AIMC Topic: Pregnancy

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Development of a single-center predictive model for conventional in vitro fertilization outcomes excluding total fertilization failure: implications for protocol selection.

Journal of ovarian research
OBJECTIVES: To develop a multidimensional clinical indicator-based prediction model for identifying high-risk patients with fertilization failure conventional in vitro fertilization (c-IVF) cycles, thereby optimizing therapeutic decision-making.

Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review.

BMC pregnancy and childbirth
OBJECTIVE: To assess the methodological quality and the risk of bias, of studies that developed prediction models using Machine Learning (ML) techniques to estimate prenatal birthweight.

Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana.

BMC pregnancy and childbirth
BACKGROUND: Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robus...

Quality assessment of large language models' output in maternal health.

Scientific reports
Optimising healthcare is linked to broadening access to health literacy in Low- and Middle-Income Countries. The safe and responsible deployment of Large Language Models (LLMs) may provide accurate, reliable, and culturally relevant healthcare inform...

Iron metabolism and preeclampsia: new insights from bioinformatics analysis.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
OBJECTIVE: Preeclampsia (PE) is a multifactorial systemic pregnancy disease, in which iron metabolism and ferroptosis play significant roles during its pathogenesis. The diagnosis and prevention of PE remain urgent clinical issues that need to be add...

An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.

Scientific reports
Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. The manual classification of these is a hectic and time-consuming pr...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

Journal of diabetes science and technology
BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longe...

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A cautionary tale.

PloS one
Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advancements in neonatal care, the prediction of sPTB remains elusive, in part due to complex etiologies a...

Analysis of maternal fetal outcomes and complete blood count parameters according to the stages of placental abruption: a retrospective study.

European journal of medical research
BACKGROUND: To compare the demographic characteristics, maternal and perinatal outcomes and hemoglobin parameters according to stages diagnosed with placental abruption.

Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.

Environmental health perspectives
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal...