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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.
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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PubType: Academic Journal
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  Data: Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.
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  Data: <searchLink fieldCode="AU" term="%22Ho+C%22">Ho C</searchLink>; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.; School of Public Health, Imperial College London, London, UK.<br /><searchLink fieldCode="AU" term="%22Puthur+C%22">Puthur C</searchLink>; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.<br /><searchLink fieldCode="AU" term="%22Nabatte+B%22">Nabatte B</searchLink>; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda.<br /><searchLink fieldCode="AU" term="%22Moore+CP%22">Moore CP</searchLink>; Department of Chemistry, Vanderbilt University, Nashville, TN, USA.; Task Force for Global Health, Atlanta, GA, USA.<br /><searchLink fieldCode="AU" term="%22Abdoel+T%22">Abdoel T</searchLink>; Mondial Diagnostics, Meibergdreef 39, 1105 AZ, Amsterdam, NL.<br /><searchLink fieldCode="AU" term="%22Paulussen+R%22">Paulussen R</searchLink>; Mondial Diagnostics, Meibergdreef 39, 1105 AZ, Amsterdam, NL.<br /><searchLink fieldCode="AU" term="%22Nganjimi+P%22">Nganjimi P</searchLink>; Department of Engineering Science, University of Oxford, Oxford, UK.<br /><searchLink fieldCode="AU" term="%22Hoekstra+PT%22">Hoekstra PT</searchLink>; Leiden University Center for Infectious Diseases, Leiden University Medical Center, Leiden, NL.<br /><searchLink fieldCode="AU" term="%22Kabatereine+NB%22">Kabatereine NB</searchLink>; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda.<br /><searchLink fieldCode="AU" term="%22Kawesa+B%22">Kawesa B</searchLink>; Mayuge District Local Government, Uganda Ministry of Health, Mayuge, Uganda.<br /><searchLink fieldCode="AU" term="%22Odea+J%22">Odea J</searchLink>; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda.<br /><searchLink fieldCode="AU" term="%22Bogere+R%22">Bogere R</searchLink>; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda.<br /><searchLink fieldCode="AU" term="%22Katushabe+R%22">Katushabe R</searchLink>; Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda.<br /><searchLink fieldCode="AU" term="%22van+Dam+G%22">van Dam G</searchLink>; Leiden University Center for Infectious Diseases, Leiden University Medical Center, Leiden, NL.<br /><searchLink fieldCode="AU" term="%22Scherr+TF%22">Scherr TF</searchLink>; Department of Chemistry, Vanderbilt University, Nashville, TN, USA.<br /><searchLink fieldCode="AU" term="%22Chami+GF%22">Chami GF</searchLink>; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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  Data: <searchLink fieldCode="JN" term="%22101767986%22">MedRxiv : the preprint server for health sciences</searchLink> [medRxiv] 2025 Oct 02. <i>Date of Electronic Publication: </i>2025 Oct 02.
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  Data: <i>Country of Publication: </i>United States <i>NLM ID: </i>101767986 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>NLM ISO Abbreviation: </i>medRxiv <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.1101/2025.10.01.25337079
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              Text: 2025 Oct 02
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              Y: 2025
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