Deep Learning and Multimodal Integration in Glaucoma Detection: A State-of-the-Art Review

This extensive review investigates recent advances in deep learning techniques for glaucoma detection focusing on multimodal imaging data fusion. Common approaches limited to one modality, such as image-based analysis, optical coherence tomography (OCT), or perimetry testing, frequently fail to capture sufficient information about the multifaceted nature of glaucoma reflected by both structural and functional changes in the eye anatomy. Such methodologies may neglect critical diagnostic insights, resulting in diminished precision and the possibility of erroneous diagnoses due to confounding variables such as significant myopia, pronounced optic nerve cupping, or age-associated transformations. On the contrary, a number of multi-modal approaches combining fundus images, OCT and perimetry shows the synergy in integrating complementary information across modalities. The architectures based on advanced deep learning models, such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models (e.g., CNN-GRU, CNN-Transformer) are employed for achieving improved diagnostic accuracy, better generalization, and more robust reliability over diverse patient cohorts. State of the art models have achieved accuracies as high as 99% on data as few as 158 or as many as 22,171 samples. However, challenges remain, including a lack of unbiased data, computational complexity, and the need for explainable models. Techniques such as Grad-CAM and attention mechanisms are increasingly adopted to alleviate the ’black box’ characteristics of DL models. This review emphasizes the paramount importance of multimodal integration, large-scale datasets, and interpretability in the development of clinically relevant and trustworthy designs for the identification of glaucoma.