Osteoporosis

Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.

Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accuracy of FFR-CT analysis may potentially be decreased by severely calcified coronary arteries because of blooming and beam hardening artifacts. The aim ...

Distribution Patterns of Subgroups of Inhibitory Neurons Divided by Calbindin 1.

The inhibitory neurons in the brain play an essential role in neural network firing patterns by releasing γ-aminobutyric acid (GABA) as the neurotransmitter. In the mouse brain, based on the protein molecular markers, inhibitory neurons are usually to be divided into three non-overlapping groups: parvalbumin (PV), neuropeptide somatostatin (SST), and vasoactive intestinal peptide (VIP)-expressing ...

Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding.

Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming i...

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning.

The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively ...

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria.

RATIONALE AND OBJECTIVES: Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental...

Artificial intelligence for coronary artery calcium scoring: A new trick for an old dog?

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction.

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis p...

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

PURPOSE: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. The...

A Laser-Driven Microrobot for Thermal Stimulation of Single Cells.

Here, the study presents a thermally activated cell-signal imaging (TACSI) microrobot, capable of photothermal actuation, sensing, and light-driven lo...

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics.

PURPOSE: Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the acc...

Opportunistic Screening: Scientific Expert Panel.

Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematic...

Patient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data?

There is widespread use of dietary supplements, some prescribed but many taken without a physician's guidance. There are many potential interactions b...

A deep learning-based automated image analysis for histological evaluation of broiler pectoral muscle.

Global market demand for chicken breast muscle with high yield and quality, together with the high incidence rate of breast muscle abnormalities in re...

Targeting operational regimes of interest in recurrent neural networks.

Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousa...

FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications.

Miniaturized calcium imaging is an emerging neural recording technique that has been widely used for monitoring neural activity on a large scale at a ...

Artificial intelligence in coronary artery calcium measurement: Barriers and solutions for implementation into daily practice.

Coronary artery calcification (CAC) measurement is a valuable predictor of cardiovascular risk. However, its measurement can be time-consuming and com...

Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography.

Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely...

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesio...

Learnable latent embeddings for joint behavioural and neural analysis.

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data incre...

Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification.

BACKGROUND: Artificial intelligence (AI) applied to cardiac imaging may provide improved processing, reading precision and advantages of automation. C...