Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial

  • Charles R. Cleland*
  • , Covadonga Bascaran
  • , William Makupa
  • , Bernadetha Shilio
  • , Frank A. Sandi
  • , Heiko Philippin
  • , Ana Patricia Marques
  • , Catherine Egan
  • , Adnan Tufail
  • , Pearse A. Keane
  • , Alastair K. Denniston
  • , David Macleod
  • , Matthew J. Burton
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction Globally, diabetic retinopathy (DR) is a major cause of blindness. Sub-Saharan Africa is projected to see the largest proportionate increase in the number of people living with diabetes over the next two decades. Screening for DR is recommended to prevent sight loss; however, in many low and middle-income countries, because of a lack of specialist eye care staff, current screening services for DR are not optimal. The use of artificial intelligence (AI) for DR screening, which automates the grading of retinal photographs and provides a point-of-screening result, offers an innovative potential solution to improve DR screening in Tanzania. 

Methods and analysis We will test the hypothesis that AI-supported DR screening increases the proportion of persons with true referable DR who attend the central ophthalmology clinic following referral after screening in a single-masked, parallel group, individually randomised controlled trial. Participants (2364) will be randomised (1:1 ratio) to either AI-supported or the standard of care DR screening pathway. Participants allocated to the AI-supported screening pathway will receive their result followed by point-of-screening counselling immediately after retinal image capture. Participants in the standard of care arm will receive their result and counselling by phone once the retinal images have been graded in the usual way (typically after 2-4 weeks). The primary outcome is the proportion of persons with true referable DR attending the central ophthalmology clinic within 8 weeks of screening. Secondary outcomes, by trial arm, include the proportion of persons attending the central ophthalmology clinic out of all those referred, sensitivity and specificity, number of false positive referrals, acceptability and fidelity of AI-supported screening. 

Ethics and dissemination The London School of Hygiene & Tropical Medicine, Kilimanjaro Christian Medical Centre and Tanzanian National Institute of Medical Research ethics committees have approved the trial. The results will be submitted to peer-reviewed journals for publication. 

Trial registration number ISRCTN18317152.

Original languageEnglish
Article numbere075055
Number of pages10
JournalBMJ open
Volume14
Issue number1
DOIs
Publication statusPublished - 25 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024 BMJ Publishing Group. All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • clinical trial
  • diabetic retinopathy
  • ophthalmology
  • public health

ASJC Scopus subject areas

  • General Medicine

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