AI Medical Compendium Topic:
Pregnancy

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Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.

BMC pregnancy and childbirth
Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilize...

Natural language processing of admission notes to predict severe maternal morbidity during the delivery encounter.

American journal of obstetrics and gynecology
BACKGROUND: Severe maternal morbidity and mortality remain public health priorities in the United States, given their high rates relative to other high-income countries and the notable racial and ethnic disparities that exist. In general, accurate ri...

Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

BMC pregnancy and childbirth
BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorit...

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning.

NeuroImage
The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and...

Machine Learning of ZnO Interaction with Immunoglobulins and Blood Proteins in Medicine.

Journal of healthcare engineering
Toxoplasmosis is a zoonotic illness caused by . Those with a normal immune system normally recover without treatment. Immunocompromised individuals and pregnant women must be treated regularly. Toxoplasmosis is a serious illness that may reactivate i...

Scrutinizing high-risk patients from ASC-US cytology via a deep learning model.

Cancer cytopathology
BACKGROUND: Atypical squamous cells of undetermined significance (ASC-US) is the most frequent but ambiguous abnormal Papanicolaou (Pap) interpretation and is generally triaged by high-risk human papillomavirus (hrHPV) testing before colposcopy. This...

Artificial intelligence in perinatal diagnosis and management of congenital heart disease.

Seminars in perinatology
Prenatal diagnosis and management of congenital heart disease (CHD) has progressed substantially in the past few decades. Fetal echocardiography can accurately detect and diagnose approximately 85% of cardiac anomalies. The prenatal diagnosis of CHD ...

Imaging fetal anatomy.

Seminars in cell & developmental biology
Due to advancements in ultrasound techniques, the focus of antenatal ultrasound screening is moving towards the first trimester of pregnancy. The early first trimester however remains in part, a 'black box', due to the size of the developing embryo a...

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.

Circulation research
Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk fact...

Deep learning fetal ultrasound video model match human observers in biometric measurements.

Physics in medicine and biology
This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestati...