Digital Stethoscope Use in Neonates: A Systematic Review
Bibliographic record
Abstract
Aim: To assess the evidence for the use of digital stethoscopes in neonates and evaluate whether they are effective, appropriate, and advantageous for neonatal auscultation.Methods: A systematic review and narrative synthesis of studies published between January 1, 1990 and May 29, 2023 was conducted following searches of MEDLINE, Embase, PubMed, Scopus, and Google Scholar databases, as well as trial registries.Results: Of 3,852 records identified, a total of 41 papers were eligible and included in the narrative synthesis.Thirteen records were non-full-text articles, either in the form of journal letters or conference abstracts, and these were included separately for completion purposes but may be unreliable.Twenty eight papers were full-text articles and were included in a full qualitative analysis.Digital stethoscopes have been studied in neonatology across various clinical areas, including artificial intelligence for sound quality assessment and chest sound separation (n = 5), cardiovascular sounds (n = 11), respiratory sounds (n = 4), bowel sounds (n = 4), swallowing sounds (n = 2), and telemedicine (n = 2).This paper discusses the potential utility of digital stethoscope technology for the interpretation of neonatal sounds for both humans and artificial intelligence.The limitations of current devices are also assessed.Conclusions: The utilization of digital stethoscopes in neonatology is an emerging field with a wide range of potential applications, which has the capacity to advance neonatal auscultation.Artificial intelligence and digital stethoscope technology offer novel objective avenues for automatic pathological sound detection.Further, digital stethoscopes may improve our scientific understanding of normal neonatal physiology and can be employed in telemedicine to facilitate remote medical access.Digital stethoscopes can also provide phonocardiograms, enabling enhanced interpretation of neonatal cardiac sounds.However, current digital stethoscopes necessitate refinement as they consistently produce low-quality sounds when used on neonates.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".