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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Oct 11, 2019
Date Accepted: Feb 16, 2020
Date Submitted to PubMed: Apr 29, 2020

The final, peer-reviewed published version of this preprint can be found here:

Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies

Tan CH, Kyaw BM, Smith H, Tan CS, Tudor Car L

Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies

J Med Internet Res 2020;22(5):e16658

DOI: 10.2196/16658

PMID: 32347810

PMCID: 7316182

The use of smartphones to detect diabetic retinopathy: a scoping review and meta-analysis of diagnostic test accuracy studies

  • Choon Han Tan; 
  • Bhone Myint Kyaw; 
  • Helen Smith; 
  • Colin S. Tan; 
  • Lorainne Tudor Car

ABSTRACT

Background:

Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphones ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible and easier to use than conventional methods.

Objective:

To determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients.

Methods:

We performed an electronic search on MEDLINE, Embase and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR, to an accurate or commonly-employed reference standard, e.g. indirect ophthalmoscopy, slit-lamp biomicroscopy and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data and assessed the quality of included studies using the QUADAS-2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random-effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the PRISMA-DTA guidelines.

Results:

Nine studies involving 1430 participants were included. Most were of high quality, except one study with limited applicability due to its reference standard. The pooled sensitivity and specificity for detecting: any DR was 87% (95% CI 74%–94%) and 94% (81%–98%); mild non-proliferative DR (NPDR) was 39% (10%–79%) and 95% (91%–98%); moderate NPDR was 71% (57%–81%) and 95% (88%–98%); severe NPDR was 80% (49%–94%) and 97% (88%–99%); PDR was 92% (79%–97%) and 99% (96%–99%); diabetic macular edema was 79% (63%–89%) and 93% (82%–97%); and referral-warranted DR was 91% (86%–94%) and 89% (56%–98%). The area under SROC curve ranged from 0.879–0.979. The diagnostic odds ratio ranged from 11.3–1225.

Conclusions:

We found heterogeneous evidence showing smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference standards and classification criteria, and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.


 Citation

Please cite as:

Tan CH, Kyaw BM, Smith H, Tan CS, Tudor Car L

Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies

J Med Internet Res 2020;22(5):e16658

DOI: 10.2196/16658

PMID: 32347810

PMCID: 7316182

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