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Year : 2021  |  Volume : 33  |  Issue : 3  |  Page : 219-226

The utility of smartphone-based artificial intelligence approaches for diabetic retinopathy: A literature review and meta-analysis

1 Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
2 Department of Ophthalmology, Singleton Hospital, Swansea, Wales, UK
3 Department of Public Health Sciences, University of North Carolina, Charlotte, USA
4 Department of Ophthalmology, James Paget University Hospitals NHS Foundation Trust, Great Yarmouth, UK
5 Department of Ophthalmology, East Surrey Hospital, Redhil, Surrey, UK

Correspondence Address:
Aadil Sheikh
264 Hillbury Road, Warlingham, Surrey, CR6 9TP
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2452-2325.329064

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Purpose: To assess the diagnostic accuracy measures such as sensitivity and specificity of smartphone-based artificial intelligence (AI) approaches in the detection of diabetic retinopathy (DR). Methods: A literature search of the EMBASE and MEDLINE databases (up to March 2020) was conducted. Only studies using both smartphone-based cameras and AI software for image analysis were included. The main outcome measures were pooled sensitivity and specificity, diagnostic odds ratios and relative risk of smartphone-based AI approaches in detecting DR (of all types), and referable DR (RDR) (moderate nonproliferative retinopathy or worse and/or the presence of diabetic macular edema). Results: Smartphone-based AI has a pooled sensitivity of 89.5% (95% confidence interval [CI]: 82.3%–94.0%) and pooled specificity of 92.4% (95% CI: 86.4%–95.9%) in detecting DR. For referable disease, sensitivity is 97.9% (95% CI: 92.6%-99.4%), and the pooled specificity is 85.9% (95% CI: 76.5%–91.9%). The technology is better at correctly identifying referable retinopathy. Conclusions: The smartphone-based AI programs demonstrate high diagnostic accuracy for the detection of DR and RDR and are potentially viable substitutes for conventional diabetic screening approaches. Further, high-quality randomized controlled trials are required to establish the effectiveness of this approach in different populations.

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