Multi-Modal AI/ML Integration for Precision Glaucoma Detection: A Comprehensive Analysis using Optical Coherence Tomography, Fundus Imaging, RNFL, and Vessel Density

Glaucoma, a prevailing cause of irreversible blindness, necessitates early and accurate detection for optimal management. This paper introduces a novel approach integrating AI and ML to elevate glaucoma detection precision. Leveraging SD-OCT, fundus photography, RNFL, and vessel density, the proposed system embodies a holistic analysis. Preliminary results underscore the efficacy of this multimodal system, positioning it as a frontrunner in revolutionizing ocular health diagnostics.The conventional methods for glaucoma diagnosis are often limited by their reliance on single-modal imaging, prompting a paradigm shift towards comprehensive multi-modal integration. By fusing the capabilities of SDOCT, fundus photography, RNFL, and vessel density, our approach addresses the shortcomings of individual modalities, providing a more nuanced understanding of ocular health. The innovative combination of these imaging techniques, coupled with advanced AI and ML algorithms, establishes a robust foundation for glaucoma detection, heralding a new era in ocular health diagnostics. The suggested multi-modal system not only demonstrates superior accuracy but also showcases the potential for early detection and intervention. The intricate interplay of algorithms, illustrated in detailed block diagrams, underscores the synergy achieved by integrating diverse modalities. This paper presents a pioneering step towards a more comprehensive and accurate approach to glaucoma detection, opening avenues for further advancements at the intersection of AI, ML, and ophthalmic imaging.