AIMC Topic: Pregnancy

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SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images.

Medical image analysis
Quality control (QC) of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development by enhancing network performance in semi-supervised and active l...

Deep Learning Model for Real-Time Nuchal Translucency Assessment at Prenatal US.

Radiology. Artificial intelligence
Purpose To develop and evaluate an artificial intelligence-based model for real-time nuchal translucency (NT) plane identification and measurement in prenatal US assessments. Materials and Methods In this retrospective multicenter study conducted fro...

Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach.

International journal of medical informatics
BACKGROUND: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for analyzing dietary associations and uncovering intricate patterns within data. We explored dietary patterns associated with serum iron biomarkers and vitam...

Fetal growth disorders detection during first trimester gestation through comprehensive maternal circulating DNA profiling.

Human molecular genetics
BACKGROUND: Early diagnosis, close follow-up and timely delivery constitute the main elements for appropriate detection and management of Fetal Growth Disorders (FGD). We hypothesized that fetoplacental FGD-associated alterations can be detected in c...

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

The American journal of psychiatry
OBJECTIVE: Postpartum depression (PPD) is a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. The auth...

Exploring the link between grandmaternal air pollution exposure and Grandchild's ASD risk: A multigenerational population-based study in California.

Environment international
BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increasing prevalence. While genetics play a strong causal role, among environmental factors, air pollution (AP) exposure in pregnancy and infancy has been strongly endo...

Generative AI for thematic analysis in a maternal health study: coding semistructured interviews using large language models.

Applied psychology. Health and well-being
STUDY OBJECTIVES: The coding of semistructured interview transcripts is a critical step for thematic analysis of qualitative data. However, the coding process is often labor-intensive and time-consuming. The emergence of generative artificial intelli...

Developmental toxicity: artificial intelligence-powered assessments.

Trends in pharmacological sciences
Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about developmental toxicity, that is, drug-induced toxicity and adverse effects in pregnant women and fetus...

Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics.

Obstetrics and gynecology
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
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...