Acoustic airway clearance devices: A systematic review of experimental and numerical studies
Bibliographic record
Abstract
• Categorized 14 airway clearance devices according to their working principles. • Discussed existing hot technologies, inconsistencies, and gaps behind the devices. • Reviewed both experimental and numerical studies from 1958 to 2024 in this field. • Evaluated devices regarding the multidisciplinary nature of airway clearance. • Identified the current and future trends in airway clearance devices. The global respiratory care devices market, including acoustic airway clearance devices (ACDs), is expected to experience a compound annual growth rate of 6.10 % from 2023 to 2030. However, there are a number of inconsistencies in the categorization and working frequency range from one discipline to another one. A better understanding of the mechanisms of action of these devices is therefore of prime importance in order for physicians, physiotherapists, scientists, and engineers to remain abreast of up-to-date studies in the field and specifically on the frequency range used. In the present review, we have categorized acoustic ACDs according to their working principles while reviewing their existing shortcomings in both experimental and numerical studies, thereby paving the way for future research directions. A total of 14 different ACDs are discussed, taking into account their working principle and frequency range, and classified as follows: mechano-acoustic devices, high-frequency chest wall compression (HFCWC), and high-frequency chest wall oscillation (HFCWO) for high-frequency chest compression (HFCC) and oral high-frequency oscillation (OHFO). Existing studies highlight that ACDs with HFCWC distinguish themselves from other devices by supplying compression in a homogeneous manner, allowing the delivery of both efficient and gentle therapy up to approximately 40 Hz. Notwithstanding, a stark difference in the working frequency range across the various devices was found and identified as a literature gap. Given that this difference arises from both experimental and numerical studies between the various disciplines, the studies are further classified according to their respective objectives, methodology and outputs to help readers quickly and straightforwardly locate the articles of interest for potential future investigations. The review also brings to light the interdisciplinary nature of ACDs, whereby numerical biomedical studies can actively assist experimental studies in terms of reproducibility and reliability, creating a digital twin of the human chest and its respective components.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".