Investigation of Airflow in Human Nasal Cavity and Virtual Septoplasty Using CFD
Bibliographic record
Abstract
Breathing is essential for oxygen supply, and nasal structure significantly impacts airflow.Conditions like septal deviation which affect 20-35% of people often require septoplasty, but long-term outcomes remain uncertain.Virtual surgery, combining CFD and medical imaging, offers a predictive tool to evaluate surgical efficacy beforehand.This study presents a CFD simulation of airflow in human nasal cavity and proposes a numerical methodology for virtual septoplasty.The method was validated using the Carleton University Standardized Human Nasal Cavity Geometry available in the open-source literature.Virtual septoplasty was simulated by modifying computational grid of the nasal cavity, and pre-/postoperative flow results were compared at various breath rates.Key parameters involving nasal resistance and nasal power were quantified.Results demonstrated measurable changes in airflow dynamics, enabling objective assessment of surgical outcomes.This approach helps clinicians predict postoperative improvements, avoid unnecessary surgeries, and enhance patient counselling.The study highlights the potential of the interdisciplinary collaboration of engineers and medical doctors in surgical planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".