Osteoporosis

Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models

Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current research in large-scale neuro-behavioral data analysis often relies on decoding models, which quantify behavioral information in neural data but lack details on behavior encoding. This raises an intriguing scientific question: ``how can we enable in-depth exploration of neural representations in behavioral ...

Modeling Alzheimer's Disease: From Memory Loss to Plaque & Tangles Formation

We employ the Hopfield model as a simplified framework to explore both the memory deficits and the biochemical processes characteristic of Alzheimer's disease. By simulating neuronal death and synaptic degradation through increasing the number of stored patterns and introducing noise into the synaptic weights, we demonstrate hallmark symptoms of dementia, including memory loss, confusion, and de...

TAVRNN: Temporal Attention-enhanced Variational Graph RNN Captures Neural Dynamics and Behavior

We introduce Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a novel framework for analyzing the evolving dynamics ...

Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank.

Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed ...

Vertebral compression fractures at abdominal CT: underdiagnosis, undertreatment, and evaluation of an AI algorithm.

Vertebral compression fractures (VCFs) are common and indicate a high future risk of additional osteoporotic fractures. However, many VCFs are unrepor...

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

OBJECTIVE: To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopen...

NeuroBind: Towards Unified Multimodal Representations for Neural Signals

Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, me...

Improved Segmentation of Confocal Calcium Videos of Hela Cells Using Deep-Learning-Assisted Watershed Algorithm.

The potential of calcium imaging in high-throughput drug screening experiments remains underutilized, primarily because of time-intensive manual ident...

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The ...

[Exploration of prognostic models for chronic rhinosinusitis with nasal polyps based on machine learning].

To analysis the molecular characteristics of chronic rhinosinusitis with nasal polyps (CRSwNP), to unravel its pathophysiological mechanisms, and to ...

AI and Chest Radiographs: A Dawning Era in Osteoporosis Screening.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial.

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening f...

Identification of disease-specific genes related to immune infiltration in nonalcoholic steatohepatitis using machine learning algorithms.

To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gen...

Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.

This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating geneti...

Deep learning in the radiologic diagnosis of osteoporosis: a literature review.

OBJECTIVE: Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and suscepti...

Mammography-based deep learning model for coronary artery calcification.

AIMS: Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep le...

Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork.

Low level of drip loss (DL) is an important quality characteristic of meat with high economic value. However, the key genes and regulatory networks co...

A Multi-Task Learning Approach for Segmentation of Breast Arterial Calcifications in Screening Mammograms.

Screening mammogram is a standard and cost-efficient imaging procedure to measure breast cancer risk among 45+ year old women. Quantifying breast arte...

Machine learning model for osteoporosis diagnosis based on bone turnover markers.

To assess the diagnostic utility of bone turnover markers (BTMs) and demographic variables for identifying individuals with osteoporosis. A cross-sect...

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics.

BACKGROUND: Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neu...