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Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

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  • Jinxiang Xi
  • Weizhong Zhao
  • Jiayao Eddie Yuan
  • JongWon Kim
  • Xiuhua Si
  • Xiaowei Xu

Abstract

Background: Each 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 Methods: In 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. Findings: By 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. Conclusion: For 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.

Suggested Citation

  • Jinxiang Xi & Weizhong Zhao & Jiayao Eddie Yuan & JongWon Kim & Xiuhua Si & Xiaowei Xu, 2015. "Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-19, September.
  • Handle: RePEc:plo:pone00:0139511
    DOI: 10.1371/journal.pone.0139511
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    References listed on IDEAS

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    1. Sernetz, M. & Wübbeke, J. & Wlczek, P., 1992. "Three-dimensional image analysis and fractal characterization of kidney arterial vessels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 191(1), pages 13-16.
    2. Jinxiang Xi & Xiuhua A Si & JongWon Kim & Edward Mckee & En-Bing Lin, 2014. "Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    3. B. Mauroy & M. Filoche & E. R. Weibel & B. Sapoval, 2004. "An optimal bronchial tree may be dangerous," Nature, Nature, vol. 427(6975), pages 633-636, February.
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    Cited by:

    1. Sijilmassi, Ouafa & López Alonso, José-Manuel & Del Río Sevilla, Aurora & Barrio Asensio, María del Carmen, 2020. "Multifractal analysis of embryonic eye structures from female mice with dietary folic acid deficiency. Part I: Fractal dimension, lacunarity, divergence, and multifractal spectrum," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Jinxiang Xi & Weizhong Zhao, 2019. "Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-22, January.
    3. Jinxiang Xi & Xiuhua April Si, 2018. "Review of Feature Extraction from Exhaled Aerosol Fingerprints to Diagnose Lung Structural Remolding," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 11(3), pages 8504-8508, November.

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