Pulmonary diseases, such as asthma and Chronic Obstructive Pulmonary Disease (COPD), constitute a major public health challenge. The disease symptoms, including airway obstruction and inflammation, usually result in changes in airway mechanical properties, such as the caliber and impedance of the airway. To measure such airway properties for disease evaluation and diagnosis purposes, pulmonary function tests (PFT) has been widely adopted. However, most existing PFT systems require expensive and cumbersome hardware that are impossible to be used out of clinic. To allow out-clinic continuous pulmonary disease evaluation, in this paper we present AWARE, a new sensing and AI system that supports accurate and reliable PFT using commodity smartphones. AWARE uses a smartphone to transmit acoustic signals and reconstructs the profile of human airway based on the analysis of reflected acoustic waves captured from the smartphone's microphone. The subject's pulmonary condition is then evaluated by a multi-task learning model that integrates both the airway measurements and the subject's lung function records as the ground truth. Evaluations on 75 human subjects demonstrate that AWARE has the capability to achieve 80% accuracy on distinguishing between humans with healthy pulmonary function and with asthma symptoms.
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Aiding Airway Obstruction Diagnosis With Computational Fluid Dynamics and Convolutional Neural Network: A New Perspective and Numerical Case Study
Abstract It is challenging to locate small-airway obstructions induced by chronic obstructive pulmonary disease (COPD) directly from visualization using available medical imaging techniques. Accordingly, this study proposes an innovative and noninvasive diagnostic method to detect obstruction locations using computational fluid dynamics (CFD) and convolutional neural network (CNN). Specifically, expiratory airflow velocity contours were obtained from CFD simulations in a subject-specific 3D tracheobronchial tree. One case representing normal airways and 990 cases associated with different obstruction sites were investigated using CFD. The expiratory airflow velocity contours at a selected cross section in the trachea were labeled and stored as the database for training and testing two CNN models, i.e., ResNet50 and YOLOv4. Gradient-weighted class activation mapping (Grad-CAM) and the Pearson correlation coefficient were employed and calculated to classify small-airway obstruction locations and pulmonary airflow pattern shifts and highlight the highly correlated regions in the contours for locating the obstruction sites. Results indicate that the airflow velocity pattern shifts are difficult to directly visualize based on the comparisons of CFD velocity contours. CNN results show strong relevance exists between the locations of the obstruction and the expiratory airflow velocity contours. The two CNN-based models are both capable of classifying the left lung, right lung, and both lungs obstructions well using the CFD simulated airflow contour images with total accuracy higher than 95.07%. The two automatic classification algorithms are highly transformative to clinical practice for early diagnosis of obstruction locations in the lung using the expiratory airflow velocity distributions, which could be imaged using hyperpolarized magnetic resonance imaging.
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- Award ID(s):
- 2120688
- PAR ID:
- 10320673
- Date Published:
- Journal Name:
- Journal of Fluids Engineering
- Volume:
- 144
- Issue:
- 8
- ISSN:
- 0098-2202
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Clearance of intrapulmonary mucus by the high-velocity airflow generated by cough is the major rescue clearance mechanism in subjects with mucoobstructive diseases and failed cilial-dependent mucus clearance, e.g., subjects with cystic fibrosis (CF) or chronic obstructive pulmonary disease (COPD). Previous studies have investigated the mechanical forces generated at airway surfaces by cough but have not considered the effects of mucus biophysical properties on cough efficacy. Theoretically, mucus can be cleared by cough from the lung by an adhesive failure, i.e., breaking mucus-cell surface adhesive bonds and/or by cohesive failure, i.e., directly fracturing mucus. Utilizing peel-testing technologies, mucus-epithelial surface adhesive and mucus cohesive strengths were measured. Because both mucus concentration and pH have been reported to alter mucus biophysical properties in disease, the effects of mucus concentration and pH on adhesion and cohesion were compared. Both adhesive and cohesive strengths depended on mucus concentration, but neither on physiologically relevant changes in pH nor bicarbonate concentration. Mucus from bronchial epithelial cultures and patient sputum samples exhibited similar adhesive and cohesive properties. Notably, the magnitudes of both adhesive and cohesive strength exhibited similar velocity and concentration dependencies, suggesting that viscous dissipation of energy within mucus during cough determines the efficiency of cough clearance of diseased, hyperconcentrated, mucus. Calculations of airflow-induced shear forces on airway mucus related to mucus concentration predicted substantially reduced cough clearance in small versus large airways. Studies designed to improve cough clearance in subjects with mucoobstructive diseases identified reductions of mucus concentration and viscous dissipation as key therapeutic strategies.
