AIMC Topic: Spirometry

Clear Filters Showing 41 to 48 of 48 articles

Deep Learning-Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common and progressive respiratory condition characterized by persistent airflow limitation and symptoms such as dyspnea, cough, and sputum production. Acute exacerbations (AE) of COPD (AE...

Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning.

Radiology. Cardiothoracic imaging
Purpose To measure the benefit of single-phase CT, inspiratory-expiratory CT, and clinical data for convolutional neural network (CNN)-based chronic obstructive pulmonary disease (COPD) staging. Materials and Methods This retrospective study included...

Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study.

BMJ open respiratory research
RATIONALE: Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or mis...

Area under the expiratory flow-volume curve: predicted values by regression and deep learning methods and recommendations for clinical practice.

BMJ open respiratory research
BACKGROUND: In spirometry, the area under expiratory flow-volume curve (AEX-FV) was found to perform well in diagnosing and stratifying physiologic impairments, potentially lessening the need for complex lung volume testing. Expanding on prior work, ...

Improving Detection of Early Chronic Obstructive Pulmonary Disease.

Annals of the American Thoracic Society
Despite being a major cause of morbidity and mortality, chronic obstructive pulmonary disease (COPD) is frequently undiagnosed. Yet the burden of disease among the undiagnosed is significant, as these individuals experience symptoms, exacerbations, a...

Pulmonary functional parameters and blood cotinine level in chronic obstructive pulmonary disease.

Tuberkuloz ve toraks
INTRODUCTION: Smoking is the leading cause of chronic obstructive pulmonary disease (COPD) and cotinine is reliable marker of tobacco exposure. We aimed to investigate the relationship between pulmonary function tests (FVC%, FEV1, FEV1/FVC and FEF25-...

A telehealth system for automated diagnosis of asthma and chronical obstructive pulmonary disease.

Journal of the American Medical Informatics Association : JAMIA
This paper presents the development and real-time testing of an automated expert diagnostic telehealth system for the diagnosis of 2 respiratory diseases, asthma and Chronic Obstructive Pulmonary Disease (COPD). The system utilizes Android, Java, MAT...

SpiroConfidence: Determining the Validity of Smartphone Based Spirometry Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Prior work has shown that smartphone spirometry can effectively measure lung function using the phone's built-in microphone and could one day play a critical role in making spirometry more usable, accessible, and cost-effective. Although traditional ...