Obstetrics & Gynecology

Latest AI and machine learning research in obstetrics & gynecology for healthcare professionals.

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Showing 3281-3300 of 3,676 articles

Association of Deep Learning-Derived Histologic Features of Placental Chorionic Villi with Maternal and Infant Characteristics in the New Hampshire Birth Cohort Study

Quantification of placental histopathological structures is challenging due to a limited number of perinatal pathologists, constrained resources, and subjective assessments prone to variability. Objective standardization of placental structure is crucial for easing the burden on pathologists, gaining deeper insights into placental growth and adaptation, and ultimately improving maternal and fetal ...

Deep learning clarifies association of osteoporosis risk with bone metastasis in premenopausal women after surgery for early-stage breast cancer: a multicenter retrospective cohort study

Adjuvant use of bone-modifying agents (BMAs) to early-stage breast cancer (eBC) aims to maintain bone density, leading to prevention of bone metastasis (BM) in postmenopausal women; its mechanism remains unknown. Clinically, one-quarter of premenopausal women develop osteopenia. To this, deep learning (DL) enables to evaluate the risk of osteoporosis (RO) using perioperative chest radiography. The...

Adherence Risk Stratification in Physiatry: A Multivariate Analysis of Factors in Community-Based Care Using Algorithmic Modeling Techniques

Missed appointments represent a double-edged sword in community health settings. Policies designed to retain patients and ensure continuity of care fo...

GlucoseGo: A Simple, User-Friendly, Machine Learning-Derived Tool for Predicting Exercise-Related Hypoglycaemia Risk in Type 1 Diabetes

This study aims to develop an accessible, machine learning-derived tool for people with type 1 diabetes that predicts hypoglycaemia risk at the start ...

Development and validation of diagnostic and prognostic prediction tools for dental caries in young children: A protocol

Dental caries is the most common oral disease worldwide, affecting up to 90% of children globally. It can lead to pain, infection, and impaired qualit...

MenstLLaMA: A Specialized Large Language Model for Menstrual Health Education in India

The quality and accessibility of menstrual health education in developing nations, including India, remain inadequate due to challenges such as povert...

Steroid Metabolome Profiling Identifies a Unique Androgen Hormone Signature Associated with Endometriosis

Endometriosis is a chronic, hormone-dependent condition that affects 190 million women worldwide. There are no validated biomarkers for endometriosis ...

Domain specific models outperform large vision language models on cytomorphology tasks

Large vision-language models (LVLMs) show impressive capabilities in image understanding across domains. However, their suitability for high-risk medi...

Leveraging neighborhood-level Information to Improve Model Fairness in Predicting Prenatal Depression

Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood...

USING ARTIFICIAL INTELLIGENCE TO PREDICT TREATMENT OUTCOMES IN PATIENTS WITH NEUROGENIC OVERACTIVE BLADDER AND MULTIPLE SCLEROSIS

Many women with multiple sclerosis (MS) experience neurogenic overactive bladder (NOAB) characterized by urinary frequency, urinary urgency and urgenc...

Adolescent engagement and sentiment toward reproductive-health videos on Chinese social media: a cross-sectional content analysis

Adolescent reproductive health information is increasingly sought on social-media platforms, yet the scope, credibility, and emotional reception of su...

Regulatory risk loci link disrupted androgen response to pathophysiology of Polycystic Ovary Syndrome

A major challenge in deciphering the complex genetic landscape of Polycystic Ovary Syndrome (PCOS) lies in the limited understanding of how susceptibi...

Barriers and Facilitators to the Implementation of Artificial Intelligence Enabled Diabetes Interventions in Lower-Middle-Income Countries: A Systematic Review Protocol

Diabetes represents an emerging global health crisis, with lower-middle-income countries experiencing a fast growth in prevalence. Diabetes care in th...

Clinical-grade autonomous cytopathology via whole-slide edge tomography

Cytopathology plays a central role in the early detection of cancers such as cervical, lung, and bladder cancer due to its speed, simplicity, and mini...

Predicting Utilization of Emergency Contraceptive Usage in Ethiopia and Identifying Its Predictors Using Machine Learning

Despite policy support, inappropriate use of emergency contraception (EC) in Ethiopia contributes to high rates of unintended pregnancy and maternal m...

CausalDRIFT: Causal Dimensionality Reduction via Inference of Feature Treatments for Robust Healthcare Machine Learning

High-dimensional medical datasets present challenges in feature selection, where traditional methods often prioritize spurious correlations over causa...

Development of an AI-enabled predictive model to identify the ‘sick child’ at a pediatric telemedicine and medication delivery service in Haiti

One of the most difficult challenges in pediatric telemedicine is to accurately discriminate between the ‘sick’ and ‘not sick’ child, especially in re...

Privacy Protection for Chinese Electronic Medical Records Using Large Language Models: Effectiveness Evaluation and Application of LLM Models in Medical Data Tasks

The privacy protection of medical patients has remained a critical concern in healthcare information management during the digital era. Conventional a...

Differential Predictability of Preterm Birth Types: Strong Signals for Indicated Cases versus Limited Success in Spontaneous Preterm Birth

Preterm birth, defined as birth occurring before 37 weeks of gestation, poses a significant and enduring public health challenge, with substantial emo...

Artificial intelligence in prenatal ultrasound: A systematic review of diagnostic tools for detecting congenital anomalies

Artificial intelligence (AI) has potentially shown promise in interpreting ultrasound imaging through flexible pattern recognition and algorithmic lea...

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