ArticleJournal of medical engineering & technology2023
Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation.
Article in Journal of medical engineering & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- TriFusion-PCGNet: A Hybrid Multi-Branch Deep Learning Network for Energy-Aware Normal/Abnormal Heart Sound Classification.Life (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Pediatric Cardiology: Present Applications and Future Directions.Pediatric reports · 2026Review
- Advancing Point-of-Care Still's Murmur Identification: Evaluating the Efficacy of ConvNets and Transformers Using the StethAid Multicenter Heart Sound Database.IEEE transactions on bio-medical engineering · 2026Article
- Streamlining heart failure patient care with machine learning of thoracic cavity sound data.World journal of cardiology · 2025Review
- Digital auscultation in clear and present threat of novel respiratory infectious disease: a narrative review.Journal of Yeungnam medical science · 2025Article
- A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.IEEE access : practical innovations, open solutions · 2024Article
- StethAid: A Digital Auscultation Platform for Pediatrics.Sensors (Basel, Switzerland) · 2023Article
- Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.Frontiers in artificial intelligence · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.