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Pregnancy

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Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction: insights from a retrospective cohort study.

Scientific reports
This study aims to develop physician support software for determining ovulation time and assess its impact on pregnancy outcomes in natural cycle frozen embryo transfers (NC-FET). To develop, assess, and validate an ovulation prediction model, three ...

Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.

Frontiers in public health
BACKGROUND: Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine...

Artificial intelligence chatbots versus traditional medical resources for patient education on "Labor Epidurals": an evaluation of accuracy, emotional tone, and readability.

International journal of obstetric anesthesia
BACKGROUND: Labor epidural analgesia is a widely used method for pain relief in childbirth, yet information accessibility for expectant mothers remains a challenge. Artificial intelligence (AI) chatbots like Chat Generative Pre-Trained Transformer (C...

Constructing small for gestational age prediction models: A retrospective machine learning study.

European journal of obstetrics, gynecology, and reproductive biology
OBJECTIVE: To develop machine learning prediction models for small for gestational age with baseline characteristics and biochemical tests of various pregnancy stages individually and collectively and compare predictive performance.

Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample.

Genes
BACKGROUND: The genetic determinants of peripartum depression (PPD) are not fully understood. Using a multi-polygenic score approach, we characterized the relationship between genome-wide information and the history of PPD in patients with mood disor...

Machine-Learning-Based Predictive Model for Bothersome Stress Urinary Incontinence Among Parous Women in Southeastern China.

International urogynecology journal
INTRODUCTION AND HYPOTHESIS: Accurate identification of female populations at high risk for urinary incontinence (UI) and early intervention are potentially effective initiatives to reduce the prevalence of UI. We aimed to apply machine-learning tech...

LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection.

Sensors (Basel, Switzerland)
The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet's network parameters have a large ...

Deep learning model using continuous skin temperature data predicts labor onset.

BMC pregnancy and childbirth
BACKGROUND: Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be li...

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.

Scientific reports
The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the l...

I-SIRch: AI-powered concept annotation tool for equitable extraction and analysis of safety insights from maternity investigations.

International journal of population data science
BACKGROUND: Maternity care is a complex system involving treatments and interactions between patients, healthcare providers, and the care environment. To enhance patient safety and outcomes, it is crucial to understand the human factors (e.g. individ...