Multi-resolution classification of exhaled aerosol images to detect obstructive lung diseases in small airways.

Exhaled aerosol patterns have been used to detect obstructive respiratory diseases in the upper airways. Signals from small airway diseases are weak and may not manifest themselves in the exhaled aerosol patterns. Therefore, it will be more challenging to detect abnormalities in small airways. The objective of this study is to develop a simulation-based classification model that can accurately classify small airway diseases. The model performance was evaluated in five obstructed models that are located in lung bifurcations G7-9. The exhaled aerosol images were quantified using local fractal dimensions at different sampling resolutions (n × n). The datasets were classified using both the random forest (RF) and support vector machine (SVM) algorithms. Results show that RF performs slightly and persistently better than SVM. The sampling resolution of 12 × 12 gave the optimal classification for both algorithms. Based on the lung models with predefined obstructive levels, the optimal classification accuracy is 87.0% for 5-class classification, and is 92.5% for 4-class classification by regrouping the mislabeled samples. The proposed model with multi-resolution fractal feature extraction and RF algorithm appears to be sensitive enough to accurately distinguish airway abnormalities in small airways beyond G7 with healthy bronchiole diameter <4 mm. This aerosol-based breath test is promising to develop into an alternative or supplemental tool to the low-dose CT scanning for lung cancer screening.

Simulation study of electric-guided delivery of 0.4µm monodisperse and polydisperse aerosols to the ostiomeatal complex.

Despite the high prevalence of rhinosinusitis, current inhalation therapy shows limited efficacy due to extremely low drug delivery efficiency to the paranasal sinuses. Novel intranasal delivery systems are needed to enhance targeted delivery to the sinus with therapeutic dosages. An optimization framework for intranasal drug delivery was developed to target polydisperse charged aerosols to the ostiomeatal complex (OMC) with electric guidance. The delivery efficiency of a group of charged aerosols recently reported in the literature was numerically assessed and optimized in an anatomically accurate nose-sinus model. Key design variables included particle charge number, particle size and distribution, electrode strength, and inhalation velocity. Both monodisperse and polydisperse aerosol profiles were considered. Results showed that the OMC delivery efficiency was highly sensitive to the applied electric field and electrostatic charges carried by the particles. Through the synthesis of electric-guidance and point drug release, focused deposition with significantly enhanced dosage in the OMC can be achieved. For 0.4 µm charged aerosols, an OMC delivery efficiency of 51.6% was predicted for monodisperse aerosols and 34.4% for polydisperse aerosols. This difference suggested that the aerosol profile exerted a notable effect on intranasal deliveries. Sensitivity analysis indicated that the OMC deposition fraction was highly sensitive to the charge and size of particles and was less sensitive to the inhalation velocity considered in this study. Experimental studies are needed to validate the numerically optimized designs. Further studies are warranted to investigate the targeted OMC delivery with both electric and acoustics controls, the latter of which has the potential to further deliver the drug particles into the sinus cavity.

Visualization and Quantification of Nasal and Olfactory Deposition in a Sectional Adult Nasal Airway Cast.

PURPOSE: To compare drug deposition in the nose and olfactory region with different nasal devices and administration techniques. A Sar-Gel based colorimetry method will be developed to quantify local deposition rates. METHODS: A sectional nasal airway cast was developed based on an MRI-based nasal airway model to visualize deposition patterns and measure regional dosages. Four nasal spray pumps and four nebulizers were tested with both standard and point-release administration techniques. Delivered dosages were measured using a high-precision scale. The colorimetry correlation for deposited mass was developed via image processing in Matlab and its performance was evaluated through comparison to experimental measurements. RESULTS: Results show that the majority of nasal spray droplets deposited in the anterior nose while only a small fraction (less than 4.6%) reached the olfactory region. For all nebulizers considered, more droplets went beyond the nasal valve, leading to distinct deposition patterns as a function of both the nebulizer type (droplet size and initial speed) and inhalation flow rate. With the point-release administration, up to 9.0% (±1.9%) of administered drugs were delivered to the olfactory region and 15.7 (±2.4%) to the upper nose using Pari Sinus. CONCLUSIONS: Standard nasal devices are inadequate to deliver clinically significant olfactory dosages without excess drug losses in other nasal epitheliums. The Sar-Gel based colorimetry method appears to provide a simple and practical approach to visualize and quantify regional deposition.

Design and Testing of Electric-Guided Delivery of Charged Particles to the Olfactory Region: Experimental and Numerical Studies.

Neurological drugs delivered to the olfactory region can enter the brain via olfactory pathways and bypass the blood-brain barrier. However, clinical applications of the direct nose-to-brain delivery are rare because of the extremely low olfactory doses using conventional nasal devices. This poor bioavailability is mainly caused by two factors: the complex nasal structure that traps particles in the anterior nose and the complete lack of control over particle motions after their release at the nostrils. In this study, the feasibility of electric-guided delivery to the olfactory region was tested in an anatomically accurate nasal airway model both experimentally and numerically. The nose replicas were prepared using 3-D printing and could be dissembled to reveal the local deposition patterns within the nasal cavity. A test platform was developed that included a dry powder charging system and a particle point-release nozzle. Numerical modeling was conducted using COMSOL and compared to corresponding experiments. Compared to conventional nasal devices, electric-guidance of charged particles noticeably reduced particle losses in the anterior nose and increased depositions in the olfactory region. The thickness and relative permittivity of the wall were observed to affect the electric field strength and olfactory dosages. Consistent deposition patterns were obtained between experiments and numerical simulations in both 2-D and 3-D nose models. Two conceptual designs were proposed to generate, charge, and control aerosols. Results of this study indicate that it is feasible to use an electric field to control charged particles in the human nose. Both electric-guidance and point-release of particles are essential to achieve targeted olfactory delivery. Future studies to refine the aerosol charging and release systems are needed for further enhancement of olfactory dosages.

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

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.

Numerical optimization of targeted delivery of charged nanoparticles to the ostiomeatal complex for treatment of rhinosinusitis.

BACKGROUND: Despite the prevalence of rhinosinusitis that affects 10%-15% of the population, current inhalation therapy shows limited efficacy. Standard devices deliver <5% of the drugs to the sinuses due to the complexity of nose structure, secluded location of the sinus, poor ventilation, and lack of control of particle motions inside the nasal cavity. METHODS: An electric-guided delivery system was developed to guide charged particles to the ostiomeatal complex (OMC). Its performance was numerically assessed in an MRI-based nose-sinus model. Key design variables related to the delivery device, drug particles, and patient breathing were determined using sensitivity analysis. A two-stage optimization of design variables was conducted to obtain the best performance of the delivery system using the Nelder-Mead algorithm. RESULTS AND DISCUSSION: The OMC delivery system exhibited high sensitivity to the applied electric field and electrostatic charges carried by the particles. Through the synthesis of electric guidance and point drug release, the new delivery system eliminated particle deposition in the nasal valve and turbinate regions and significantly enhanced the OMC doses. An OMC delivery efficiency of 72.4% was obtained with the optimized design, which is one order of magnitude higher than the standard nasal devices. Moreover, optimization is imperative to achieve a sound delivery protocol because of the large number of design variables. The OMC dose increased from 45.0% in the baseline model to 72.4% in the optimized system. The optimization framework developed in this study can be easily adapted for the delivery of drugs to other sites in the nose such as the ethmoid sinus and olfactory region.