Osteoporosis

Spatiotemporal Remodeling of Bone as a Reversibly Adaptive Biological Material in Djungarian Hamsters under Regulated Photoperiod Conditions.

Bone loss in humans is typically progressive and difficult to reverse, posing challenges for both pharmacological therapy and the long-term performance of bone replacement materials. Photoperiod-sensitive species such as the Djungarian hamster undergo cyclical physiological adaptations in response to seasonal light cues, providing a unique framework to investigate bone adaptation during physiologi...

Automatic classification of kidney stone components based on smartphone microscopy and the GoogLeNet model.

BACKGROUND: To develop an automated classification system for urinary stone composition by integrating smartphone-based microscopic imaging (TIPSCOPE) with the GoogLeNet architecture, with the goal of enabling rapid, accurate, and cost-effective analysis of stone composition. METHODS: A total of 140 surgically extracted kidney stone samples were collected and classified into four categories: calci...

Multimodal MRI reveals hypothalamic structural-functional alterations associated with bone mineral density loss in postmenopausal women.

Emerging evidence implicates central nervous system dysregulation in the pathogenesis of osteoporosis. However, the role of hypothalamic subregions - ...

Phenotypic clustering of cardiovascular risk profiles in systemic lupus erythematosus.

ObjectiveThis study aimed to identify distinct cardiovascular risk phenotypes in systemic lupus erythematosus (SLE) using an unsupervised cluster anal...

Clinical impact of adopting an AI-driven opportunistic bone health screening software using routine X-ray.

OBJECTIVES: To evaluate the clinical impact of an artificial intelligence device, Rho, that opportunistically screens X-rays for low bone mineral dens...

Artificial intelligence on opportunistic computed tomography for predicting vertebral fracture risk in women undergoing estrogen deprivation therapy for breast cancer.

PURPOSE: Fracture risk prediction in women exposed to hormone deprivation therapies (HDTs) for breast cancer is challenging, since bone mineral densit...

Integrated LIBS-Raman spectroscopy coupled with explainable machine learning for biochemical characterization of melanoma.

Melanoma remains one of the most aggressive and lethal cutaneous malignancies, underscoring the need for objective and label-free diagnostic methods t...

Deep Learning Reconstruction Combined with Contrast-Enhancement Boost Technique in "Quadruple-low" CCTA Protocol: Evaluation of Image Quality and Diagnostic Accuracy.

RATIONALE AND OBJECTIVES: To evaluate the impact of a deep learning reconstruction (DLR) algorithm combined with contrast-enhancement boost (CE-boost)...

Recognition of counterfeit identity documents using FTIR spectroscopy and machine learning approaches.

Document forgery represents a persistent challenge in forensic science, demanding analytical approaches capable of identifying chemical and structural...

DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.

Coronary artery disease (CAD), one of the leading causes of mortality worldwide, necessitates effective risk assessment strategies, with coronary arte...

Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning.

Developing and validating a machine learning model for predicting post-thrombolysis seizures in acute ischemic stroke.

Post-stroke seizures (PSS) manifests variably due to ischemic brain injury, yet its risk factors remain unclear. This study developed a machine learni...

A fast fully automated approach for evaluating calcified lesions in intravascular ultrasound.

AIMS: Catheter-based coronary intervention is an effective treatment for acute coronary syndrome. However, calcified plaques pose significant challeng...

Open-Source Machine Learning Computed Tomography Scan Segmentation for Spine Osteoporosis Diagnostics.

BACKGROUND AND OBJECTIVES: Dual-energy x-ray absorptiometry (DXA) is the standard for assessing bone mineral density (BMD); however, its accuracy is l...

Association of urinary heavy metals with osteoporosis in US adults using interpretable machine learning.

BACKGROUND: Exposure to heavy metals in the environment has always been the focus of public concern. More and more evidence suggests that heavy metal ...

Machine learning-based prediction of CAC-defined cardiovascular risk using routine health examination data: a retrospective cross-sectional study in a Taiwanese population.

BACKGROUND: Early identification of individuals at elevated cardiovascular risk using routine health examination data is essential for preventive card...

Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are o...

Discovery of High-Affinity Glutamine-Derived Peptides from Wheat Gliadin Targeting CaSR: a Computational Approach Integrating Deep Learning and Molecular Dynamics.

Calcium-sensing receptor (CaSR)-targeting agonist peptides play a crucial role in maintaining intestinal homeostasis. In this study, we integrated dee...

Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.

Purpose To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnose...

Osteoporosis prediction using lumbar CT Hounsfield units: comparative performance and clinical implications of seven machine learning models.

PURPOSE: This study aimed to evaluate the utility of L1-L4 average Hounsfield Unit (HU) values from lumbar spine computed tomography (CT) in predictin...