Osteoporosis

Predictive modelling of knee osteoporosis.

OBJECTIVE: The objective of this research was to develop a machine learning-based predictive model for osteoporosis screening using demographic and clinical data, including T-scores derived from calcaneus Quantitative Ultrasound (QUS). The study aimed to offer a cost-effective and accessible alternative to Dual-Energy X-ray Absorptiometry (DXA) scans, especially in resource-constrained settings.

Integrated machine learning based groundwater quality prediction through groundwater quality index for drinking purposes in a semi-arid river basin of south India.

The main objective of this study is to predict and monitor groundwater quality through the use of modern Machine Learning (ML) techniques. By employing ML techniques, the research effectively evaluates groundwater quality to forecast its future trends. Five machine learning models Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Grad...

Genetically encoded sensors illuminate detection for neurotransmission: Development, application, and optimization strategies.

Limitations in existing tools have hindered neuroscientists from achieving a deeper understanding of complex behaviors and diseases. The recent develo...

Advanced SERSome-based artificial-intelligence technology for identifying medicinal and edible homologs.

Medicinal and edible homologs (MEHs) offer significant preventive and therapeutic benefits for various diseases and health functions. However, the wid...

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography.

BACKGROUND: Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessme...

Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.

Quantifying aortic valve calcification is critical for assessing the severity of aortic stenosis, predicting cardiovascular risk, and guiding treatmen...

Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis.

BACKGROUND: Osteoporosis is a complex condition that drives research into its causes, diagnosis, treatment, and prevention, significantly affecting pa...

Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?

OBJECTIVE: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without ...

PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach.

OBJECTIVES: Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitat...

Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.

INTRODUCTION: Osteoporosis, a common condition characterised by decreased bone mass and microarchitectural deterioration, leading to increased fractur...

Precision soil sampling strategy for the delineation of management zones in olive cultivation using unsupervised machine learning methods.

Climate change and environmental degradation pose a significant threat to the global community. Soil management is one of the critical factors for ach...

An ultra-sensitive, intelligent platform for food safety monitoring: Label-free detection of illegal additives using self-assembled SERS substrates and machine learning.

To overcome the limitations of SERS in food safety monitoring, particularly significant interference from citrate ions, this study introduces an intel...

Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major...

AI integrations with lung cancer screening: Considerations in developing AI in a public health setting.

Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-exposed populations. Growing numbers of screening case...

A Surface-Enhanced Raman Spectroscopy Platform Integrating Dual Signal Enhancement and Machine Learning for Rapid Detection of Veterinary Drug Residues in Meat Products.

The detection and quantification of veterinary drug residues in meat remain a significant challenge due to the complex background interference inheren...

Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.

Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting ...

Living Microalgae-Based Magnetic Microrobots for Calcium Overload and Photodynamic Synergetic Cancer Therapy.

The combination of Ca overload and reactive oxygen species (ROS) production for cancer therapy offers a superior solution to the lack of specificity i...

GLASSR-Net: Glass Substrate Spectral Restoration Neural Network for Fourier Transform Infrared Microspectroscopy in the Fingerprint Region.

Fourier transform infrared (FTIR) microspectroscopy has emerged as a pivotal pathological tool, offering informative spectral biomarkers for numerous ...

Large Model Era: Deep Learning in Osteoporosis Drug Discovery.

Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in a d...

Artificial intelligence for osteoporosis detection on panoramic radiography: A systematic review and meta analysis.

INTRODUCTION: Osteoporosis is a disease characterized by low bone mineral density and an increased risk of fractures. In dentistry, mandibular bone mo...