Steroids

Deep learning for automatic mandible segmentation on dental panoramic x-ray images.

Many studies in the last decades have correlated mandible bone structure with systemic diseases like osteoporosis. Mandible segmentation, as well as segmentation of other oral structures, is an essential step in studies that correlate oral structures' conditions with systemic diseases in general. However, manual mandible segmentation is a time-consuming and training-required task that suffers from...

Advances in materials-based therapeutic strategies against osteoporosis.

Osteoporosis is caused by the disruption in homeostasis between bone formation and bone resorption. Conventional management of osteoporosis involves systematic drug administration and hormonal therapy. These treatment strategies have limited curative efficacy and multiple adverse effects. Biomaterials-based therapeutic strategies have recently emerged as promising alternatives for the treatment of...

Artificial Neural Network-Assisted Classification of Hearing Prognosis of Sudden Sensorineural Hearing Loss With Vertigo.

This study aimed to determine the impact on hearing prognosis of the coherent frequency with high magnitude-squared wavelet coherence (MSWC) in video ...

Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation.

BACKGROUND: Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-lear...

Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches.

Osteoporosis contributes significantly to health and economic burdens worldwide. However, the development of osteoporosis-related prediction tools has...

Molecular Networking and On-Tissue Chemical Derivatization for Enhanced Identification and Visualization of Steroid Glycosides by MALDI Mass Spectrometry Imaging.

Spatial metabolomics describes the spatially resolved analysis of interconnected pathways, biochemical reactions, and transport processes of small mol...

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis.

The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing ...

Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method.

INTRODUCTION: One of the current approaches to improve our understanding of osteoporosis is to study the development of bone microdamage under mechani...

Automated classification of estrous stage in rodents using deep learning.

The rodent estrous cycle modulates a range of biological functions, from gene expression to behavior. The cycle is typically divided into four stages,...

A wearable soft robot that can alleviate the pain and fear of the wearer.

Social soft robotics may provide a new solution for alleviating human pain and fear. Here, we introduce a hand-held soft robot that can be clenched by...

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.

BACKGROUND: Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely unde...

Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population.

PURPOSE: Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for scr...

Improvement of Patient Classification Using Feature Selection Applied to Bidirectional Axial Transmission.

Osteoporosis is still a worldwide problem, particularly due to associated fragility fractures. Patients at risk of fracture are currently detected usi...

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning.

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an impor...

Nuclear morphology is a deep learning biomarker of cellular senescence.

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack...

Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.

BACKGROUND AND PURPOSE: To investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening b...

Integrated biomarker approach strongly explaining in vivo sub-lethal acute toxicity of butachlor on Labeo rohita.

Butachlor herbicide belongs to the family of chloroacetanilide group, widely used for control of grass and broadleaf weeds in paddy fields however, it...

Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network.

OBJECTIVES: This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using p...

Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis.

BACKGROUND AND OBJECTIVE: The specific aim of this study is to develop machine learning models as a clinical approach for personalized treatment of os...

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation.

Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include co...