Retinal imaging techniques and analysis

Retinal imaging serves as a cornerstone in the diagnosis, management, and monitoring of various retinal diseases, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders. This specialized conference track aims to convene ophthalmologists, researchers, clinicians, and industry experts to explore the latest advancements, challenges, and innovations in the field of retinal imaging.

Key Themes and Topics:

1. Fundus Photography: Explore the principles, techniques, and applications of fundus photography in capturing high-resolution images of the retina, optic nerve, and retinal vasculature. Discuss advancements in digital imaging systems, color fundus photography, and wide-field imaging for comprehensive retinal evaluation.
2. Optical Coherence Tomography (OCT): Review the principles and clinical applications of OCT in generating cross-sectional and three-dimensional images of the retina, macula, and optic nerve head. Discuss advancements in OCT technology, including spectral-domain OCT (SD-OCT), swept-source OCT (SS-OCT), and OCT angiography (OCTA), for quantitative assessment of retinal anatomy and microvasculature.
3. Fluorescein Angiography (FA): Discuss the role of FA in assessing retinal vascular perfusion, leakage, and neovascularization in various retinal diseases. Explore the interpretation of FA findings and their correlation with clinical diagnosis and management decisions.
4. Indocyanine Green Angiography (ICGA): Examine the utility of ICGA in visualizing choroidal circulation, polypoidal choroidal vasculopathy (PCV), and other choroidal abnormalities. Discuss the indications, technique, and interpretation of ICGA findings in clinical practice.
5. Multimodal Imaging: Evaluate the complementary role of multimodal imaging techniques, including OCT, FA, and autofluorescence imaging, in the comprehensive evaluation and management of retinal diseases. Discuss integrated imaging platforms and software tools for data fusion and image analysis.
6. Artificial Intelligence (AI) and Image Analysis: Explore the application of AI-based algorithms and machine learning techniques in automated retinal image analysis, disease detection, and risk stratification. Discuss the potential for AI to enhance diagnostic accuracy, efficiency, and reproducibility in retinal imaging interpretation.