Osteoporosis

Deep learning-based material decomposition of iodine and calcium in mobile photon counting detector CT.

Photon-counting detector (PCD)-based computed tomography (CT) offers several advantages over conventional energy-integrating detector-based CT. Among them, the ability to discriminate energy exhibits significant potential for clinical applications because it provides material-specific information. That is, material decomposition (MD) can be achieved through energy discrimination. In this study, de...

Deep learning application of vertebral compression fracture detection using mask R-CNN.

Vertebral compression fractures (VCFs) of the thoracolumbar spine are commonly caused by osteoporosis or result from traumatic events. Early diagnosis of vertebral compression fractures can prevent further damage to patients. When assessing these fractures, plain radiographs are used as the primary diagnostic modality. In this study, we developed a deep learning based fracture detection model that...

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review.

PURPOSE: This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for str...

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data...

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindi...

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the...

Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on multiple machine learning methods.

Postoperative venous thromboembolic events (VTEs), such as lower extremity deep vein thrombosis (DVT), are major risk factors for gastric cancer (GC) ...

Expanding Approaches to Improve Orthopaedic Care Through the Application of Artificial Intelligence: Commentary on an article by Neil P. Sheth, MD, et al.: "Effects of Abaloparatide on Bone Mineral Density in Proximal Femoral Regions Corresponding to Arthroplasty Gruen Zones. A Study of Postmenopausal Women with Osteoporosis".

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.

BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some con...

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning.

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts da...

Identifying factors associated with locomotive syndrome using machine learning methods: The third survey of the research on osteoarthritis/osteoporosis against disability study.

AIM: To identify factors associated with locomotive syndrome (LS) using medical questionnaire data and machine learning.

Predicting the governing factors for the release of colloidal phosphorus using machine learning.

Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on...

Infusion of active compound into sliced button mushrooms through vacuum impregnation to improve functionality: Comparing response surface methodology and artificial neural network.

The present study explores the infusion of active compounds (ascorbic acid and calcium lactate) into sliced button mushrooms (Agaricus bisporus) to in...

The potential role for artificial intelligence in fracture risk prediction.

Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these ...

Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to .

(Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant ...

Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.

PURPOSE: Osteoporosis is a common bone disorder characterized by decreased bone mineral density (BMD) and increased bone fragility, which can lead to ...

Enhancing trabecular CT scans based on deep learning with multi-strategy fusion.

Trabecular bone analysis plays a crucial role in understanding bone health and disease, with applications like osteoporosis diagnosis. This paper pres...

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.

Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low sol...

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients.

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited b...

A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques.

PURPOSE: The main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analys...