Publication
Deep Learning for Ophthalmology - The State-of-the-Art and Future Trends
Ho Minh Duy Nguyen; Hasan Md Tusfiqur Alam; Tai Nguyen; Devansh Srivastav; Hans-Jürgen Profitlich; Ngan Le; Daniel Sonntag
DFKI, DFKI Research Reports (RR), Vol. 01, 2024.
Abstract
The emergence of artificial intelligence (AI), particularly Deep Learning (DL), has marked a new era in the realm of ophthalmology, offering the transformative potential for the diagnosis and treatment of posterior segment eye diseases. This review explores the cutting-edge applications of DL across a range of ocular conditions, including diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation. We provide a comprehensive overview of foundational machine learning techniques and advanced DL architectures, such as convolutional neural networks, attention mechanisms, and transformer-based models, highlighting the evolving role of AI in enhancing diagnostic accuracy, optimizing treatment strategies, and improving overall patient care. Additionally, we present key challenges in integrating AI solutions into clinical practice, including ensuring data diversity, improving algorithm transparency, and effectively leveraging multimodal data. This review emphasizes AI's potential to improve disease diagnosis and enhance patient care while stressing the importance of collaborative efforts to overcome these barriers and fully harness AI's impact in advancing eye care.