Xi Jinxiang, Zhao Weizhong, Yuan Jiayao Eddie, Kim JongWon, Si Xiuhua, Xu Xiaowei
School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.
College of Information Engineering, Xiangtan University, Xiangtan, Hunan Province, China; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Jefferson, Arkansas, United States of America.
PLoS One. 2015 Sep 30;10(9):e0139511. doi: 10.1371/journal.pone.0139511. eCollection 2015.
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.
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.
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.
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.
每个肺部结构呼出的气溶胶模式独特，可用于非侵入性地检测和监测肺部疾病。挑战在于准确解读呼出的气溶胶指纹图谱并将其与肺部疾病进行定量关联。
在本研究中，我们提出了一种呼出气溶胶测试范式，该范式解决了上述两个挑战，有望检测肺部疾病的部位和严重程度。此范式包括两个步骤：使用子区域分形分析进行图像特征提取以及使用支持向量机（SVM）进行数据分类。进行了数值实验以评估该呼气测试在四种哮喘肺部模型中的可行性。采用高保真图像 - 计算流体动力学（CFD）方法来计算不同疾病状况下呼出的气溶胶模式。
通过采用10折交叉验证方法，使用理想的108样本数据集时，我们在四种哮喘模型中实现了100%的分类准确率，使用更现实的324样本数据集时准确率为99.1%。分形 - SVM分类器已被证明具有鲁棒性，对结构变化高度敏感，并且本质上适合研究气溶胶与疾病的相关性。
本研究首次将呼出的气溶胶模式与其潜在疾病进行了定量关联，为开发用于非侵入性检测阻塞性呼吸道疾病的计算机辅助诊断系统奠定了基础。
