  Background/Aim. This study aims to predict Amyloid Positron Emission
Tomography (AmyloidPET) status with multimodal retinal imaging and
convolutional neural networks (CNNs) and to improve the performance through
pretraining with synthetic data. Methods. Fundus autofluorescence, optical
coherence tomography (OCT), and OCT angiography images from 328 eyes of 59
AmyloidPET positive subjects and 108 AmyloidPET negative subjects were used for
classification. Denoising Diffusion Probabilistic Models (DDPMs) were trained
to generate synthetic images and unimodal CNNs were pretrained on synthetic
data and finetuned on real data or trained solely on real data. Multimodal
classifiers were developed to combine predictions of the four unimodal CNNs
with patient metadata. Class activation maps of the unimodal classifiers
provided insight into the network's attention to inputs. Results. DDPMs
generated diverse, realistic images without memorization. Pretraining unimodal
CNNs with synthetic data improved AUPR at most from 0.350 to 0.579. Integration
of metadata in multimodal CNNs improved AUPR from 0.486 to 0.634, which was the
best overall best classifier. Class activation maps highlighted relevant
retinal regions which correlated with AD. Conclusion. Our method for generating
and leveraging synthetic data has the potential to improve AmyloidPET
prediction from multimodal retinal imaging. A DDPM can generate realistic and
unique multimodal synthetic retinal images. Our best performing unimodal and
multimodal classifiers were not pretrained on synthetic data, however
pretraining with synthetic data slightly improved classification performance
for two out of the four modalities.
