  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) was
screened for retinal diseases using a handheld portable fundus camera (Pictor
Plus, Volk Optical Inc., USA) at the Mexican Advanced Imaging Laboratory for
Ocular Research. The images were graded for DR by specialists according to the
Scottish DR grading scheme. The performance of the AI system was evaluated,
retrospectively, in assessing Referable DR (RDR) and Proliferative DR (PDR) and
compared to the performance on a publicly available desktop camera benchmark
dataset.
  Results: For RDR detection, Pegasus performed with an 89.4% (95% CI:
88.0-90.7) Area Under the Receiver Operating Characteristic (AUROC) curve for
the MAILOR cohort, compared to an AUROC of 98.5% (95% CI: 97.8-99.2) on the
benchmark dataset. This difference was statistically significant. Moreover, no
statistically significant difference was found in performance for PDR detection
with Pegasus achieving an AUROC of 94.3% (95% CI: 91.0-96.9) on the MAILOR
cohort and 92.2% (95% CI: 89.4-94.8) on the benchmark dataset.
  Conclusions: Pegasus showed good transferability for the detection of PDR
from a curated desktop fundus camera dataset to real-world clinical practice
with a handheld portable fundus camera. However, there was a substantial, and
statistically significant, decrease in the diagnostic performance for RDR when
using the handheld device.

Image and Video Processing;Computer Vision and Pattern Recognition