Purpose: To automatically detect epiretinal membranes (ERM) in various OCT scans of the central and paracentral macula region and classify them by size using deep neural networks (DNNs). Methods: 11,061 OCT-images of 624 volume OCT scans (624 eyes of 461 patients) were included and graded according to the presence of an ERM and its size (small 100-1000μm, large >1000μm). The data set was divided into training, validation and test sets (comprising of 75%, 10%, 15% of the data, respectively). An ensemble of DNNs was trained and saliency maps were generated using Guided Backprob. OCT-scans were also transformed into a one-dimensional-value using t-SNE analysis. Results: The DNNs' receiver-operating-characteristics on the test set showed a high performance for no ERM, small ERM and large ERM cases (AUC: 0.99, 0.92, 0.99, respectively; 3-way accuracy: 89% ), with small ERMs being the most difficult ones to detect. t-SNE analysis sorted cases by size and, in particular, revealed increased classification uncertainty at the transitions between groups. Saliency maps reliably highlighted ERM, regardless of the presence of other OCT features (i.e. retinal thickening, intraretinal pseudo- cysts, epiretinal proliferation) and entities such as ERM-retinoschisis, macular pseudohole and lamellar macular hole. Conclusion: DNNs can reliably detect and grade ERMs according to their size not only in the fovea but also in the paracentral region. This is also achieved in cases of hard-to-detect, small ERMs. In addition, the generated saliency maps can be used effectively to highlight small ERMs that might otherwise be missed. The proposed model could be used for screening programs or decision support systems in the future.

The authors have declared no competing interest.

We thank the German Ministry of Science and Education (BMBF) for funding through the Tuebingen AI Center (FKZ 01IS18039A) and the German Science Foundation for funding through a Heisenberg Professorship (BE5601/4-2) and the Excellence Cluster "Machine Learning - New Perspectives for Science" (EXC 2064, project number 390727645). MMU received a postdoctorate international research grant (grant number: BIDEB-2219) from "The Scientific and Technological Research Council of Turkey-TUBITAK." The funding bodies did not have any influence in the study planning and design.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Our dataset consisted of 624 OCT volume scans from 624 eyes of 461 patients presenting to the Department of Ophthalmology at the University of Tuebingen. The study was conducted according to the guidelines and standards of the Declaration of Helsinki, and was approved by the Ethics Committee of the University of Tuebingen, Germany.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

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The optical coherence tomography scans were obtained from the University Eye Clinic and their use was permitted by the Institutional Ethics Committee of the University of Tuebingen.