COPD

A deep learning approach to identifying immunogold particles in electron microscopy images.

Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain co...

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added va...

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic...

Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults.

Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear w...

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it gr...

Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks.

Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The econ...

Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach.

PURPOSE: To develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients...

CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network.

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilita...

Novel Machine Learning Can Predict Acute Asthma Exacerbation.

BACKGROUND: Asthma exacerbations result in significant health and economic burden, but are difficult to predict.

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data.

Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recent...

Positive Surgical Margins After Robot-Assisted Partial Nephrectomy Predict Long-Term Oncologic Outcomes for Clinically Localized Renal Masses.

For patients with clinically localized renal masses, positive surgical margins (PSMs) after robotic partial nephrectomy (RPN) have been associated wi...

Prediction of Obstructive Lung Disease from Chest Radiographs via Deep Learning Trained on Pulmonary Function Data.

BACKGROUND: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is often underdiagnosed.

A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning.

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a ...

Utilizing an Artificial Neural Network to Predict Self-Management in Patients With Chronic Obstructive Pulmonary Disease: An Exploratory Analysis.

PURPOSE: The main objective of this study was to utilize an artificial neural network in an exploratory fashion to predict self-management behaviors b...

Deep-learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria.

RATIONALE: While American Thoracic Society (ATS)/European Respiratory Society (ERS) quality control criteria for spirometry include several quantitati...

Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores.

PURPOSE: To develop radiomics-based CT scores for assessing lung disease severity and exacerbation risk in adult patients with cystic fibrosis (CF).

Label-Free Detection of Based on Bacteria-Imprinted Polymer and Turn-on Fluorescence Probes.

The effective identification and quantitative determination of is a major public health concern. Here, an innovative strategy that combines a bacteri...

Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training.

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise...

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation.

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease ...

Early-Stage Feasibility of a Mobile Health Intervention (Copilot) to Enhance Exacerbation-Related Self-Management in Patients With Chronic Obstructive Pulmonary Disease: Multimethods Approach.

BACKGROUND: There is an emergence of mobile health (mHealth) interventions to support self-management in patients with chronic obstructive pulmonary d...