Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models

Abstract

Purpose: Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results.

Methods: In this review, we systematically report studies with datasets of retinal images from patients with neurodegenerative diseases, including Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and others. We also review and characterize the models in the current literature which have been used for classification, regression, or segmentation problems using retinal images in patients with neurodegenerative diseases.

Results: Our review found several existing datasets and models with various imaging modalities primarily in patients with Alzheimer's disease, with most datasets on the order of tens to a few hundred images. We found limited data available for the other neurodegenerative diseases. Although cross-sectional imaging data for Alzheimer's disease is becoming more abundant, datasets with longitudinal imaging of any disease are lacking.

Conclusions: The use of bilateral and multimodal imaging together with metadata seems to improve model performance, thus multimodal bilateral image datasets with patient metadata are needed. We identified several deep learning tools that have been useful in this context including feature extraction algorithms specifically for retinal images, retinal image preprocessing techniques, transfer learning, feature fusion, and attention mapping. Importantly, we also consider the limitations common to these models in real-world clinical applications.

Translational relevance: This systematic review evaluates the deep learning models and retinal features relevant in the evaluation of retinal images of patients with neurodegenerative disease.

Figure 1.

Conceptual search design as structured within Ovid MEDLINE search tool. exp, exploded, represents that the search was expanded to include similar related MeSH terms; .mp, multipurpose, indicates that all areas of the publication (title, abstract, body, etc.) were searched.

Figure 2.

Architecture of VGGNet. Used with permission from Goutam et al. 2022. CONV, a conversion of the convolutional neural network; FC, fully connected layer; ReLU, rectified linear unit.

Figure 3.

Simple approach for combining fundoscopic image processing and metadata processing. Adapted with permission from Gessert et Al. 2020. CONV, a conversion of the convolutional neural network; FC, fully connected layer; ReLU, rectified linear unit.

Figure 4.

Example of attention maps used in the classification of diabetic retinopathy. Pixels in the image that are of higher relevance to model decision making are highlighted in yellow and red. Adapted with permission from Zhang et al. 2022.