Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis
Nature Communications, vol. 17
Abstract
There is a lack of automated pipelines for diagnostic classification of point-of-care tests for neglected tropical diseases. Here we present an end-to-end automated pipeline for the analysis of point-of-care circulating cathodic antigen tests for schistosomiasis. We incorporated deep learning for cassette segmentation with signal processing. Automated classifications were compared to quantitative readings from calibrated antigen samples examined in lateral flow readers, and visual readings from highly trained field and senior technicians. The pipeline was evaluated for 3188 individuals within the SchistoTrack cohort in rural Uganda. Our quantitative classifications were on par with a lateral flow reader, and showed 86.6% sensitivity and 96.5% specificity with visual readings from a senior technician, which was an improvement on the visual readings from field technicians. Automated classifications were possible in as little as five minutes after test preparation for high antigen concentrations. We showed visual trace uncertainty can be resolved with signal processing, indicating visual traces should be classified as negative. Our pipeline will aid in advancing diagnostics to meet the World Health Organization target product profile for schistosomiasis, provide quantitative assessments for other diagnostics, enable large-scale surveillance in areas targeting elimination, and provide real-time quality control for diagnostics introduced into primary healthcare facilities.
Authors 14
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Affiliation as printed
Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
University of Oxford
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Affiliation as printed
Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda
Republic of Uganda Ministry of Health
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Vanderbilt University · The Task Force for Global Health
Affiliation as printed
Department of Chemistry, Vanderbilt University, Nashville, TN, USA
Task Force for Global Health, Atlanta, GA, USA
The Task Force for Global Health
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Affiliation as printed
Mondial Diagnostics, Amsterdam, The Netherlands
MondialDx
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Affiliation as printed
Mondial Diagnostics, Amsterdam, The Netherlands
MondialDx
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Affiliation as printed
Department of Engineering Science, University of Oxford, Oxford, UK
University of Oxford
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Pytsje T. Hoekstra Aachen
Leiden University · Leiden University Medical Center
Affiliation as printed
Leiden University Center for Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands
Leiden University Medical Center
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Affiliation as printed
Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda
Uganda Ministry of Health
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Affiliation as printed
Mayuge District Local Government, Uganda Ministry of Health, Mayuge, Uganda
Uganda Ministry of Health
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Affiliation as printed
Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda
Uganda Ministry of Health
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Affiliation as printed
Division of Vector Borne Diseases and Neglected Tropical Diseases, Uganda Ministry of Health, Kampala, Uganda
Uganda Ministry of Health
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Govert J. van Dam Aachen
Leiden University · Leiden University Medical Center
Affiliation as printed
Leiden University Center for Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands
Leiden University Medical Center
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Affiliation as printed
Department of Chemistry, Vanderbilt University, Nashville, TN, USA
Vanderbilt University
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Goylette F. Chami corresponding
Affiliation as printed
Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK. goylette.chami@ndph.ox.ac.uk
University of Oxford
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