LITERATURE REVIEW: ADJOINT-BASED COMPUTATIONAL FLUID DYNAMICS FOR INDIVIDUALIZED SEPTOPLASTY PLANNING IN NASAL SEPTUM DEVIATION
Ключевые слова:
Key words: Nasal septum deviation, Septoplasty ,Computational fluid dynamics (CFD) , Adjoint-based CFD , Nasal airflow simulation ,Surgical planning ,Nasal obstructionАннотация
Abstract: Nasal septum deviation (NSD) is common anatomical pathology leading to nasal airway obstruction and reduced quality of life. Despite the accepted corrective surgery being septoplasty, postoperative outcomes remain unpredictable. Traditional surgical planning often depends on anatomical assessment and surgeon experience rather than necessarily accurately predicting airflow improvement. Recent progress in computational fluid dynamics (CFD), specifically adjoint-based optimization techniques, has brought about a paradigm shift toward customized septoplasty planning. In this review article, critically appraising the state-of-the-art in the literature regarding adjoint-based CFD techniques being utilized in the management of NSD, their ability to customize surgical interventions based on individual patient-specific airflow dynamics is emphasized. The existing clinical literature, computational methods, benefits, drawbacks, and future directions of incorporating these technologies into clinical routine are explored[1].
Библиографические ссылки
1. Garcia, G., et al. (2010). Nasal septal deviation: Anatomical and airflow studies. Otolaryngology-Head and Neck Surgery, 143(5), 709–714.
2. Macellari, F., et al. (2024). Adjoint-based computational fluid dynamics for personalized septoplasty planning. Journal of Biomechanical Engineering, 146(2), 021002.
3. Moghaddam, S., et al. (2020). Computational fluid dynamics in nasal surgery: State of the art. Frontiers in Surgery, 7, 46.
4. Rhee, J. S., et al. (2011). Correlation between nasal airflow and patient symptoms after septoplasty. Laryngoscope, 121(12), 2505–2510.
5. Schillaci, A., et al. (2023). Imaging and computational approaches in nasal septal deviation. European Archives of Oto-Rhino-Laryngology, 280(6), 2467–2476.
6. Segalerba, M., et al. (2023). Sensitivity analysis of nasal airflow using adjoint methods. International Journal of Computational Fluid Dynamics, 37(4), 292–303.
7. Van Strien, T., et al. (2021). Outcomes of septoplasty: A systematic review. Clinical Otolaryngology, 46(3), 436–445.
8. Zhao, K., Jiang, J., & Lee, S. H. (2022). Computational modeling of nasal airflow: A review of recent advances and future directions. Biomechanics and Modeling in Mechanobiology, 21(4), 783–798.
9. Wexler, D., Cohen, N., & Yezersky, M. (2019). Patient-specific nasal airflow simulation in septal deviation: Clinical applications. Annals of Biomedical Engineering, 47(11), 2321–2331.
10. Kimbell, J. S., & Rhee, J. S. (2017). Quantitative assessment of nasal airflow improvement after septoplasty: Using computational modeling and clinical data. JAMA Facial Plastic Surgery, 19(5), 413–420.
11. Zhang, X., & Santago, P. (2018). CFD-based surgical planning of nasal valve repair: Methodology and case studies. Medical & Biological Engineering & Computing, 56(3), 401–412.
12. Lee, H., et al. (2020). Integrating machine learning with CFD for improved prediction of nasal airflow post-septoplasty. Artificial Intelligence in Medicine, 104, 101817.
13. Zhao, K., & Jiang, J. (2019). Mesh generation techniques for CFD nasal airway modeling: Impact on simulation accuracy. Computers in Biology and Medicine, 108, 92–100.
