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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. 

Recurrent respiratory papillomatosis (RRP) is a chronic condition primarily affecting children, known as juvenile onset RRP (JORRP), caused by a viral infection. Antiviral medications have been used to reduce the need for frequent surgeries, slow the growth of papillomata, and prevent disease spread. Effective treatment of JORRP necessitates targeted drug delivery (TDD) to ensure that inhaled aerosolized drugs reach specific sites, such as the larynx and glottis, without harming healthy tissues. Using computational fluid particle dynamics (CFPD) and machine learning (ML), this study (1) investigated how drug properties and individual factors influence TDD efficiency for JORRP treatment and (2) developed personalized inhalation therapy using an ML-empowered smart inhaler control algorithm for precise medication release. This algorithm optimizes the inhaler nozzle position and diameter based on drug and patient-specific data, enhancing drug delivery to the larynx and glottis. CFPD simulations show that particle size significantly affects deposition fractions in the upper airway, emphasizing the importance of particle size selection. Additionally, optimal nozzle diameter and delivery efficiency depend on particle size, inhalation flow rate, and release time. The ML-based TDD strategy, employing a classification and regression tree model, outperforms conventional inhalation therapy by achieving a higher delivery efficiency to the larynx and glottis. This innovative concept of an ML-empowered smart inhaler represents a promising step toward personalized and precise pulmonary healthcare through inhalation therapy. It demonstrates the potential of AI-driven smart inhalers for improving the treatment outcomes of lung diseases that require TDD at designated lung sites. 

Tracheobronchial tumors, while uncommon, are often malignant in adults. Surgical removal is the primary therapy for non-metastatic lung malignancies, but it is only possible in a small percentage of non-small-cell lung cancer patients and is limited by the number and location of tumors, as well as the patient’s overall health. This study proposes an alternative treatment: administering aerosolized chemotherapeutic particles via the pulmonary route using endotracheal catheters to target lung tumors. To improve delivery efficiency to the lesion, it is essential to understand local drug deposition and particle transport dynamics. This study uses an experimentally validated computational fluid particle dynamics (CFPD) model to simulate the transport and deposition of inhaled chemotherapeutic particles in a 3-dimensional tracheobronchial tree with 10 generations (G). Based on the particle release maps, targeted drug delivery strategies are proposed to enhance particle deposition at two lung tumor sites in G10. Results indicate that controlled drug release can improve particle delivery efficiencies at both targeted regions. The use of endotracheal catheters significantly affects particle delivery efficiencies in targeted tumors. The parametric analysis shows that using smaller catheters can deliver more than 74% of particles to targeted tumor sites, depending on the location of the tumor and the catheter diameter used, compared to less than 1% using conventional particle administration methods. Furthermore, the results indicate that particle release time has a significant impact on particle deposition under the same inhalation profile. This study serves as a first step in understanding the impact of catheter diameter on localized endotracheal injection for targeting tumors in small lung airways. 

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.