Deep learning of HIV field-based rapid tests.

Journal: Nature medicine
PMID: 34140702

Abstract

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans-experienced nurses and newly trained community health worker staff-and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections.

Authors

  • Valérian Turbé
    London Centre for Nanotechnology, University College London, London, UK. v.turbe@ucl.ac.uk.
  • Carina Herbst
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
  • Thobeka Mngomezulu
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
  • Sepehr Meshkinfamfard
    London Centre for Nanotechnology, University College London, London, UK.
  • Nondumiso Dlamini
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
  • Thembani Mhlongo
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
  • Theresa Smit
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
  • Valeriia Cherepanova
    Department of Computer Science, University College London, London, UK.
  • Koki Shimada
    Department of Computer Science, University College London, London, UK.
  • Jobie Budd
    London Centre for Nanotechnology, University College London, London, UK.
  • Nestor Arsenov
    London Centre for Nanotechnology, University College London, London, UK.
  • Steven Gray
    UCL Centre for Advanced Spatial Analysis, London, UK.
  • Deenan Pillay
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa.
  • Kobus Herbst
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa. Kobus.Herbst@ahri.org.
  • Maryam Shahmanesh
    Africa Health Research Institute, Nelson R. Mandela Medical School, Durban, South Africa. m.shahmanesh@ucl.ac.uk.
  • Rachel A McKendry
    London Centre for Nanotechnology, University College London, London, UK. r.a.mckendry@ucl.ac.uk.

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