Osteoporosis

Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks.

Cellular calcium fluorescence imaging utilized to study cellular behaviors typically results in large datasets and a profound need for standardized and accurate analysis methods. Here, we describe open-source software (4SM) to overcome these limitations using an automated machine learning pipeline for subcellular calcium signal segmentation of spatiotemporal maps. The primary use of 4SM is to anal...

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.

Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent ...

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...

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.

Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristical...

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

BACKGROUND: Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. C...

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...

Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning.

Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted c...

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...

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiaut...

Development of a spontaneous pain indicator based on brain cellular calcium using deep learning.

Chronic pain remains an intractable condition in millions of patients worldwide. Spontaneous ongoing pain is a major clinical problem of chronic pain ...

Feasibility of Laser Lithotripsy for Midsize Stones Using Robotic Retrograde Intrarenal Surgery System easyUretero in a Porcine Model.

To test the safety and feasibility of laser lithotripsy for midsize renal stones using a newly developed robotic retrograde intrarenal surgery (RIRS)...

Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos.

To assess the performance and added value of processing complete digital endoscopic video sequences for the automatic recognition of stone morphologic...

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...

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...

Validating Accuracy of an Internet-Based Application against USDA Computerized Nutrition Data System for Research on Essential Nutrients among Social-Ethnic Diets for the E-Health Era.

Internet-based applications (apps) are rapidly developing in the e-Health era to assess the dietary intake of essential macro-and micro-nutrients for ...

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...

Microrobotic Swarms for Intracellular Measurement with Enhanced Signal-to-Noise Ratio.

In cell biology, fluorescent dyes are routinely used for biochemical measurements. The traditional global dye treatment method suffers from low signal...

Using a deep learning algorithm to score coronary artery calcium in myocardial perfusion imaging: A real opportunity or just a new hype?