Nature Medicine: Deep learning to quickly detect AIDS

Although deep learning algorithms show increasing promise in disease diagnosis , their use has not been extensively tested in the rapid diagnostic tests performed in this field
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diagnosis

Nature Medicine published an article using deep learning to classify rapid human immunodeficiency virus (HIV) test images obtained in rural South Africa
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Using the newly developed image acquisition protocol of Samsung SM-P585 tablet computer, 60 field workers regularly collect images for HIV lateral flow testing

Nature Medicine published an article using deep learning to classify rapid human immunodeficiency virus (HIV) test images obtained in rural South Africa

Infographics illustrate the benefits of data capture in supporting on-site decision-making
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The current workflow used by field workers (blue); the mHealth system of the automatic RDT classifier proposed in the study plus data capture and transmission to a secure mHealth database (orange); and the benefits of deploying the proposed system (green) )

Standardization of image acquisition, image preprocessing and training library
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Standardization of image acquisition, image preprocessing and training library

Algorithm training and performance
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Algorithm training and performance

Performance evaluation of mHealth system and traditional visual interpretation : field pilot study
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Graph of agreement (%) between two groups of study participants when using traditional visual interpretation methods to interpret HIV RDT results .
Participants were 2 experienced nurses (N1 , N2) and 3 community health workers (C1 , C2 , C3) .

Performance evaluation of mHealth system and traditional visual interpretation : field pilot study

From 11374 images, train a deep learning algorithm to classify the test as positive or negative

The research has proved the potential of deep learning to accurately classify RDT images.

Original source

Turbé, V.
, Herbst, C.
, Mngomezulu, T.
et al.
 Deep learning of HIV field-based rapid tests.
  Nat Med  (2021).
https://doi.
org/10.
1038/s41591-021-01384-9

Turbé, V.
, Herbst, C.
, Mngomezulu, T.
  et al.
 Deep learning of HIV field-based rapid tests.
  Nat Med  (2021).
https://doi.
org/10.
1038/s41591-021-01384-9 Turbé, V.
, Herbst, C.
, Mngomezulu, T.
  et al.
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