AI Medical Compendium Topic:
Pregnancy

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Machine learning approaches for the prediction of retained placenta in dairy cows.

Theriogenology
Retained placenta (RP) is a reproductive disorder that causes significant financial losses to the dairy industry. Predicting RP risk in cows post-calving is a challenging task. This study aimed to evaluate the predictive capabilities of five machine ...

Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success.

NPJ biofilms and microbiomes
Humans are the only species with a commensal Lactobacillus-dominant vaginal microbiota. Reproductive tract microbes have been linked to fertility outcomes, as has intrauterine inflammation, suggesting immune response may mediate adverse outcomes. In ...

Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms.

Scientific reports
Prenatal healthcare development requires accurate automated techniques for fetal ultrasound image segmentation. This approach allows standardized evaluation of fetal development by minimizing time-exhaustive processes that perform poorly due to human...

Automated interpretation of cardiotocography using deep learning in a nationwide multicenter study.

Scientific reports
Timely detection of abnormal cardiotocography (CTG) during labor plays a crucial role in enhancing fetal prognosis. Recent research has explored the use of deep learning for CTG interpretation, most studies rely on small, localized datasets or focus ...

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...

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...