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Spirometry

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Curve-Modelling and Machine Learning for a Better COPD Diagnosis.

International journal of chronic obstructive pulmonary disease
BACKGROUND: Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD...

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.

The journal of allergy and clinical immunology. In practice
BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.

Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study.

Respiratory research
BACKGROUND: The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory symptoms and preserved pulmonary function(PPF). This paper evaluated the performance of several ML a...

A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan.

The Lancet. Digital health
BACKGROUND: Chest x-ray is a basic, cost-effective, and widely available imaging method that is used for static assessments of organic diseases and anatomical abnormalities, but its ability to estimate dynamic measurements such as pulmonary function ...

Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial.

BMJ open
INTRODUCTION: Spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whe...

Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning.

Academic radiology
RATIONALE AND OBJECTIVES: The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with compute...

Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations.

Computers in biology and medicine
PROBLEM: Current spirometers face challenges in evaluating acceptability criteria, often requiring manual visual inspection by trained specialists. Automating this process could improve diagnostic workflows and reduce variability in test assessments.

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 for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.

NPJ systems biology and applications
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but the...

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...