[Numerical simulation and machine learning analysis of aerosol drug delivery efficiency following Draf Ⅱ-Ⅲ surgery in patients with chronic rhinosinusitis].
Authors
Affiliations (7)
Affiliations (7)
- Department of Otolaryngology Head and Neck Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China Shaanxi Provincial Key Laboratory for Precision Diagnosis and Treatment of Otorhinolaryngology, Xi'an 710004, China Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
- The Comprehensive Breast Care Center, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China.
- Department of Otolaryngology Head and Neck Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China Shaanxi Provincial Key Laboratory for Precision Diagnosis and Treatment of Otorhinolaryngology, Xi'an 710004, China.
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China.
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne VIC 8001, Australia.
- Department of Otolaryngology and Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology and Head and Neck Surgery, Ministry of Education, Beijing 100730, China.
Abstract
<b>Objective:</b> To establish a predictive system for aerosol drug deposition by integrating computational fluid dynamics (CFD) and artificial intelligence (AI) modeling, and to propose optimized strategies for intranasal drug delivery in patients with chronic rhinosinusitis with nasal polyps (CRSwNP) after various Draf procedures. <b>Methods:</b> Post-operative 3D nasal airway models of Draf Ⅱa, Ⅱb, and Ⅲ were reconstructed based on CT data from a CRSwNP patient treated at the Second Affiliated Hospital of Xi'an Jiaotong University. CFD simulations were employed to assess the impact of inspiratory flow rates (15, 30, and 45 L/min), nebulizer oxygen flow rates (6 and 8 L/min), and particle sizes (5-50 μm) on drug deposition efficiency (DE) across three target areas (TAs): the frontal sinus (TA1), the olfactory region and ethmoid sinus (TA2), and the respiratory zone (TA3). Based on 540 sets of CFD data, XGBoost machine learning models were developed to predict regional DE and interpret variable importance. Statistical analysis was performed using SPSS 21.0. <b>Results:</b> Regarding particle size, optimal DE was observed with 25 μm drug particles for TA1 (DE<sub>max</sub>=30.19%), 15-25 μm for TA2 (27.55%), and 20-35 μm for TA3 (25.77%). Nowadays, clinically available nebulizers producing small particles (1-5 μm) generated extremely low DE in TA1 (<1%) and TA2 (<2%). Among the surgical variants, Draf Ⅲ provided the largest frontal sinus ostium with the highest airflow velocity (1.53 m/s), resulting in the highest DE of TA1 (τ=0.75, <i>P</i><0.001). However, Draf IIb achieved the highest cumulative DE across all three TAs (τ=0.40, <i>P</i>=0.001). The XGBoost models exhibited excellent predictive performance (R²: 0.81-0.98). Feature importance analysis and SHapley Additive exPlanations (SHAP) values revealed that drug particle size was the primary determinant of cumulative DE across all three TAs (accounting for >40% of importance), while surgical procedure had a dominant influence on the DE of TA1 (accounting for >40% of importance), with the Draf Ⅲ surgery significantly promoting frontal sinus drug deposition. <b>Conclusion:</b> By integrating CFD and AI techniques, this study demonstrates that the Draf III procedure significantly improves frontal sinus ventilation and inhances drug deposition. Medium-sized particles (25 μm) combined with low-to-moderate inspiratory flow rates (15-30 L/min) are optimal for nasal-targeted aerosol therapy.