Visualization Analysis of Research Status and Hotspots of Computerized Respiratory Sound Analysis Based on CiteSpace
Bibliographic record
Abstract
Objective: To explore the research landscape and hotspots of Computerized Respiratory Sound Analysis (CORSA) and provide a reference for future in-depth studies. Methods: Literature related to CORSA published up to August 27, 2020, was retrieved from the Web of Science Core Collection. CiteSpace 5.6.R3 was used to perform co-authorship analysis, institutional collaboration analysis, keyword co-occurrence analysis, and co-citation analysis. Results: A total of 1,897 publications were included. Co-authorship analysis identified several influential contributors, including Zahra Moussavi, Kenneth Sundaraj, and H. Pasterkamp. Major research institutions included the University of Manitoba, the University of Queensland, and Aristotle University of Thessaloniki. Keyword co-occurrence analysis indicated that “respiratory sound,” “lung sound,” “asthma,” “children,” and “classification” were major research themes. The most frequently co-cited articles were published by Arati Gurung (2011), Mohammed Bahoura (2009), and H. Pasterkamp (1997). Highly cited journals included Chest, the American Journal of Respiratory and Critical Care Medicine, and IEEE Transactions on Biomedical Engineering. Conclusion: CORSA research is primarily driven by European and North American scholars and institutions, with China still in an early stage of development. Current hotspots include respiratory sound acquisition and processing, feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs), and classification techniques based on machine learning and deep learning. CORSA is suitable for diverse populations and is widely applied in respiratory diseases, especially bronchial asthma. Its non-invasive nature offers particular advantages for infants and pregnant women. Although CORSA demonstrates strong clinical potential, its clinical translation remains limited. Advancing clinical applications and bridging the gap between research and practice will be key directions for future development. The prominence of top-tier respiratory and engineering journals among citations suggests that CORSA is an emerging and influential research frontier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.080 | 0.073 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".