Background: Retinal fundus images, obtained from routine eye screenings, contain important information, not only concerning microvascular lesions of the retina, but potentially also of the progression and manifestation of various other diabetes complications. Recent advances in the field of machine learning, so-called deep learning neural networks (DNN), make it possible to automatically detect retinal lesions from fundus photos. Our overall aim is to develop a DNN to detect retinal vascular lesions. In this abstract, we describe a pre-processing step consisting of application of a DNN.

Methods: For each eye, 5 retinal images of parts of the eye are taken, and collected into a joint mosaic image, used for image grading. There is no automated labelling of the images to identify left or right eyes. To distinguish left- from right eye images, we used a set of 1,916 unique, high-definition retina mosaic fundus images from diabetes patients followed at Steno Diabetes Center Copenhagen (SDCC) between 2003-2017. We trained and validated a DNN, using 964 left-eye (training set: 713, validation: 251) and 952 right-eye images (training set: 706, validation: 246). A DNN is a method that finds the optimal algorithm for relating a large set of input (mosaic fundus images) to output (left or right eye). The DNN used was based on the InceptionV3 architecture and run for 100 epochs with 100 gradient descent steps pr. epoch.

Results: The DNN classification obtained a validation AUC-ROC of 0.92 and a validation accuracy of 0.86.

Conclusions: The DNN successfully distinguished left-eye from right-eye images. We plan to use the DNN to identify right- and left-eye images in the SDCC image database comprising ∼ 80,000 left-eye and right-eye images. We will subsequently develop a DNN for automated detection of referable diabetic retinopathy of retinal fundus images, for use in the clinic.


L. Díaz: None. D. Vistisen: Stock/Shareholder; Self; Novo Nordisk A/S. M.E. Jørgensen: Research Support; Self; Amgen, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S. M. Valerius: None. J.N. Hajari: None. H. Lund-Andersen: None. S. Byberg: None.

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