Review of Feature Extraction from Exhaled Aerosol Fingerprints to Diagnose Lung Structural Remolding
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2018, Vol 11, Issue 3
Abstract
Previous studies indicated that the patterns of the exhaled aerosol distributions, even though seemingly chaotic, are indeed unique with respect to the airway geometry. These patterns will differ whenever there is a change to the airway geometry. It is hypothesized that each lung structure has a unique Aerosol Fingerprint (AFP). As such, any difference from the normal AFP indicates an anomaly in the lung structure. However, these exhaled aerosol profiles exhibit highly complex patterns and should be quantified as feature vectors before they can be used for classification. This paper reviews the feature extraction algorithms to characterize the aerosol fingerprints from different airway geometries. These include local deposition fraction, fractal dimension, multifractal dimension, spatial-temporal dynamics and unsupervised feature extraction (deep learning).Symptomless development of lung cancer leads to high mortality rates of lung cancer patients. It is expected that the mortality rate can be dramatically reduced if cancer can be detected and treated at its early stage. However, effective methods for early cancer detection are lacking despite years of research [1]. Bronchoscope and percutaneous lung needle biopsy have the capacity of detecting and diagnosing lung cancers but are not used for of screening because of their invasive nature. In recent years, Low-Dose (LD) CT scanning gain popularity in screening lung cancers in senior populations who are or have been heavy smokers and are considered to have a high lung cancer risk. However, the reliability and cost-to-benefit ratio still need to be improved. A large-scale two-year trial (2002-2004) with 53,454 participants demonstrated that LDCT could increase the survival rate of lung cancer by 20% [2]. Moreover, LDCT was known to have a high false positive rate, overdiagnosis, and potential risk from long-term radiation exposure [2].Serval studies have investigated the feasibility of employing aerosols to diagnose respiratory diseases. One example is the method of Aerosol Bolus Dispersion (ABD) [3,4], which measures the temporal concentrations of exhaled aerosols to gauge the lung health. It is noted that the ABD method provides no extra information in comparison to current lung function tests [4]. Recently, we proposed an exhalation breath test that is promising to detect lung diseases as well as their locations. This method was based on persistent exhaled aerosol patterns from a given lung geometry [5-9]. In other words, each lung has a unique exhaled aerosol distribution, which is called the signature “Aerosol Fingerprint (AFP)”, to differentiate from the “gas fingerprint” used in VOC-based breath tests [10]. It is hypothesized that a deviation from the normal AFP pattern will be indicative of lung geometry remodeling or tumorigenesis.
Authors and Affiliations
Jinxiang Xi, Xiuhua April Si
Keywords
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- EP ID EP592372
- DOI 10.26717/BJSTR.2018.11.002097
- Views 160
- Downloads 0
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