The use of Homecare-OCT devices can help patients to take images of their own eyes for clinical evaluation from the freedom of their homes. These images are then analyzed to provide accurate diagnostics. In comparison, the manual analysis can delay the results for a considerable time. This project aims to implement a digital platform with neural networks, which analyze patient´s images and predict an accurate diagnostic. After the evaluation, the platform will notify the patient and the patient´s doctor of the neural network’s output, so that in case of a worsening of the patient´s condition, an appointment with the doctor can be made.

At the same time, neural network´s methods are used to gain insights into which features in the images (biomarkers) have a particularly high diagnostic significance. Therefore, the collection of image data can be optimized specifically for this purpose.

Team & Contact

• David Caraveo (david.caraveo@isp.uni-luebeck.de)
• Philipp Bende (bende@isp.uni-luebeck.de)
• Ludwig Pechmann (lpechmann@unitransferklinik.de)
• Article on the Ki-Sigs website