AIMC Topic: Pregnancy

Clear Filters Showing 941 to 950 of 1122 articles

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...

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study.

Journal of medical Internet research
BACKGROUND: Fetal growth restriction (FGR) is a common complication of preeclampsia. FGR in patients with preeclampsia increases the risk of neonatal-perinatal mortality and morbidity. However, previous prediction methods for FGR are class-biased or ...

Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age.

Annals of family medicine
PURPOSE: Identifying cardiovascular disease before conception and in early pregnancy can better inform obstetric cardiovascular care. Our main objective was to evaluate the diagnostic performance of artificial intelligence (AI)-enabled digital tools ...

Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence.

BMC medical imaging
BACKGROUND: Microcephaly and macrocephaly, which are abnormal congenital markers, are associated with developmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imaging early on. However, resource-limited...

Fetal origins of adult disease: transforming prenatal care by integrating Barker's Hypothesis with AI-driven 4D ultrasound.

Journal of perinatal medicine
INTRODUCTION: The fetal origins of adult disease, widely known as Barker's Hypothesis, suggest that adverse fetal environments significantly impact the risk of developing chronic diseases, such as diabetes and cardiovascular conditions, in adulthood....

Machine learning-based analysis on factors influencing blood heavy metal concentrations in the Korean CHildren's ENvironmental health Study (Ko-CHENS).

The Science of the total environment
Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing research focusing on pregnant women's heavy metal concentration has considered individual environmen...

Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia.

Scientific reports
Abortion is a critical health issue that leads to numerous complications, maternal deaths, and significant financial burdens on women, families, and healthcare systems. Studies have identified factors associated with abortion using traditional statis...

A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective.

JMIR formative research
BACKGROUND: Hepatitis B virus (HBV) can be transmitted from mother to child either through transplacental infection or via blood-to-blood contact during or immediately after delivery. Early and accurate risk assessments are essential for guiding clin...

Deep learning classification integrating embryo images with associated clinical information from ART cycles.

Scientific reports
An advanced Artificial Intelligence (AI) model that leverages cutting-edge computer vision techniques to analyse embryo images and clinical data, enabling accurate prediction of clinical pregnancy outcomes in single embryo transfer procedures. Three ...

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...