Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations
CNN models, particularly VGG16, show promise in automating spirometry quality control, potentially reducing the need for manual inspection by specialized technicians. This approach can streamline spirometry assessments, offering consistent, high-quality diagnostics even in non-specialized or low-resource environments.
Authors
- Henrique Barros (ORCID: https://orcid.org/0000-0003-4699-6571)
- Carla Martins (ORCID: https://orcid.org/0000-0002-4643-2285)
- Andre M. Moreira (ORCID: https://orcid.org/0000-0002-7294-9296)
Institutions
- Administração Regional de Saúde de Lisboa e Vale do Tejo (PT)
- Instituto de Saúde (BR)
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto (PT)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109341
- Citations
- 5
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.74
Funders
- Fundação para a Ciência e a Tecnologia