AIMC Topic: Pregnancy
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Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics.
Obstetrics and gynecology
Jun 1, 2025
OBJECTIVE: To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical chara...
Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors.
Journal of biomedical informatics
Jun 1, 2025
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
Jun 1, 2025
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...
PregAN-NET: Addressing Class Imbalance with GANs in Interpretable Computational Framework for Predicting Safety Profile of Drugs Considering Adverse Reactions During Pregnancy.
Journal of biomedical informatics
Jun 1, 2025
Adverse Drug Reactions (ADRs) during pregnancy pose significant risks to both the mother and the fetus. Conventional approaches to predict ADR are inadequate due to ethical restrictions that prevent performing medication studies in pregnant women, le...
Upregulation of immune genes in the proliferative phase endometrium enables classification into women with recurrent pregnancy loss versus controls.
Human reproduction (Oxford, England)
Jun 1, 2025
STUDY QUESTION: Does the transcriptome of preconceptional endometrium in the proliferative phase show a specific profile in women with recurrent pregnancy loss (RPL)?
An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades.
Human reproduction (Oxford, England)
Jun 1, 2025
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
Jun 1, 2025
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...
Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND: Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Early screening and interventions can reduce PE incidence and related complications. We aim to 1) tempor...
Risk factors for recurrent implantation failure as defined by the European Society for Human Reproduction and Embryology.
Human reproduction (Oxford, England)
Jun 1, 2025
STUDY QUESTION: What are the unrecognized risk factors for recurrent implantation failure (RIF) as defined in the ESHRE recommendation?
Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise.
Archives of pathology & laboratory medicine
Jun 1, 2025
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.
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