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Pregnancy

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AI driven interpretable deep learning based fetal health classification.

SLAS technology
In this study, a deep learning model is proposed for the classification of fetal health into 3 categories: Normal, suspect, and pathological. The primary objective is to utilize the power of deep learning to improve the efficiency and effectiveness o...

Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method.

Computers in biology and medicine
Fetal echocardiography, a specialized ultrasound application commonly utilized for fetal heart assessment, can greatly benefit from automated segmentation of anatomical structures, aiding operators in their evaluations. We introduce a novel approach ...

Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.

PloS one
INTRODUCTION: IVF is a widely-used assisted reproductive technology with a consistent success rate of around 30%, and improving this rate is crucial due to emotional, financial, and health-related implications for infertile couples. This study aimed ...

The utilization of artificial intelligence in enhancing 3D/4D ultrasound analysis of fetal facial profiles.

Journal of perinatal medicine
Artificial intelligence (AI) has emerged as a transformative technology in the field of healthcare, offering significant advancements in various medical disciplines, including obstetrics. The integration of artificial intelligence into 3D/4D ultrasou...

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist.

Seminars in reproductive medicine
Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF ...

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of normal and congenital disease-affected fetuses. Manual labeling is very time-consuming, and automated se...

Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data.

International journal of medical informatics
BACKGROUND: Globally, pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. PE prediction using routinely collected data has the advantage of being widely applicable, particularly in low-resource settings. Early int...

A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.

Interdisciplinary sciences, computational life sciences
Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeu...

Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence.

Medicina (Kaunas, Lithuania)
: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the pre...