AI Medical Compendium Topic:
Pregnancy

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Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors.

Journal of biomedical informatics
BACKGROUND: Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional prediction methods depend heavily on ultrasonography, which does not consider risk factors affectin...

Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis.

Computers in biology and medicine
Artificial intelligence (AI) is transforming fetal brain imaging by addressing key challenges in diagnostic accuracy, efficiency, and data integration in prenatal care. This review explores AI's application in enhancing fetal brain imaging through ul...

An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades.

Human reproduction (Oxford, England)
STUDY QUESTION: Can a quantitative method be developed to differentiate between blastocysts with similar or same inner cell mass (ICM) and trophectoderm (TE) grades, while also reflecting their potential for live birth?

Predicting pregnancy-related pelvic girdle pain using machine learning.

Musculoskeletal science & practice
BACKGROUND: Pregnancy-related pelvic girdle pain (PPGP) is a common complication during gestation which negatively influences pregnant women's quality of life. There are numerous risk factors associated with PPGP, however, there is limited informatio...

Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise.

Archives of pathology & laboratory medicine
CONTEXT.—: Assessment of placental villous maturation is among the most common tasks in perinatal pathology. However, the significance of abnormalities in morphology is unclear and interobserver variability is significant.

Deep Learning-Based Automated Measurement of Cervical Length in Transvaginal Ultrasound Images of Pregnant Women.

IEEE journal of biomedical and health informatics
Cervical length (CL) measurement using transvaginal ultrasound is an effective screening tool to assess the risk of preterm birth. An adequate assessment of CL is crucial, however, manual sonographic CL measurement is highly operator-dependent and cu...

Assessing fetal lung maturity: Integration of ultrasound radiomics and deep learning.

African journal of reproductive health
This study built a model to forecast the maturity of lungs by blending radiomics and deep learning methods. We examined ultrasound images from 263 pregnancies in the pregnancy stages. Utilizing the GE VOLUSON E8 system we captured images to extract a...

Effects of neonicotinoid pesticide exposure in the first trimester on gestational diabetes mellitus based on interpretable machine learning.

Environmental research
BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and seriously threatens the health of mothers and offspring. Neonicotinoids (NEOs) is a new class of pesticide and widely used worldwide. Prenatal NEOs ...

OB HUB: Remote Electronic Fetal Monitoring Surveillance.

MCN. The American journal of maternal child nursing
OBJECTIVE: The purpose of this project was to implement a remote fetal surveillance unit with increased vigilance and timelier responses to electronic fetal monitor tracings to improve neonatal outcomes and increase safety.

High throughput recurrent pregnancy loss screening: urine metabolic fingerprints LDI-MS and machine learning.

The Analyst
Infertility is a significant challenge faced by many families worldwide, with recurrent pregnancy loss (RPL) being a prevalent cause of infertility among women. This condition causes immense emotional and physical distress for affected individuals an...