ArticlePloS one2015
Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 24 citations in OpenAlex.
- Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Simplifying the complex: accessible microfluidic solutions for contemporary processes withinLab on a chip · 2022Review
- Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning.PloS one · 2019Article
- Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols.Analytica chimica acta · 2018Review
- Particle Disposition in the Realistic Airway Tree Models of Subjects with Tracheal Bronchus and COPD.BioMed research international · 2018Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEach lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases. OBJECTIVE AND
methodsIn this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions.
findingsBy employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations.
conclusionFor the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases.
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Registered trials
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