  Diabetes is a chronic disease characterized by excess sugar in the blood and
affects 422 million people worldwide, including 3.3 million in France. One of
the frequent complications of diabetes is diabetic retinopathy (DR): it is the
leading cause of blindness in the working population of developed countries. As
a result, ophthalmology is on the verge of a revolution in screening,
diagnosing, and managing of pathologies. This upheaval is led by the arrival of
technologies based on artificial intelligence. The "Evaluation intelligente de
la r\'etinopathie diab\'etique" (EviRed) project uses artificial intelligence
to answer a medical need: replacing the current classification of diabetic
retinopathy which is mainly based on outdated fundus photography and providing
an insufficient prediction precision. EviRed exploits modern fundus imaging
devices and artificial intelligence to properly integrate the vast amount of
data they provide with other available medical data of the patient. The goal is
to improve diagnosis and prediction and help ophthalmologists to make better
decisions during diabetic retinopathy follow-up. In this study, we investigate
the fusion of different modalities acquired simultaneously with a PLEXElite
9000 (Carl Zeiss Meditec Inc. Dublin, California, USA), namely 3-D structural
optical coherence tomography (OCT), 3-D OCT angiography (OCTA) and 2-D Line
Scanning Ophthalmoscope (LSO), for the automatic detection of proliferative DR.
