Endocrinology

Menopause

Latest AI and machine learning research in menopause for healthcare professionals.

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Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.

Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest pre...

Utilizing artificial intelligence to determine bone mineral density using spectral CT.

BACKGROUND: Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-wat...

Estrogen-mediated modulation of sterile inflammatory markers and baroreflex sensitivity in ovariectomized female Wistar rats.

OBJECTIVE: This study aims to explore the role of estrogen in providing cardioprotective benefits to...

Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.

Non-small cell lung cancer (NSCLC) exhibits a comparatively slower rate of metastasis in contrast to...

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in heal...

HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs.

PURPOSE: Osteoporosis, affecting over 200 million individuals, often remains unrecognized and untrea...

A survey on representation learning for multi-view data.

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas b...

Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.

BACKGROUND: A high portal pressure gradient (PPG) is associated with an increased risk of failure to...

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.

There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter sc...

Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.

OBJECTIVE: To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and di...

AI-Based solutions for current challenges in regenerative medicine.

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a si...

Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

BACKGROUND: The clinical care process for people with prediabetes starts with lifestyle intervention...

Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.

The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on...

Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery.

BACKGROUND: This study aimed to develop a machine learning classifier for predicting intraoperative ...

GO-MAE: Self-supervised pre-training via masked autoencoder for OCT image classification of gynecology.

Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oe...

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