Principal Investigator: Mr Richard Haarburger
Department: University of Goettingen
Dr Pascal Geldsetzer – Stanford University – USATags: 57136, artifical intelligence, diabetes, fundus, Imaging, Machine Learning, retina
This project aims at determining if and/or how well Diabetes can be predicted based on Retinal Fundus images using image-based machine learning. For this purpose we train a convolutional neural network on deducting if an individual has Diabetes given this individuals’ Retinal Fundus images. Once the model is trained and shows to be reliable, it can be used to provide information on individuals’ Diabetes status based solely on Retinal Fundus imagery.
Conventional methods used for diagnosing Diabetes in middle and low income countries, i.e. random blood glucose as well as questionnaire-based methods perform poorly. If we can show that the Retinal Fundus image-based prediction of Diabetes works well, the next step would be to collect smartphone Retinal Fundus images and check whether our results can be replicated. Predicting Diabetes using Retinal Fundus imagery bears great potential with regard to facilitating and enabling Diabetes screening in middle and low income countries, where prevalence keeps rising.