Diagnostic accuracy of smartphone-based artificial intelligence systems for detecting diabetic retinopathy: A systematic review and meta-analysis

Abstract

Aims: Diabetic retinopathy (DR) is a major cause of blindness globally, early detection is critical to prevent vision loss. Traditional screening that, rely on human experts are, however, costly, and time-consuming. The purpose of this systematic review is to assess the diagnostic accuracy of smartphone-based artificial intelligence(AI) systems for DR detection.

Methods: Literature review was conducted on MEDLINE, Embase, Scopus, CINAHL Plus, and Cochrane from inception to December 2022. We included diagnostic test accuracy studies evaluating the use of smartphone-based AI algorithms for DR screening in patients with diabetes, with expert human grader as the reference standard. Random-effects model was used to pool sensitivity and specificity. Any DR(ADR) and referable DR(RDR) were analyzed separately.

Results: Out of 968 identified articles, six diagnostic test accuracy studies met our inclusion criteria, comprising 3,931 patients. Four of these studies used the Medios AI algorithm. The pooled sensitivity and specificity for diagnosis of ADR were 88 % and 91.5 % respectively and for diagnosis of RDR were 98.2 % and 81.2 % respectively. The overall risk of bias across the studies was low.

Conclusions: Smartphone-based AI algorithms show high diagnostic accuracy for detecting DR. However, more high-quality comparative studies are needed to evaluate the effectiveness in real-world clinical settings.

Keywords: Artificial intelligence; Diabetic retinopathy; Machine learning; Smartphone screening.