Menopause

Artificial intelligence meets the world experts; updates and novel therapies in autoimmunity - The 14th international congress on autoimmunity 2024 (AUTO14), Ljubljana.

The bi-annual international congress on autoimmunity is a huge opportunity for the medical community to discuss the latest updates in the field. During the 14th congress 2024 (AUTO14) in Ljubljana, artificial intelligence (AI) occupied special attention due to its recent and ongoing unequivocal role in various medical fields including autoimmunity. For instance, through a challenging debate betwee...

Application of Machine Learning to Osteoporosis and Osteopenia Screening Using Hand Radiographs.

PURPOSE: Fragility fractures associated with osteoporosis and osteopenia are a common cause of morbidity and mortality. Current methods of diagnosing low bone mineral density require specialized dual x-ray absorptiometry (DXA) scans. Plain hand radiographs may have utility as an alternative screening tool, although optimal diagnostic radiographic parameters are unknown, and measurement is prone to...

Evaluation and process monitoring of jujube hot air drying using hyperspectral imaging technology and deep learning for quality parameters.

Timely and effective detection of quality attributes during drying control is essential for enhancing the quality of fruit processing. Consequently, t...

Prediction of Bone Mineral Density based on Computer Tomography Images Using Deep Learning Model.

INTRODUCTION: The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of old...

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.

Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing ...

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

BACKGROUND: Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring ded...

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 untreated, increasing the risk of fractures in older adu...

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 scoring machine learning (ML) techniques were develo...

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking.

Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the fou...

AI-Based solutions for current challenges in regenerative medicine.

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of ta...

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 oestrogen in menopausal women. Gradually, its sympto...

Predicting photosynthetic bacteria-derived protein synthesis from wastewater using machine learning and causal inference.

Causal inference-assisted machine learning was used to predict photosynthetic bacterial (PSB) protein production capacity and identify key factors. Th...

Current status and dilemmas of osteoporosis screening tools: A narrative review.

OBJECTIVE: This review aims to explore the strengths and dilemmas of existing osteoporosis screening tools and suggest possible ways of optimization, ...

Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning.

Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over t...

MemberShield: A framework for federated learning with membership privacy.

Federated Learning (FL) allows multiple data owners to build high-quality deep learning models collaboratively, by sharing only model updates and keep...

Transition from sulfur autotrophic to mixotrophic denitrification: Performance with different carbon sources, microbial community and artificial neural network modeling.

To address the limitations inherent in both sulfur autotrophic denitrification (SAD) and heterotrophic denitrification (HD) processes, this study intr...

Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen.

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current go...

Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets.

BACKGROUND: The sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving coca...

The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs.

PURPOSE: To assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence mode...

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.

OBJECTIVE: To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in ...