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Abstract Tracheal stenosis, a severe airway narrowing, poses significant challenges in respiratory function and often necessitates surgical intervention to restore proper airflow. This study aims to demonstrate how computational fluid dynamics (CFD) can provide a non-invasive, efficient, and highly individualized approach to assist surgeons in modeling and planning various surgical strategies for treatment. The CFD-based approach in this study provides significant advantages, including reduced time and cost, and the ability to analyze complex pulmonary airflow characteristics that are difficult to investigate using in vitro and in vivo studies. This research compares three tracheal geometries: a diseased airway with tracheal stenosis and two post-surgical configurations from different surgical plans. Simulations were conducted under four inhalation flow rates, i.e., rest (6 L/min), normal (30 L/min), moderate (60 L/min), and intensive exercise (120 L/min), to evaluate the impact of surgical outcomes on pulmonary airflow dynamics. The upper airway, modeled with a mouth inlet diameter of 20 mm, exhibited average velocities of 0.32, 1.59, 3.18, and 6.37 m/s, corresponding to the respective flow rates. The laminar model was used for the rest flow rate, while the shear stress transport (SST) k-ω model was applied to simulate turbulence with higher inhalation flow rates. The results revealed substantial improvements in flow parameters following surgery. The stenotic geometry exhibited extreme resistance, with pressure drops increasing from 1.96 Pa at rest to 318.9 Pa under intensive flow, and high wall shear stress (WSS) values peaking at 330.8 Pa. Surgical Plan 1 reduced pressure drops by up to 47% and WSS by 97%, while Surgical Plan 2 achieved even greater reductions, with pressure drops lowered by 45% and WSS reduced to 2.54 Pa under high flow rates. Localized flow disturbances, such as uneven airflow distribution among lung lobes, were also alleviated post-surgery. In the diseased airway, the right lower lobe received up to 40% of the total flow, causing severe imbalances. Surgical Plan 2 achieved the most uniform distribution, with all lobes receiving 13%-29% of airflow across all flow rates, ensuring effective oxygenation and minimizing risks of overdistension or under-perfusion. These findings suggest that the CFD-based approach employed in this study can effectively model surgical outcomes, providing surgeons with a fast, detailed, and non-invasive tool for tailoring procedures to individual patient needs.
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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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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4053651
