Cargando…
Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning
BACKGROUND: Exhaled aerosols from lungs have unique patterns, and their variation can be correlated to the underlying lung structure and associated abnormities. However, it is challenging to characterize such aerosol patterns and differentiate their difference because of their complexity. This chall...
Autores principales: | Xi, Jinxiang, Zhao, Weizhong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354993/ https://www.ncbi.nlm.nih.gov/pubmed/30703132 http://dx.doi.org/10.1371/journal.pone.0211413 |
Ejemplares similares
-
Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification
por: Xi, Jinxiang, et al.
Publicado: (2015) -
Exhaled particles and small airways
por: Bake, B., et al.
Publicado: (2019) -
CFD Modeling and Image Analysis of Exhaled Aerosols due to a Growing Bronchial Tumor: towards Non-Invasive Diagnosis and Treatment of Respiratory Obstructive Diseases
por: Xi, Jinxiang, et al.
Publicado: (2015) -
Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis
por: Xi, Jinxiang, et al.
Publicado: (2014) -
Electrostatic Charge Effects on Pharmaceutical Aerosol Deposition in Human Nasal–Laryngeal Airways
por: Xi, Jinxiang, et al.
Publicado: (2014)