Smartphone-based DNA diagnostics for malaria detection using deep learning for local decision support and blockchain technology for security | Nature Electronics
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Smartphone-based DNA diagnostics for malaria detection using deep learning for local decision support and blockchain technology for security

Nature Electronics volume 4, pages 615–624 (2021)Cite this article

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Abstract

In infectious disease diagnosis, results need to be communicated rapidly to healthcare professionals once testing has been completed so that care pathways can be implemented. This represents a particular challenge when testing in remote, low-resource rural communities, in which such diseases often create the largest burden. Here, we report a smartphone-based end-to-end platform for multiplexed DNA diagnosis of malaria. The approach uses a low-cost paper-based microfluidic diagnostic test, which is combined with deep learning algorithms for local decision support and blockchain technology for secure data connectivity and management. We validated the approach via field tests in rural Uganda, where it correctly identified more than 98% of tested cases. Our platform also provides secure geotagged diagnostic information, which creates the possibility of integrating infectious disease data within surveillance frameworks.

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Fig. 1: System architecture.
Fig. 2: System design.
Fig. 3: Mobile heater characterization.
Fig. 4: System architecture of the blockchain network.
Fig. 5: AI performance.

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Data availability

The data that support the findings of this study for Figs. 3 and 5 are available in the University of Glasgow’s Enlighten repository with the identifier https://doi.org/10.5525/gla.researchdata.1106.

Code availability

The code for the Android app, the CNN and blockchain architecture is available through an open Zenodo repository (https://doi.org/10.5281/zenodo.4429293), which includes a GitHub repository for the code (https://github.com/XGuoo/BlockchainDiagnostics), and is licensed under an Open Source GNU GPLv3 Licence. The repository README.md markdown file describes the dataset and includes installation and demo instructions.

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Acknowledgements

We thank P. Lamberton for help with ethical clearance and facilitation, Epigem Ltd for help with device manufacturing, E. O’Shaughnessy for device assembly, O. Tufayl for initial app GUI development, as well as A. Atuhire for sampling and treatment in the field in Uganda. The study was supported by the UK Global Challenges Research Fund, the Scottish Funding Council and the Engineering and Physical Sciences Research Council (EPSRC) Institutional Support Fund (grant no. EP/R512813/1), as well as by EPSRC EP/R01437X/1, also supported by the National Institute for Health Research and EP/T029765/1. A.G. acknowledges support from EPSRC studentship EP/N509668/1.

Author information

Author notes

  1. These authors contributed equally: Xin Guo, Muhammad Arslan Khalid.

Authors and Affiliations

  1. Division of Biomedical Engineering, The James Watt School of Engineering, University of Glasgow, Glasgow, UK

    Xin Guo, Muhammad Arslan Khalid, Alice Garrett, Shantimoy Kar, Xiaoxiang Yan, Julien Reboud & Jonathan M. Cooper

  2. School of Computing Science, University of Glasgow, Glasgow, UK

    Ivo Domingos & Anna Lito Michala

  3. Vector Control Division, Ministry of Health, Kampala, Uganda

    Moses Adriko, Candia Rowel, Diana Ajambo & Edridah M. Tukahebwa

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Contributions

X.G. and M.A.K. developed and implemented the decision support system and, with I.D., implemented it on mobile platforms. M.A.K., A.G., S.K. and X.Y. performed experiments in the laboratory in Glasgow. A.G., M.A.K., M.A., J.R., E.M.T. and J.M.C. designed the field study. M.A.K., A.G., C.R., D.A., J.R. and J.M.C. carried out the field study. X.G., A.L.M., J.R. and J.M.C. analysed the data and wrote the manuscript. All authors edited the manuscript.

Corresponding author

Correspondence to Jonathan M. Cooper.

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The authors declare no competing interests.

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Peer review information Nature Electronics thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary methods, Figs. 1–7 and Tables 1–4.

Reporting Summary

Supplementary Video 1

App usage demonstration.

Supplementary Data 1

Tabulated field test results. Notation: TP, true positive (positive for PCR and for origami test); TN, true negative (negative for PCR and origami test); FN, false negative (negative for origami test but positive for PCR); FP, false positive (positive for origami test but negative for PCR). Real-time PCR Ct values are obtained in triplicate. It is important to note that the prevalence of the disease is extremely high in the area studied. In this situation, where the existing field tests have lower sensitivity, and where we cannot return for treatment, after confirmatory, more sensitive, gold-standard PCR analysis (performed retrospectively in Glasgow), the ethical protocol advises to treat on a ‘presumed positive’ basis.

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Guo, X., Khalid, M.A., Domingos, I. et al. Smartphone-based DNA diagnostics for malaria detection using deep learning for local decision support and blockchain technology for security. Nat Electron 4, 615–624 (2021). https://doi.org/10.1038/s41928-021-00612-x

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