Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis
Bibliographic record
Abstract
Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digitised audio biomarkers for disease diagnostics and monitoring. In respiratory care, evidence supporting their clinical use remains fragmented and inconclusive. This study aimed to assess the current research landscape of digital audio biomarkers in respiratory medicine through a bibliometric analysis and systematic review (PROSPERO CRD 42022336730). MEDLINE, Embase, Cochrane Library and CINAHL were searched for references indexed up to 9 April 2024. Eligible studies evaluated the accuracy of sound analysis for diagnosing and managing obstructive (asthma and COPD) or infectious respiratory diseases, excluding COVID-19. A narrative synthesis was conducted, and the QUADAS-2 tool was used to assess study quality and risk of bias. Of 14 180 studies, 81 were included. Bibliometric analysis identified fundamental ( e.g. “diagnostic accuracy”+“machine learning”) and emerging ( e.g. “developing countries”) themes. Despite methodological heterogeneity, audio biomarkers generally achieved moderate (60–79%) to high (80–100%) accuracies. 80% of studies (eight out of ten) reported high sensitivities and specificities for asthma diagnosis, 78% (seven out of nine) reported high sensitivities and 56% (five out of nine) reported high specificities for COPD, and 64% (seven out of eleven) reported high sensitivity or specificity values for pneumonia diagnosis. Breathing and coughing were the most common biomarkers, with artificial neural networks being the most common analysis technique. Future research on audio biomarkers should focus on testing their validity in clinically diverse populations and resolving algorithmic bias. If successful, digital audio biomarkers hold promise for complementing existing clinical tools in enabling more accessible applications in telemedicine, communicable disease monitoring, and chronic condition management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.052 | 0.126 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".