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Purpose  To validate the performance of a commercially available,  CE ‐certified deep learning ( DL ) system, Ret CAD  v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy ( DR ) and age‐related macular degeneration ( AMD ) in colour 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 (133 821 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%).    Conclusion  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.

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration