  Purpose: To validate the performance of a commercially-available,
CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The
Netherlands), for the joint automatic detection of diabetic retinopathy (DR)
and age-related macular degeneration (AMD) in color fundus (CF) images on a
dataset with mixed presence of eye diseases.
  Methods: Evaluation of joint detection of referable DR and AMD was performed
on a DR-AMD dataset with 600 images acquired during routine clinical practice,
containing referable and non-referable cases of both diseases. Each image was
graded for DR and AMD by an experienced ophthalmologist to establish the
reference standard (RS), and by four independent observers for comparison with
human performance. Validation was furtherly assessed on Messidor (1200 images)
for individual identification of referable DR, and the Age-Related Eye Disease
Study (AREDS) dataset (133821 images) for referable AMD, against the
corresponding RS.
  Results: Regarding joint validation on the DR-AMD dataset, the system
achieved an area under the ROC curve (AUC) of 95.1% for detection of referable
DR (SE=90.1%, SP=90.6%). For referable AMD, the AUC was 94.9% (SE=91.8%,
SP=87.5%). Average human performance for DR was SE=61.5% and SP=97.8%; for AMD,
SE=76.5% and SP=96.1%. Regarding detection of referable DR in Messidor, AUC was
97.5% (SE=92.0%, SP=92.1%); for referable AMD in AREDS, AUC was 92.7%
(SE=85.8%, SP=86.0%).
  Conclusions: The validated system performs comparably to human experts at
simultaneous detection of DR and AMD. This shows that DL systems can facilitate
access to joint screening of eye diseases and become a quick and reliable
support for ophthalmological experts.
