AIMC Topic: Pregnancy
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An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy.
Scientific reports
Jul 17, 2025
B-ultrasound results are widely used in early pregnancy loss (EPL) prediction, but there are inevitable intra-observer and inter-observer errors in B-ultrasound results especially in early pregnancy, which lead to inconsistent assessment of embryonic...
Association of artificial intelligence-predicted milk yield residuals to behavioral patterns and transition success in multiparous dairy cows.
Journal of dairy science
Jul 16, 2025
Data-driven health monitoring based on milk yield has shown potential to identify health-perturbing events during the transition period. As a proof of principle, we explored the association between the cow's residual milk yield, that is, the differen...
Comparative study of 2D vs. 3D AI-enhanced ultrasound for fetal crown-rump length evaluation in the first trimester.
BMC pregnancy and childbirth
Jul 16, 2025
BACKGROUND: Accurate fetal growth evaluation is crucial for monitoring fetal health, with crown-rump length (CRL) being the gold standard for estimating gestational age and assessing growth during the first trimester. To enhance CRL evaluation accura...
Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers.
Scientific reports
Jul 15, 2025
Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of the fetus and image quality fluctuation, its interpretation is quite challenging. Although deep learning include Co...
Exploring novel molecular mechanisms underlying recurrent pregnancy loss in decidual tissues.
Scientific reports
Jul 15, 2025
Recurrent pregnancy loss (RPL), which affects approximately 2.5% of reproductive-aged women, remains idiopathic in more than 50% of cases, necessitating mechanistic insights and biomarkers. Three RPL decidual tissue transcriptomic datasets (GSE113790...
A novel machine-learning algorithm to screen for trisomy 21 in first-trimester singleton pregnancies.
Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology
Jul 9, 2025
BACKGROUND: Antenatal screening for Trisomy 21 (T21) in the UK is performed primarily in the first trimester. Nuchal Translucency (NT), gestational age, Free β-HCG and PAPP-A are used in combination, creating the 'combined' test. Multivariate Gaussia...
Using Natural Language Processing to Describe the Use of an Online Community for Abortion During 2022: Dynamic Topic Modeling Analysis of Reddit Posts.
JMIR infodemiology
Jul 9, 2025
BACKGROUND: Abortion access in the United States has been in a state of rapid change and increasing restriction since the Dobbs v Jackson Women's Health Organization decision from the US Supreme Court in June 2022. With further constraints on access ...
Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data.
Journal of medical Internet research
Jul 9, 2025
BACKGROUND: Preterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of infant mortality in the United States. There is mounting evidence that COVID-19 infection during pr...
Adverse impact of paternal age on embryo euploidy: insights from retrospective analysis and interpretable Machine learning.
Human fertility (Cambridge, England)
Jul 8, 2025
The trend of delayed childbearing has increased the average age of parents, with the impact of paternal age on embryo euploidy remaining controversial. Therefore, this study aimed to investigate the impact of paternal age on embryo euploidy using ret...
Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data.
Scientific reports
Jul 7, 2025
Neonatal mortality poses a critical challenge in global health, particularly in low- and middle-income countries. Leveraging advancements in technology, such as machine learning (ML) algorithms, offers the potential to improve neonatal care by enabli...
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