“PEDAGOGS”
international research journal ISSN:
2181-3027
_SJIF:
5.449
https://scientific-jl.com/ped
Volume-82, Issue-1, May -2025
211
LITERATURE REVIEW: ADJOINT-BASED COMPUTATIONAL
FLUID DYNAMICS FOR INDIVIDUALIZED SEPTOPLASTY
PLANNING IN NASAL SEPTUM DEVIATION
Das Sharodiya
1
, Norjigitov Firdavs Nordirjonovich
2
Student , International Students’ Faculty Of Medicine ,
Tashkent Medical Academy
1
Assistant Teacher of the Department of Otolaryngology,
Tashkent Medical Academy
2
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].
Key words
: Nasal septum deviation, Septoplasty ,Computational fluid dynamics
(CFD) , Adjoint-based CFD , Nasal airflow simulation ,Surgical planning ,Nasal
obstruction
1. Introduction
Nasal septum deviation (NSD) happens in approximately 20–30% of the general
population and is the most frequent source of nasal obstruction [1]. It results from
displacement of the nasal septum, composed of cartilage and bone partition separating
the two sides of the nasal cavity, leading to compromised airflow and symptoms
including congestion, breathing, and snoring difficulty [4]. Repair through surgical
procedure by septoplasty remains the most frequent therapeutic intervention to restore
nasal patency.
Even though a standard procedure, septoplasty outcomes are not invariably
consistent, and 15–20% of patients still have symptoms postoperatively [7] . Part of
this variability is caused by the use of a subjective anatomical evaluation and lack of
“PEDAGOGS”
international research journal ISSN:
2181-3027
_SJIF:
5.449
https://scientific-jl.com/ped
Volume-82, Issue-1, May -2025
212
objective predictive means of measuring preoperative and postoperative airflow
dynamics [2,3].
Current developments in computational modeling, in the form of computational
fluid dynamics (CFD), offer a promising solution through nasal airflow simulation to
identify areas of blockages and predict surgical outcomes [3]. Of CFD approaches,
adjoint-based optimization is one that is a highly advanced methodology capable of
accurately identifying geometric sensitivities in airflow to allow precise and
personalized surgical planning[4].
2. Methodological Explanation: Computational Fluid Dynamics (CFD)
Modeling Workflow for Nasal Septum Deviation
Table 1. workflow for Nasal Septum Deviation [8,9]
Step
Description
Medical Imaging
Acquisition
High-resolution CT or MRI scans capture the patient's
nasal anatomy in detail.
3D Geometry
Reconstruction
The nasal cavity and septum are segmented from
imaging data using software to create a 3D model.
Mesh Generation
The 3D geometry is discretized into a computational
mesh refined in regions of interest.
Boundary Condition
Setup
Physiological boundary conditions are applied, such as
inlet flow rates at nostrils and outlet pressure.
CFD Simulation
Navier-Stokes equations governing airflow are solved
using CFD software to compute velocity and pressure.
Adjoint-Based
Sensitivity Analysis
The adjoint method computes gradients of airflow
parameters relative to geometry changes to identify
critical areas.
Surgical Planning &
Virtual Surgery
Simulated modifications based on sensitivity maps are
tested virtually to predict airflow improvements.
Postoperative
Validation
Post-surgery imaging and airflow measurements validate
CFD prediction accuracy and surgical outcomes.
3. Traditional Septoplasty and Its Limitations
Conventional septoplasty is guided predominantly by visual inspection and
physical examination (anterior rhinoscopy, endoscopy), alongside imaging modalities
such as CT scans to assess septal anatomy [5]. Surgeons make intraoperative decisions
based on their experience, modifying the septum to improve airflow.
However, anatomical assessments do not always correlate well with functional
nasal airflow [1,10]. This discordance partly explains suboptimal outcomes in some
“PEDAGOGS”
international research journal ISSN:
2181-3027
_SJIF:
5.449
https://scientific-jl.com/ped
Volume-82, Issue-1, May -2025
213
patients. For example, over-resection may lead to saddle nose deformity, while
insufficient correction may fail to alleviate obstruction.
While acoustic rhinometry and rhinomanometry provide objective airflow data,
they lack spatial resolution to guide targeted surgical modification. Thus, there remains
an unmet clinical need for tools that predict airflow improvements with surgical
intervention on an individual basis [7,8].
4. Computational Fluid Dynamics (CFD) in Nasal Airway Evaluation
CFD replicates the passage of fluids (air) based on 3D models of nasal airways
acquired using CT or MRI scans. By solving the Navier-Stokes equations, CFD
provides high-resolution maps of velocity, pressure map, and resistance in the nasal
cavity [6].
Several studies have established CFD's capability to measure nasal airflow
obstruction quantitatively and visualize the impact of anatomical variabilities [1,4].
CFD facilitates virtual surgical planning—allowing surgeons to "test" changes on a
computer model before actual surgery.
However, traditional CFD procedures are computationally intensive and
frequently must be run multiple times to explore different surgical approaches, making
them inappropriate for clinical use[13].