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Abstract The significance of respiratory droplet transmission in spreading respiratory diseases such as COVID-19 has been identified by researchers. Although one cough or sneeze generates a large number of respiratory droplets, they are usually infrequent. In comparison, speaking and singing generate fewer droplets, but occur much more often, highlighting their potential as a vector for airborne transmission. However, the flow dynamics of speech and the transmission of speech droplets have not been fully investigated. To shed light on this topic, two-dimensional geometries of a vocal tract for a labiodental fricative [f] were generated based on real-time MRI of a subject during pronouncing [f]. In these models, two different curvatures were considered for the tip tongue shape and the lower lip to highlight the effects of the articulator geometries on transmission dynamics. The commercial ANSYS-Fluent CFD software was used to solve the complex expiratory speech airflow trajectories. Simultaneously, the discrete phase model of the software was used to track submicron and large size respiratory droplets exhaled during [f] utterance. The simulations were performed for high, normal, and low lung pressures to explore the influence of loud, normal, and soft utterances, respectively, on the airflow dynamics. The presented results demonstrate the variability of the airflow and droplet propagation as a function of the vocal tract geometrical characteristics and loudness.
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Background Empty nose syndrome (ENS) remains a controversial disease primarily associated with inferior turbinate tissue loss. Cotton placement into the inferior meatus often alleviates ENS symptoms within minutes, but the physiologic explanation for this phenomenon is unknown. Computational fluid dynamics (CFD) was employed to evaluate the mechanisms of altered nasal airflow conferred by cotton testing.
Methods Six ENS patients (12 sides) with pre‐existing sinus computed tomography (CT) imaging were enrolled after marked symptomatic improvement (decrease in score on the Empty Nose Syndrome 6‐Item Questionnaire [ENS6Q] of >7 points) with office‐based cotton testing. The fashioned cotton plug was labeled in situ with iohexol contrast spray, and sinus CT was immediately obtained to detect cotton contouring in the inferior meatus. CT imaging from pre‐ and post‐cotton placement was analyzed using comparative CFD techniques.
Results After cotton placement, significant symptomatic improvement and reduced ENS6Q scores (16.8 ± 4.1 to 3.1 ± 2.4;
p < 0.001) were recorded. Using CFD, cotton placement produced an expected 21% increase in upper airway resistance (p < 0.05). However, a significant shift in the nasal airflow distribution was also detected, with a transition of airflow vectors away from a middle meatus jetstream (−41%;p < 0.002).Conclusion Objective CFD assessment confirmed that the cotton test not only increases nasal resistance, but also restores airflow distribution to the inferior meatus in symptomatic ENS patients. These results highlight the potential efficacy of cotton test in ENS patients and further bolster the utility of this tool in identifying appropriate candidates for the inferior meatus augmentation procedure.
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The present investigation uses a computational approach to model the airflow paths of particulate contaminants entering inside the IT equipment using a commercially available CFD code. A Discrete Phase Particle modeling approach is chosen to calculate trajectories of the dispersed contaminants. Standard RANS approach is used to model the airflow in the airflow and the particles are superimposed on the flow field by the CFD solver using Lagrangian particle tracking. The server geometry was modeled in 2-D with a combination of rectangular and cylindrical obstructions. This was done to comprehend the effect of change in the obstruction type and aspect ratio on particle distribution. Identifying such discrete areas of contaminant proliferation based on concentration fields due to changing geometries will help with the mitigation of particulate contamination related failures in data centers.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4053651
