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Objectives: To evaluate the performance of an Artificial Intelligence (AI)system (Pegasus, Visulytix Ltd., UK), at the detection of Diabetic Retinopathy(DR) from images captured by a handheld portable fundus camera.  Methods: A cohort of 6,404 patients (~80% with diabetes mellitus) wasscreened for retinal diseases using a handheld portable fundus camera (PictorPlus, Volk Optical Inc., USA) at the Mexican Advanced Imaging Laboratory forOcular Research. The images were graded for DR by specialists according to theScottish DR grading scheme. The performance of the AI system was evaluated,retrospectively, in assessing Referable DR (RDR) and Proliferative DR (PDR) andcompared to the performance on a publicly available desktop camera benchmarkdataset.  Results: For RDR detection, Pegasus performed with an 89.4% (95% CI:88.0-90.7) Area Under the Receiver Operating Characteristic (AUROC) curve forthe MAILOR cohort, compared to an AUROC of 98.5% (95% CI: 97.8-99.2) on thebenchmark dataset. This difference was statistically significant. Moreover, nostatistically significant difference was found in performance for PDR detectionwith Pegasus achieving an AUROC of 94.3% (95% CI: 91.0-96.9) on the MAILORcohort and 92.2% (95% CI: 89.4-94.8) on the benchmark dataset.  Conclusions: Pegasus showed good transferability for the detection of PDRfrom a curated desktop fundus camera dataset to real-world clinical practicewith a handheld portable fundus camera. However, there was a substantial, andstatistically significant, decrease in the diagnostic performance for RDR whenusing the handheld device.

Evaluation of an AI System for the Detection of Diabetic Retinopathy  from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI  study