June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Artificial Intelligence (AI)-based Low-cost System for Automated Screening of Malarial Retinopathy
Author Affiliations & Notes
  • Vinayak S Joshi
    VisionQuest Biomedical Inc, Albuquerque, New Mexico, United States
  • Aswathy Kurup
    VisionQuest Biomedical Inc, Albuquerque, New Mexico, United States
  • Sheila C Nemeth
    VisionQuest Biomedical Inc, Albuquerque, New Mexico, United States
  • Gilberto Zamora
    VisionQuest Biomedical Inc, Albuquerque, New Mexico, United States
  • Peter Soliz
    VisionQuest Biomedical Inc, Albuquerque, New Mexico, United States
  • Susan Lewallen
    Kilimanjaro center for community ophthalmology, South Africa
  • Simon P Harding
    University of Liverpool, United Kingdom
  • Terrie Taylor
    Michigan State University, Michigan, United States
  • Footnotes
    Commercial Relationships   Vinayak Joshi, VisionQuest Biomedical Inc. (E); Aswathy Kurup, VisionQuest Biomedical Inc. (E); Sheila Nemeth, VisionQuest Biomedical Inc. (E); Gilberto Zamora, VisionQuest Biomedical Inc. (E); Peter Soliz, VisionQuest Biomedical Inc. (I); Susan Lewallen, Kilimanjaro center for community ophthalmology (E); Simon Harding, University of Liverpool (E); Terrie Taylor, Michigan State University (E)
  • Footnotes
    Support  NIH NIAID grant 6R44AI112164-06
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 469. doi:
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      Vinayak S Joshi, Aswathy Kurup, Sheila C Nemeth, Gilberto Zamora, Peter Soliz, Susan Lewallen, Simon P Harding, Terrie Taylor; Artificial Intelligence (AI)-based Low-cost System for Automated Screening of Malarial Retinopathy. Invest. Ophthalmol. Vis. Sci. 2020;61(7):469.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To develop a fully automated and low-cost system for malarial retinopathy (MR) screening to improve the diagnostic accuracy of cerebral malaria (CM).

Methods : Cerebral malaria (CM) remains the major killer of children in Africa; however, up to 25% of these deaths are due to misdiagnosis of CM. MR is a highly specific retinal manifestation of CM that can improve the diagnostic accuracy of CM. We developed an artificial intelligence system, ASPIRE, that integrates an MR detection software with a low-cost retinal camera, iNview. The software model was trained using Deep Learning techniques and tested on retinal fundus image data of N=125 patients clinically diagnosed with CM in a malaria clinic in Malawi, Africa. The ground truth for the presence or absence of MR in retinal images was provided by a certified retinal reader. The users recorded their feedback on ASPIRE’s usability in a malaria clinic.

Results : The dataset of 125 patients included N=84 patients with MR and N=41 patients with no MR. The MR detection model provided a specificity of 100% and sensitivity of 90% with an AUC of 0.98. The users noted ASPIRE as a user-friendly, ergonomic, and bedside diagnostic tool, suitable for the clinical flow in Africa.

Conclusions : ASPIRE will improve the diagnostic accuracy for a fatal neurological complication of cerebral malaria (CM); which will save the lives of hundreds of thousands of children in Sub-Saharan Africa. In the next phase, ASPIRE functionality will be refined to make it easily accessible and affordable to the targeted population in Africa and validated in a large clinical study.

This is a 2020 ARVO Annual Meeting abstract.

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