AIMC Topic: Pregnancy

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Artificial intelligence for sperm selection-a systematic review.

Fertility and sterility
Despite the increasing number of assisted reproductive technologies based treatments being performed worldwide, there has been little improvement in fertilization and pregnancy outcomes. Male infertility is a major contributing factor, and sperm eval...

Society for birth defects research and prevention's multidisciplinary research needs workshop 2022: A call to action.

Birth defects research
The Society for Birth Defects Research and Prevention (BDRP) strives to understand and protect against potential hazards to developing embryos, fetuses, children, and adults by bringing together scientific knowledge from diverse fields. The theme of ...

Deep learning and Gaussian Mixture Modelling clustering mix. A new approach for fetal morphology view plane differentiation.

Journal of biomedical informatics
The last three years have been a game changer in the way medicine is practiced. The COVID-19 pandemic changed the obstetrics and gynecology scenery. Pregnancy complications, and even death, are preventable due to maternal-fetal monitoring. A fast and...

Robot-assisted tubo-tubal reanastomosis after sterilization in 10 steps.

Journal of gynecology obstetrics and human reproduction
Five to 20% of women regret having a tubal ligation. These women are generally otherwise fertile and have a better chance of pregnancy than other patients experiencing infertility, whether by in vitro fertilization or after tubal surgery. Historicall...

Tafoxiparin, a novel drug candidate for cervical ripening and labor augmentation: results from 2 randomized, placebo-controlled studies.

American journal of obstetrics and gynecology
BACKGROUND: Slow progression of labor is a common obstetrical problem with multiple associated complications. Tafoxiparin is a depolymerized form of heparin with a molecular structure that eliminates the anticoagulant effects of heparin. We report on...

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally fo...

Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.

Journal of perinatal medicine
OBJECTIVES: Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial intelligence (AI) was used to assist in CHD diagnosis. No comparison has been made among the various types of algorithms that can assist in the prenat...

An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization.

Scientific data
Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potent...

Predicting preterm births from electrohysterogram recordings via deep learning.

PloS one
About one in ten babies is born preterm, i.e., before completing 37 weeks of gestation, which can result in permanent neurologic deficit and is a leading cause of child mortality. Although imminent preterm labor can be detected, predicting preterm bi...

Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.

Hypertension research : official journal of the Japanese Society of Hypertension
In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (...