AIMC Topic: Pregnancy

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Leveraging artificial intelligence for inclusive maternity care: Enhancing access for mothers with disabilities in Africa.

Women's health (London, England)
Women with disabilities face significant barriers in accessing maternal healthcare, which increases their risk of adverse pregnancy outcomes, particularly in Africa, where resources are limited. Artificial intelligence (AI) presents a unique opportun...

Machine learning based model for the early detection of Gestational Diabetes Mellitus.

BMC medical informatics and decision making
BACKGROUND: Gestational Diabetes Mellitus (GDM) is one of the most common medical complications during pregnancy. In the Gulf region, the prevalence of GDM is higher than in other parts of the world. Thus, there is a need for the early detection of G...

A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study.

European journal of pediatrics
UNLABELLED: Congenital diaphragmatic hernia (CDH) has high morbidity and mortality rates. This study aimed to develop a machine learning (ML) algorithm to predict outcomes based on prenatal and early postnatal data. This retrospective observational c...

Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.

Surgical endoscopy
BACKGROUND: Metabolic-bariatric surgery (MBS) is the last effective way to lose weight whom around half of the patients are women of reproductive age. It is recommended an interval of 12 months between surgery and pregnancy to optimize weight loss an...

Effectors and predictors of conceptus survival in cattle: What is next?

Domestic animal endocrinology
In cattle, the physiological process of switching from cycling to pregnant is complex. Ultimately, that process relies on endometrial luminal epithelial cells and is based on the paracrine context of the uterine lumen. Cells either release luteolytic...

Towards automatic US-MR fetal brain image registration with learning-based methods.

NeuroImage
Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological...

The Application of Machine Learning Models to Predict Stillbirths.

Medicina (Kaunas, Lithuania)
: This study aims to evaluate the predictive value of comprehensive data obtained in obstetric clinics for the detection of stillbirth and the predictive ability set of machine learning models for stillbirth. : The study retrospectively included all ...

Prediction of postpartum depression in women: development and validation of multiple machine learning models.

Journal of translational medicine
BACKGROUND: Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several...

Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.

Scientific reports
This study addresses a gap in research on predictive models for postpartum dyslipidemia in women with gestational diabetes mellitus (GDM). The goal was to develop a machine learning-based model to predict postpartum dyslipidemia using early pregnancy...

Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis.

BMC public health
INTRODUCTION: Maternal mortality refers to a mother's death owing to complications arising from childbirth or pregnancy. This issue is a forefront public health challenge around the globe which is pronounced in low- and middle-income countries, parti...