Effectiveness of a clinical decision support algorithm (ePOCT+) in improving quality of care for sick children in primary health facilities in Tanzania (DYNAMIC project): results from a cluster randomized trial
Discussion: Dragonfly's LAMP-based colorimetric approach offers a sample-to-result diagnostic solution for infectious diseases.Its portability and rapid testing (under 40 minutes) without the need for laboratory infrastructure or cold chain requirements make it a valuable tool for on-site diagnostics.The Dragonfly skin test panel, targeting OPXV, mpox, HSV-1, HSV-2, and VZV, supports the differential diagnosis of skin lesions and aids in screening and surveillance of suspected mpox cases.While LAMP-based solutions for mpox detection have emerged, none provide a fast, lowresource sample-to-result solution like Dragonfly. Conclusion:The recent mpox outbreak underscores the critical need for accessible, rapid, point-of-care diagnostics for screening and surveillance.Dragonfly's sample-to-result solution for skin lesions addresses this need, offering accessible diagnostics in the field and aiding in outbreak control effort s.
Authors
- Chacha Mangu (ORCID: https://orcid.org/0009-0006-7631-6812)
- Marie‐Annick Le Pogam (ORCID: https://orcid.org/0000-0003-1672-3644)
- Peter Agrea
- Honorati Masanja (ORCID: https://orcid.org/0000-0002-3860-7348)
- Alexandra V. Kulinkina (ORCID: https://orcid.org/0000-0002-4777-1254)
- Lameck Luwanda (ORCID: https://orcid.org/0000-0001-7293-3297)
- Godfrey Kavishe
- Rainer Tan (ORCID: https://orcid.org/0000-0002-9273-9632)
- Valérie D’Acremont (ORCID: https://orcid.org/0000-0002-4881-7787)
- Geofrey Ashery
- Nyanda Elias Ntinginya (ORCID: https://orcid.org/0000-0002-1575-4521)
- Dr Caroline Enos
- Sabine Renggli (ORCID: https://orcid.org/0000-0001-8233-6154)
- Humphrey Mhagama
- Ibrahim Evans Mtebene
- Margaret Joram
Publication Details
- Journal
- International Journal of Infectious Diseases
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1016/j.ijid.2024.107409
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00