Introduction
Inherited retinal diseases (IRDs), including Best disease, represent a significant challenge in ophthalmology and are a leading cause of vision loss among working-age adults. For patients and families navigating a diagnosis of Best disease—a genetic condition characterized by the accumulation of lipofuscin in the macula—timely and accurate identification is crucial. Recent technological advancements offer new hope. A landmark 2026 study published in the Journal of Ophthalmology explores how artificial intelligence (AI) models can analyze specialized retinal scans to accurately classify Best disease alongside other IRDs, paving the way for faster and more precise clinical care.
Harnessing AI for Retinal Imaging
Traditionally, diagnosing and categorizing IRDs relies heavily on expert clinical evaluation of complex imaging modalities, such as fundus autofluorescence (FAF) and ultra-widefield (UWF) pseudocolor images. However, limited datasets and the need for specialized expertise can sometimes create bottlenecks in diagnosis.
To overcome these hurdles, researchers evaluated several cutting-edge AI architectures. This included adapting RETFound—a powerful foundational model pretrained on hundreds of thousands of fundus photographs—alongside various convolutional neural networks (CNNs) like ResNet18 and ResNet50, and classical machine learning algorithms. The goal was to test how well these computational tools could distinguish between healthy retinas and specific IRDs, including Best disease, Stargardt disease, rod-cone dystrophy, and choroideremia.
Key Findings: How AI Identifies Best Disease
The study revealed encouraging results for the application of deep learning in ophthalmology. While the fine-tuned RETFound model achieved an overall accuracy of 0.815, specific CNN architectures—particularly the ResNet models—demonstrated exceptional capability across different disease categories.
Notably, the ResNet50 architecture achieved the best classification performance specifically for normal retinas and Best disease, yielding an F1 score of 0.616 for Best disease and 0.945 for normal scans. Meanwhile, other models excelled in identifying conditions like Stargardt disease and rod-cone dystrophy. These findings show that AI can successfully capture the subtle, unique visual signatures associated with Best disease from standard diagnostic images.
Implications for Clinical Care and Management
For individuals living with Best disease, the integration of AI into eye care settings holds profound promise. Accurate automated classification tools can serve as reliable triage mechanisms in busy clinics, helping general ophthalmologists flag potential IRDs and refer patients to retinal specialists sooner.
Furthermore, as clinical trials and targeted therapies for inherited eye conditions continue to expand, objective and consistent AI-driven image analysis will be instrumental. These tools can help monitor disease progression, evaluate therapeutic efficacy, and streamline clinical trial recruitment by accurately grouping patients based on their precise imaging phenotypes.
Conclusion: A Bright Future for IRD Diagnostics
The successful adaptation of foundation models and convolutional neural networks for retinal image analysis marks a significant step forward in the management of inherited retinal diseases. As research progresses, these AI frameworks will likely become integrated into everyday clinical practice, offering enhanced diagnostic precision for Best disease and ensuring that patients receive timely, tailored management strategies.