Artificial intelligence-based digital pathology for the detection and quantification of soil-transmitted helminths eggs
This study demonstrates the potential of the AI-DP-based method for STH diagnosis. While similar to KK1.0, the AI-DP-based method outperforms it in certain aspects. These findings underscore the potential of advancing the AI-DP KK2.0 prototype for dependable STH diagnosis and furthering the development of automated digital microscopes in accordance with WHO guidelines for STH diagnosis.
Authors
- Peter Ward (ORCID: https://orcid.org/0000-0001-6826-9290)
- Bruno Levecke (ORCID: https://orcid.org/0000-0001-8912-5595)
- Lindsay A. Broadfield (ORCID: https://orcid.org/0000-0002-7878-5712)
- Nancy Cure-Bolt (ORCID: https://orcid.org/0000-0002-0727-4712)
- Lieven Stuyver (ORCID: https://orcid.org/0000-0001-7818-9390)
- Fernando Perez (ORCID: https://orcid.org/0000-0002-9753-0416)
- Peter Hu (ORCID: https://orcid.org/0000-0003-2842-3948)
- John Oleynick
- María Beltrán
Institutions
- Janssen (Belgium) (BE)
- Ghent University (BE)
- Independent Dance (GB)
- Springhouse (US)
- Instituto Nacional de Enfermedades Neoplásicas (PE)
- Forum Solidaridad Perú (PE)
- Janssen (United States) (US)
Publication Details
- Journal
- PLoS neglected tropical diseases
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1371/journal.pntd.0012492
- Citations
- 16
- Primary Topic
- Parasites and Host Interactions
- Type
- article
- Field-Weighted Citation Impact
- 8.25
Funders
- Janssen Research and Development