Artificial intelligence in retinal vein occlusion: Current applications, challenges, and future directions

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Retinal vein occlusion (RVO) is a major cause of vision loss globally.
• Early detection and personalized management of RVO are crucial.
• Current diagnostic methods require improvement for objective risk stratification.

Purpose of the Study:

• To review current artificial intelligence (AI) applications in retinal vein occlusion (RVO).
• To focus on AI for disease detection, classification, ischemia assessment, and treatment support.
• To evaluate the clinical relevance and translational maturity of AI in RVO.

Main Methods:

• Systematic review and synthesis of AI applications in RVO research.
• Analysis of studies using color fundus photography, OCT, OCT angiography, and fluorescein angiography.
• Evaluation of AI model performance, methodological trends, and clinical relevance.

Main Results:

• AI, particularly deep learning, demonstrates high performance in detecting and classifying RVO from retinal images in experimental settings.
• Multimodal imaging and lesion-centric approaches enhance AI model accuracy.
• Most current AI evidence is from retrospective studies with limited validation.

Conclusions:

• AI holds significant potential for improving RVO diagnosis and management.
• Barriers to clinical adoption include data limitations, lack of prospective validation, and standardization issues.
• Future AI development should focus on multicenter validation, interpretability, and tools that aid clinical decision-making.