DOG Forum digital

The significance digitization holds for the health care system and thus also for ophthalmology is constantly rising. This development offers many opportunities, but it also poses completely new challenges and questions for research, teaching and patient care in clinics and practices.

The DOG will focus on this important topic and with its Forum Digital will be setting a special program focus within the congress. This program offers a platform aimed at ophthalmologists and scientists, manufacturers of diagnostic equipment, software providers, companies in the pharmaceutical industry and start-ups and intends to promote exchange and discourse as well as networking.

The presentations deal with the opportunities offered by digitisation without neglecting the challenges of change. Symposia will highlight the scientific issue of digitisation, while workshops and presentations will focus on the practical, legal and technical aspects that arise in this context and will arise in the future. In addition to scientific discourse and the commu­nication of information, there will also be room for exchange.

Thursday, 26. 9., to Saturday, 28. 9. 2019

Scientific coordination of the DOG Forum digital:
Nicole Eter (Münster)
Karsten Kortüm (München)

Thursday, 26. 9. 2019

Friday, 27. 9. 2019

Saturday, 28. 9. 2019

Forum Digital 10:15 - 11:30 28.09.2019
Symposien Sa12
Academic Deep Learning Projects from the Technical Perspective
Academic Deep learning projects in ophthalmology have shown to be promising approaches to improve patient care. Joint technological research teams - informatics and ophthalmologists - continue to work on a variety of topics. In this symposium, computer scientists will present their latest projects in regard to ophthalmology.
Karsten Kortüm (München/Ludwigburg)
Hrvoje Bogunovic (Wien)

Deep learning has a great potential as a precision medicine instrument for providing individualized prognosis of disease progression. In this presentation, we focus on predicting individual conversion to a late stage disease in eyes with early/intermediate age-related macular degeneration (AMD) using longitudinal optical coherence tomography (OCT) imaging. We discuss supervised and unsupervised learning approaches that allow learning representations that go beyond conventional imaging biomarkers.

Holger Langner (Mittweida)

The development of prediction models for VEGF-therapy outcome and future visual acuity in the context of Age-related Macula Degeneration (AMD) is a challenging task, especially when it is applied to real-life data that contains rich variants and cross diagnoses of degenerative eye diseases for patients who have not undergone surgery under thoroughly planned study conditions. The more classical methods for time series prediction cannot be applied here in a straightforward manner. In our talk we propose an approach of combining OCT-image analysis based on Deep-Learning methods with Survival / Hazard modeling of textual features derived from clinical notes, and present some of our current results.

Philipp Prahs (Regensburg)

The use of machine learning and neural networks in ophthalmic imaging has increased in recent years. A common approach is to employ a seperate neural network classifier for every clinical entity that is to be investigated. We present common solutions when similar clinical questions are analyzed and clinical images for neural network training are scarce.

Olivier Morelle (Bonn)

Automated image analysis has become an indispensable tool in ophthalmology due to the increasing amounts of data. Algorithms are already being used in studies and will soon be incorporated into our clinical routine. This talk will highlight the technical aspects of a deep learning algorithm for segmenting the ellipsoidal zone in OCT.

Christoph Kernstock (Tübingen)

In Germany, only 10-12% of pedestrian traffic lights are equipped with assistive signals for visually impaired or blind people. Developing a smartphone-app to assist these people poses some additional challenges compared to diagnostic applications for deep learning in ophthalmology.