AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Pregnancy

Showing 361 to 370 of 1009 articles

Clear Filters

Intelligent classification of cardiotocography based on a support vector machine and convolutional neural network: Multiscene research.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: To propose a computerized system utilizing multiscene analysis based on a support vector machine (SVM) and convolutional neural network (CNN) to assess cardiotocography (CTG) intelligently.

Deep learning with fetal ECG recognition.

Physiological measurement
Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG reco...

Robot assisted Fetoscopic Laser Coagulation: Improvements in navigation, re-location and coagulation.

Artificial intelligence in medicine
Fetoscopic Laser Coagulation (FLC) for Twin to Twin Transfusion Syndrome is a challenging intervention due to the working conditions: low quality images acquired from a 3 mm fetoscope inside a turbid liquid environment, local view of the placental su...

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

BJOG : an international journal of obstetrics and gynaecology
OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these m...

Machine learning-based approach for predicting low birth weight.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health risks. The current study employed a machine learning model to predict LBW.

Leveraging automated approaches to categorize birth defects from abstracted birth hospitalization data.

Birth defects research
BACKGROUND: The Surveillance for Emerging Threats to Pregnant People and Infants Network (SET-NET) collects data abstracted from medical records and birth defects registries on pregnant people and their infants to understand outcomes associated with ...

Seeking arrangements: cell contact as a cleavage-stage biomarker.

Reproductive biomedicine online
RESEARCH QUESTION: What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos?

Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning.

Nature communications
Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detection of potentially life-threatening conditions. However, these assessment methods can occasionally yi...

Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy.

American journal of obstetrics & gynecology MFM
BACKGROUND: Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. How...

Primary omental pregnancy after in vitro fertilization complicated by hemoperitoneum-how to manage it laparoscopically.

Fertility and sterility
OBJECTIVE: To report an uncommon case of primary OP treated laparoscopically. Ectopic pregnancy (EP) is the leading cause of maternal mortality during the first trimester and the incidence increases with assisted reproductive techniques, occurring in...