Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis
To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.
Authors
- Isabelle Ruchonnet-Métrailler (ORCID: https://orcid.org/0000-0003-4771-875X)
- Laurence Lacroix (ORCID: https://orcid.org/0000-0002-6636-3344)
- Mary‐Anne Hartley (ORCID: https://orcid.org/0000-0002-8826-3870)
- Johan Nicolas Siebert (ORCID: https://orcid.org/0000-0002-2871-3382)
Institutions
- University of Geneva (CH)
- University Hospital of Geneva (CH)
- Yale New Haven Health System (US)
- École Polytechnique Fédérale de Lausanne (CH)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-08-23
- DOI
- https://doi.org/10.2196/53662
- Citations
- 9
- Primary Topic
- Phonocardiography and Auscultation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.77