5. Adjoint-Based Optimization in CFD: A Novel Approach
Adjoint-based CFD is a more recent and mathematically sophisticated approach
that efficiently computes the sensitivity of airflow parameters to small geometric
changes in the nasal anatomy [2].Unlike brute-force parametric studies, adjoint
methods calculate gradients of objective functions (e.g., nasal resistance) relative to the
shape in a single simulation [11].
This capability allows rapid identification of anatomical regions where surgical
modification will yield the greatest functional benefit [5]. Thus, it facilitates patient-
specific, targeted septoplasty planning with less computational cost.
6. Clinical and Research Applications of Adjoint-Based CFD
The earliest clinical translations of adjoint-based CFD are emerging. Recent
studies demonstrated how this approach could guide minimal septal corrections while
maximizing airflow improvements [2]. Their patient-specific simulations identified
optimal resection zones, confirmed by postoperative airflow enhancement.
Other research groups have validated the technique on virtual cohorts, showing
improved predictive accuracy compared to traditional CFD and standard clinical
assessment [6].These promising results indicate that adjoint-based CFD could reduce
unnecessary tissue removal and improve postoperative outcomes [12,13].
“PEDAGOGS”
international research journal ISSN:
2181-3027
_SJIF:
5.449
https://scientific-jl.com/ped
Volume-82, Issue-1, May -2025
214
7. Advantages of Adjoint-Based CFD in Septoplasty Planning
Personalization: The adjoint method provides patient-specific data, allowing
surgeons to tailor interventions to each patient’s unique anatomy [2].
Computational Efficiency: By calculating sensitivity gradients in a single
simulation, adjoint-based CFD drastically reduces the computational cost compared to
traditional trial-and-error CFD studies [5].
Precision: This method pinpoints specific anatomical areas where minor
geometric changes can have significant functional impacts, potentially minimizing
surgical invasiveness[ 4].
Predictive Capability: Surgeons can simulate and compare multiple surgical
scenarios preoperatively, improving outcome prediction and surgical planning [3].
Table 2: Comparison of Traditional CFD and Adjoint-Based CFD in Septoplasty
Planning
Criteria
Traditional CFD
Adjoint-Based CFD
Computational
Cost
High — requires multiple
iterative simulations
Lower — sensitivity gradients
calculated in one run
Surgical
Planning
Trial-and-error with multiple
geometries
Gradient-driven identification
of critical regions
Precision
Limited by number of
simulations
High precision through
mathematical optimization
Clinical
Usability
Time-consuming and limited
More feasible with automation
Outcome
Prediction
Qualitative/semi-quantitative
Quantitative with detailed
sensitivity analysis
Integration
Potential
Low without specialized
expertise
Higher potential with
interdisciplinary collaboration
8. Limitations:
Technical Sophistication: Developing accurate CFD models requires high-
resolution imaging, fluid mechanics skills, and advanced computer software—
environments not always available in clinics [7].
Validation Required: Early indications are promising, but extensive clinical
trials will be required to establish predictive validity and assess long-term patient
outcomes [3].
Barriers to Integration: Integration into clinical workflows seamlessly requires
intuitive interfaces and seamless interdisciplinary interaction between surgeons,
radiologists, and engineers that can be [2].
“PEDAGOGS”
international research journal ISSN:
2181-3027
_SJIF:
5.449
https://scientific-jl.com/ped
Volume-82, Issue-1, May -2025
215
9. Future Directions and Research Opportunities
Clinical Translation and Workflow Integration:
Future research should focus on creating streamlined, automated pipelines for
converting medical images into CFD models and adjoint-based analyses accessible to
clinicians without fluid dynamics expertise [6]. Integration with surgical planning
software and intraoperative navigation tools could further improve precision.
Artificial Intelligence and Machine Learning:
Combining adjoint-based CFD with machine learning could enable rapid
prediction models trained on large datasets, further reducing computational time and
supporting real-time clinical decision-making [5].
Multiscale Modeling:
Linking nasal airflow simulations with mucosal physiology, sensory feedback,
and patient-reported symptomatology can provide holistic outcome predictions beyond
purely aerodynamic measures[1].
Expanded Indications:
Beyond septoplasty, adjoint-based CFD could guide surgical corrections for other
complex nasal pathologies, such as inferior turbinate hypertrophy and nasal valve
collapse, enhancing the scope of personalized nasal surgery [7].
9. Conclusion
Adjoint-based computational fluid dynamics represents a cutting-edge, advanced
approach for personalizing septoplasty in patients with nasal septum deviation. By
providing detailed, patient-specific airflow sensitivity maps, this method can
potentially improve surgical precision, reduce unnecessary tissue removal, and
enhance functional outcomes. Although technical challenges and validation needs
remain, ongoing multidisciplinary research and technological innovations pave the
way for routine clinical integration of this promising technology.
References:
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.
“PEDAGOGS”
international research journal ISSN:
2181-3027
_SJIF:
5.449
https://scientific-jl.com/ped
Volume-82, Issue-1, May -2025
216
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.
