AIMC Topic: Pregnancy
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Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges.
Computer methods and programs in biomedicine
Feb 23, 2025
PURPOSE: This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this phy...
Predicting mother and newborn skin-to-skin contact using a machine learning approach.
BMC pregnancy and childbirth
Feb 18, 2025
BACKGROUND: Despite the known benefits of skin-to-skin contact (SSC), limited data exists on its implementation, especially its influencing factors. The current study was designed to use machine learning (ML) to identify the predictors of SSC.
Predicting preterm birth using machine learning methods.
Scientific reports
Feb 16, 2025
Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is complex and multifactorial. The objective of this study was to develop and compare machine learning m...
PBScreen: A server for the high-throughput screening of placental barrier-permeable contaminants based on multifusion deep learning.
Environmental pollution (Barking, Essex : 1987)
Feb 14, 2025
Contaminants capable of crossing the placental barrier (PB) adversely affect female reproduction and fetal development. The rapid identification of PB-permeable contaminants is urgently needed due to the inefficiencies of conventional cell-based tran...
AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.
Journal of perinatal medicine
Feb 14, 2025
The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and evolutionary anthropology, offering insights into early neurological and social development. Fetal fa...
Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.
BMC medical informatics and decision making
Feb 12, 2025
BACKGROUND: Despite substantial progress in maternal and neonatal health, Rwanda's mortality rates remain high, necessitating innovative approaches to meet health related Sustainable Development Goals (SDGs). By leveraging data collected from Electro...
Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China.
BMC pregnancy and childbirth
Feb 11, 2025
BACKGROUND: Macrosomia presents significant risks to both maternal and neonatal health, however, accurate antenatal prediction remains a major challenge. This study aimed to develop machine learning approaches to enhance the prediction of fetal macro...
Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods.
BMC pregnancy and childbirth
Feb 11, 2025
BACKGROUND: Preeclampsia (PE) is a multisystem progressive disease that occurs during pregnancy. Previous studies have shown that the immune system is involved in the placental trophoblast function and the pathological process of uterine vascular rem...
Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.
Proceedings of the National Academy of Sciences of the United States of America
Feb 10, 2025
The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated ...
Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.
BMC medical informatics and decision making
Feb 7, 2025
BACKGROUND: Low birth weight (LBW) is a critical global health issue that affects infants disproportionately, particularly in developing countries. This study adopted causal machine learning (CML) algorithms for predicting LBW in newborns, drawing fr...
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