Osteoporosis

Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis.

BACKGROUND: Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies have highlighted the use of simple vertebral trabecular attenuation values for opportunistic osteoporosis screening. Meanwhile, machine learning has been used to accurately segment large parts of the human skeleton.

Fabrication of patterned calcium carbonate materials through template-assisted microbially induced calcium carbonate precipitation.

Patterned calcium carbonate materials with controlled morphologies have broad applications in both environmental and engineering fields. However, how to fabricate such materials through environmental-friendly methods under ambient conditions is still challenging. Here, we report a green approach for fabricating patterned calcium carbonate materials. This eco-friendly approach is based on template-...

Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.

Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on visual assessments of stained sections. Resulting sem...

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis.

: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-...

Deep Learning for Automatic Calcium Scoring in Population-Based Cardiovascular Screening.

Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed ...

Risk factors of osteoporosis in soldiers of the Armed Forces: A cross-sectional study from Western India.

BACKGROUND: Osteoporosis may result from risk factors such as smoking, alcohol, low body mass index, less physical exercise, and dietary calcium defic...

Predicting Incident Heart Failure in Women With Machine Learning: The Women's Health Initiative Cohort.

BACKGROUND: Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learn...

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation.

Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models c...

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentia...

Interpreting wide-band neural activity using convolutional neural networks.

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings....

Application of Artificial Intelligence to Cardiovascular Computed Tomography.

Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. A...

Prophylactic effect of . seed extract on inflammatory markers and histopathological changes in high-fat-fed ovariectomized rats.

BACKGROUND AND AIM: L. seeds (TFG) are used as spices in Indian cuisine. In Indian traditional medicine, TFG is used to treat diabetes, dyslipidemia,...

Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve...

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.

Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prev...

Methane production from wheat straw pretreated with CaO/cellulase.

There are various lignocellulosic biomass pretreatments that act as attractive strategies to improve anaerobic digestion for methane (CH) generation. ...

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

PURPOSE: The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC...

Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network.

BACKGROUND: Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establis...

A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs.

PURPOSE: Fractures in vertebral bodies are among the most common complications of osteoporosis and other bone diseases. However, studies that aim to p...

Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis.

BACKGROUND: Osteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatur...