Artificial Intelligence in Inherited Retinal Diseases: Imaging-Based Applications and Emerging Trends

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Inherited retinal diseases (IRDs) present diagnostic and management challenges due to their complexity.
• Current retinal imaging advances improve assessment but struggle with early diagnosis and prognosis.
• Artificial intelligence (AI), particularly deep learning (DL), offers potential solutions in ophthalmology.

Purpose of the Study:

• To review recent studies on AI applications in retinal imaging for IRDs.
• To assess AI's performance in disease detection, segmentation, classification, and prognosis.
• To identify limitations and future directions for AI in IRD management.

Main Methods:

• Narrative review of peer-reviewed studies from 2019-2025.
• Focused on AI in optical coherence tomography (OCT), fundus autofluorescence (FAF), and color fundus photography.
• Examined AI for automated image analysis tasks in IRDs.

Main Results:

• AI models demonstrate high performance in automated image analysis for IRDs like retinitis pigmentosa and Stargardt disease.
• Deep learning methods show strong results in segmentation, classification, and structure-function analysis.
• Emerging applications in prognostic modeling exist but require further exploration.

Conclusions:

• AI holds significant potential for improving IRD diagnosis and management through automated image analysis.
• Current AI applications are limited by small datasets, lack of validation, and limited longitudinal data.
• Future research requires multicenter collaboration, multimodal data integration, and interpretable AI for clinical adoption.