COPD

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography.

OBJECTIVES: To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothe...

AI in predicting COPD in the Canadian population.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that produces non-reversible airflow limitations. Approximately 10% of Canadians aged 35 years or older are living with COPD. Primary care is often the first contact an individual will have with the healthcare system providing acute care, chronic disease management, and services aimed at health maintenance. This study used ...

Utilization of Ultrasonic Image Characteristics Combined with Endoscopic Detection on the Basis of Artificial Intelligence Algorithm in Diagnosis of Early Upper Gastrointestinal Cancer.

The aim of this study was to evaluate the diagnostic value of artificial intelligence algorithm combined with ultrasound endoscopy in early esophageal...

Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection.

The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with sever...

A ferret brain slice model of oxygen-glucose deprivation captures regional responses to perinatal injury and treatment associated with specific microglial phenotypes.

Organotypic brain slice models are an ideal technological platform to investigate therapeutic options for hypoxic-ischemic (HI) brain injury, a leadin...

Intelligent Monitoring of Care Status for COPD Patients Based on Deep Learning.

To discuss the application method and effect of COPD patients in deep learning in intelligent monitoring, two groups were used under a reasonable sele...

Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression.

Non-contact physiological measurements have been under investigation for many years, and among these measurements is non-contact spirometry, which cou...

Identification of asthma control factor in clinical notes using a hybrid deep learning model.

BACKGROUND: There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician's documentation is ...

Machine learning implicates the IL-18 signaling axis in severe asthma.

Asthma is a common disease with profoundly variable natural history and patient morbidity. Heterogeneity has long been appreciated, and much work has ...

Deep Learning Algorithms-Based CT Images in Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction.

CT image information data under deep learning algorithms was adopted to evaluate small airway function and analyze the clinical efficacy of different ...

Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes.

Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery...

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental ...

New sensing platform of poly(ester-urethane)urea doped with gold nanoparticles for rapid detection of mercury ions in fish tissue.

A new electrochemical sensor has been fabricated based on the synthesis of poly(ester-urethane) urea (PUU) doped with gold nanoparticles (AuNPs), and...

A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data.

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be...

Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.

Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Cli...

Intravenous Iron Replacement Improves Exercise Tolerance in COPD: A Single-Blind Randomized Trial.

INTRODUCTION: Iron deficiency affects exercise capacity because of the critical role iron plays in the optimal functioning of skeletal muscle metaboli...

Use of a Digital Chronic Obstructive Pulmonary Disease Respiratory Tracker in a Primary Care Setting: A Feasibility Study.

INTRODUCTION: Telemonitoring is a promising self-management strategy to improve health care outcomes. This study evaluated real-world adoption of the ...

Development of a gold-nanorod-based lateral flow immunoassay for a fast and dual-modal detection of C-reactive protein in clinical plasma samples.

Fast and simple detection of C-reactive protein (CRP) is highly significant for the diagnosis and prognosis of inflammatory or infectious diseases. La...

A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data.

Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients ...

Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.

Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible t...