Osteoporosis

Application of machine learning in osteoporosis screening: a narrative review.

Machine learning has the potential to address limitations of traditional osteoporosis screening through advanced data processing and pattern recognition capabilities. This review provides a critical analysis of ML applications in opportunistic screening, precision risk assessment, and quantitative bone quality analysis, while addressing current implementation challenges and future research directi...

Calcined coal gangue-derived Mg- and Ca-based layered double hydroxides for Cr(VI) soil remediation: Experiments and machine learning.

Layered double hydroxides (LDHs), including magnesium-aluminum-type (Mg-C-LDH) and calcium-aluminum-type (Ca-C-LDH), were synthesized from coal gangue and calcined at 500 °C to form layered double oxides (LDOs). Both materials exhibited effective Cr(VI) immobilization in contaminated soils. Under optimal conditions, magnesium-based material achieved a maximum immobilization efficiency of 95.64 %, ...

AI-enabled cardiac volumetry on non-contrast calcium scoring CT for predicting atrial fibrillation and mortality.

BACKGROUND: The prognostic value of cardiac volumetry derived from non-contrast coronary calcium scoring CT (CSCT) remains uncertain. This study evalu...

Comment on "Assessing deep learning model performance in osteoporosis screening with lumbar spine radiographs".

Predicting Osteoporosis Risk from Knee Radiographs and Clinical Features through Deep Learning: A Multimodal Approach.

BACKGROUND: Osteoporosis is a prevalent skeletal disease with high morbidity rates in developing countries due to limited access to gold-standard dual...

Opportunistic Cardiovascular Risk Assessment Using Routine Head CT in the Emergency Department.

BACKGROUND: Routine noncardiac computed tomography (CT) imaging may contain information about cardiovascular risk. Head computed tomography (CTH) is a...

AI-Derived LA Volume Index, LA/RA and LA/LV Volume Ratios From Coronary Artery Calcium Scans Predict Long-Term Atrial Fibrillation and Stroke.

BACKGROUND: The AI-CVD initiative aims to extract actionable insights from coronary artery calcium (CAC) scans beyond the traditional CAC score. Altho...

Association Between Breast Arterial Calcifications and Increased Cardiovascular Risk: A Canadian Perspective on Clinical Implications and the Potential Role in Preventive Care.

Breast arterial calcifications (BAC) are associated with increased cardiovascular risk and have been correlated with other methods of cardiovascular r...

AI-Assisted Dialysis Decision-Making: Assessing Agreement Between ChatGPT and Nephrologist in Initial Dialysis Indication and Prescription in Emergency and ICU Settings.

BACKGROUND Artificial intelligence (AI) is increasingly explored as a clinical decision-support tool in nephrology; however, its real-world applicabil...

Development and external validation of a machine learning model for predicting chronic critical illness in ICU patients with acute pancreatitis.

BACKGROUND: To develop and validate a machine learning (ML) model to assess the risk of chronic critical illness (CCI) in intensive care unit (ICU) pa...

Integrating Machine Learning into Fracture Liaison Services: Toward precision risk stratification in osteoporosis care: A prospective cohort study.

BACKGROUND: Traditional fracture risk assessment tools have limitations in accurately predicting re-fracture risk. Machine learning (ML) approaches of...

High-resolution LA-ICP-MS imaging combined with attention-enhanced residual network reveal cadmium-induced metal distribution heterogeneity and toxicity in Mytilus galloprovincialis.

Marine bivalves often face heavy metal stress, yet we still lack of high-resolution way to observe the metal pollution status in soft tissue. This stu...

Apoptotic extracellular vesicles act as master regulators of the bone healing niche.

Apoptotic extracellular vesicles (ApoEVs), natural bilayer nanoparticles released during programmed cell death, have emerged as pivotal regulators and...

Predicting one-year mortality risk in ICU patients with ischemic stroke using multi-algorithm machine learning and a nomogram.

Introduction: Ischemic stroke is a leading cause of mortality, and patients requiring intensive care unit (ICU) admission carry a guarded prognosis. W...

Recycling waste concrete into alkali-activated geo-binders for sand stabilization.

This study investigates the feasibility of using waste concrete powder (WCP) as an alkali-activated binder (AAB) for sustainable soil stabilization. S...

Real-World Implementation of Artificial Intelligence: Fully Automated Coronary Artery Calcium Scoring.

Automated abdominal aortic calcification and trabecular bone score independently predict incident fracture during routine osteoporosis screening.

Abdominal aortic calcification (AAC), a marker of subclinical cardiovascular disease, has previously shown to be associated with low BMD and fracture....

Using machine learning algorithms to predict MACE in peritoneal dialysis patients.

The study aimed to predict the risks of Major adverse cardiac events (MACE) in patients undergoing peritoneal dialysis (PD) with machine learning (ML)...

Identification of biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis based on bulk and single-cell transcriptomic analyses and experimental validation.

Osteoporosis (OP) is a metabolic bone disease characterized by low bone mineral density (BMD), and its pathogenesis involves endoplasmic reticulum (ER...

AI-derived left-to-right cardiac chamber volume ratios in coronary artery calcium scans strongly predict heart failure.

AIMS: The AI-CVD initiative seeks to extract actionable insights from coronary artery calcium (CAC) scans beyond the traditional CAC score. We previou...