Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.
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| Title: | Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis. |
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| Authors: | Ho C; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.; School of Public Health, Imperial College London, London, UK., Puthur C; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK., Nabatte B; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda., Moore CP; Department of Chemistry, Vanderbilt University, Nashville, TN, USA.; Task Force for Global Health, Atlanta, GA, USA., Abdoel T; Mondial Diagnostics, Meibergdreef 39, 1105 AZ, Amsterdam, NL., Paulussen R; Mondial Diagnostics, Meibergdreef 39, 1105 AZ, Amsterdam, NL., Nganjimi P; Department of Engineering Science, University of Oxford, Oxford, UK., Hoekstra PT; Leiden University Center for Infectious Diseases, Leiden University Medical Center, Leiden, NL., Kabatereine NB; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda., Kawesa B; Mayuge District Local Government, Uganda Ministry of Health, Mayuge, Uganda., Odea J; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda., Bogere R; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda., Katushabe R; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda., van Dam G; Leiden University Center for Infectious Diseases, Leiden University Medical Center, Leiden, NL., Scherr TF; Department of Chemistry, Vanderbilt University, Nashville, TN, USA., Chami GF; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK. |
| Source: | MedRxiv : the preprint server for health sciences [medRxiv] 2025 Oct 02. Date of Electronic Publication: 2025 Oct 02. |
| Publication Type: | Journal Article; Preprint |
| Journal Info: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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