Phenotyping and genotyping inherited retinal diseases: A comprehensive review of molecular genetics, clinical and imaging features, and therapeutic options

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Artificial intelligence tool Eye2Gene accelerates the diagnosis of inherited retinal diseases by analyzing multimodal imaging data to predict causative genetic mutations, according to a 2025 study published in Nature Machine Intelligence.

The Clinical Challenge of Inherited Retinal Diseases

Inherited retinal diseases encompass a heterogeneous group of conditions—including Stargardt disease, retinitis pigmentosa, and Leber congenital amaurosis—that lead to progressive vision loss. According to findings published in Progress in Retinal and Eye Research by Georgiou et al., identifying the specific genetic variant driving a patient’s condition requires correlating clinical phenotypes with complex molecular data. Furthermore, interpreting panel results frequently yields variants of uncertain significance, complicating clinical management.

How the Eye2Gene AI Framework Operates

The Eye2Gene model uses deep learning algorithms to analyze routine ophthalmic imaging, specifically spectral-domain optical coherence tomography and fundus autofluorescence, to predict underlying genetic mutations. Pontikos et al. reported in Nature Machine Intelligence that the system evaluates multimodal imaging features to rank likely causative genes with high accuracy. By narrowing down candidate genes before or alongside comprehensive genetic testing, the AI tool aims to improve diagnostic efficiency.

Researchers evaluated the algorithm using retrospective cohorts to determine its efficacy against expert genetic diagnoses. Studies referenced in protocols published in BMJ Open by Nguyen et al. and subsequent clinical validations demonstrate that integrating AI support into ophthalmic genetics multidisciplinary teams can streamline patient stratification. As advanced therapeutics, including gene editing for conditions such as CEP290-associated retinal degeneration documented in the New England Journal of Medicine by Pierce et al., become more prevalent, rapid and accurate genetic identification remains essential for matching patients to appropriate clinical trials and targeted interventions.

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