseling and affordability via cost-success transparency. Clinicians ask how two types of live birth prediction (LBP) models perform: machine learning, center-specific (MLCS) m...
Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist.
Scientific reports
Apr 16, 2025
This study evaluated the feasibility of HeartAssist, a novel automated tool designed for classification of fetal cardiac views, annotation of cardiac structures, and measurement of cardiac parameters. Unlike previous AI tools that primarily focused o...
Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer.
Frontiers in endocrinology
Apr 16, 2025
RESEARCH QUESTION: Can machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?
Leveraging artificial intelligence in the prediction, diagnosis and treatment of depression and anxiety among perinatal women in low- and middle-income countries: a systematic review.
BMJ mental health
Apr 15, 2025
AIM: The adoption of artificial intelligence (AI) tools is gaining traction in maternal mental health (MMH) research. Despite its growing usage, little is known about its prospects and challenges in low- and middle-income countries (LMICs). This stud...
Incorporating machine learning and statistical methods to address maternal healthcare disparities in US: A systematic review.
International journal of medical informatics
Apr 14, 2025
BACKGROUND: Maternal health disparities are recognized as a significant public health challenge, with pronounced disparities evident across racial, socioeconomic, and geographic dimensions. Although healthcare technologies have advanced, these dispar...
Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening.
BMC pregnancy and childbirth
Apr 14, 2025
BACKGROUND: Circulating cell-free RNA (cfRNA) is gaining recognition as an effective biomarker for the early detection of preeclampsia (PE). However, the current methods for selecting disease-specific biomarkers are often inefficient and typically on...
Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children.
Neurotoxicology
Apr 11, 2025
Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approac...
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