Artificial intelligence-driven mobile interpretation of a semi-quantitative cryptococcal antigen lateral flow assay
This technology's adaptability for various LFAs suggests broader applications. AI-driven interpretations have transformative potential, revolutionizing cryptococcosis diagnosis, offering standardized, reliable, and efficient POCT results.
Authors
- Narda Medina (ORCID: https://orcid.org/0000-0001-6176-0052)
- Daniel Capellán-Martín (ORCID: https://orcid.org/0000-0002-9743-0845)
- Ana Alastruey‐Izquierdo (ORCID: https://orcid.org/0000-0001-8651-4405)
- Miguel Luengo-Oroz (ORCID: https://orcid.org/0000-0002-8694-2001)
- Oscar Bonilla (ORCID: https://orcid.org/0000-0003-4227-3632)
- David Bermejo-Peláez (ORCID: https://orcid.org/0000-0002-0181-3957)
- Juan L. Rodrı́guez-Tudela (ORCID: https://orcid.org/0000-0003-0458-7139)
Institutions
- Synlab Czech (Czechia) (CZ)
- Instituto de Salud Carlos III (ES)
- Hospital General San Juan de Dios (GT)
- Action for ME (GB)
- Unidad de Cirugía Cardiovascular de Guatemala (GT)
Publication Details
- Journal
- IMA Fungus
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1186/s43008-024-00158-5
- Citations
- 17
- Primary Topic
- Fungal Infections and Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.57
Funders
- Instituto de Salud Carlos III
- Agencia Estatal de Investigación