Digital Technology and Artificial Intelligence in Ophthalmology

Dear Colleagues,

1. Aim:

The cost of blindness to society and individuals is huge, and many cases can be avoided by early intervention. Early and reliable diagnosis strategies and effective treatments are therefore a world priority.

Automatic and semi-automatic ophthalmic image analysis has been widely used in the study of ophthalmic disease assisted diagnosis and treatment, and increasingly percolating into clinical practice.  Significant challenges remain in terms of reliability and validation, number and type of conditions considered, multimodal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies, to mention a few.

This Special Issue aims to bring together scientists, clinicians, and students from multiple disciplines in the growing ophthalmic image analysis community, such as electronic engineering, computer science, mathematics, and medicine, to discuss the latest advancements in the field.

2. Scope：

• Computer-aided detection and diagnosis of disease
• Image analysis of novel ophthalmic imaging modalities
• Multimodal ophthalmic image analysis
• Cross-modal image generation
• Ophthalmic image atlases
• Ophthalmic image analysis in animals
• Registration of ophthalmic images, including multimodal
• Segmentation of structures (e.g., vasculature, lesions, landmarks)
• Combined analysis of images of the eye and other organs
• Crowd sourcing
•  ……

Prof. Dr. Yanwu Xu
Guest Editor

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