Central to the clinical workflow is imaging, which encompasses a spectrum from macroscopic to cellular-level visualization. Traditional fundus photography remains the cornerstone imaging modality widely utilized for screening conditions such as diabetic retinopathy, glaucoma, and cataracts. The adoption of AI in analyzing these color fundus photographs has enhanced the sensitivity and specificity of lesion detection dramatically. Complementary imaging techniques include red-free fundus photography, which better resolves superficial vascular structures, enabling earlier identification of glaucomatous changes. Furthermore, ultra-widefield imaging, empowered by AI algorithms, extends visualization to peripheral retinal regions previously difficult to capture, thereby facilitating comprehensive assessments of peripheral pathologies.

Angiographic modalities further enrich the diagnostic landscape, offering dynamic insight into retinal and choroidal vasculature. Fluorescein fundus angiography, when paired with AI-enabled segmentation tools, can automate the identification and quantitative analysis of critical features such as non-perfusion areas and microaneurysms. Similarly, indocyanine green angiography benefits from AI’s pattern recognition capabilities to improve detection accuracy of conditions like polypoidal choroidal vasculopathy—a notoriously challenging diagnosis. These angiographic advances allow clinicians to map microvascular changes with unprecedented precision.

Optical imaging technologies, particularly optical coherence tomography (OCT) and its angiographic counterpart (OCTA), introduce high-resolution cross-sectional and vascular flow visualization without the need for contrast agents. AI-powered OCT analysis algorithms facilitate automated measurement of intraretinal fluid accumulation, an important biomarker for diseases such as age-related macular degeneration and diabetic macular edema. OCTA offers a revolutionary, non-invasive capability to visualize capillary-level blood flow, and deeper still, adaptive optics OCT pushes the boundaries further by enabling microscopic imaging of photoreceptor cells. This progression from macroscopic to microscopic imaging exemplifies the profound depth of insight AI brings to ophthalmic diagnostics.

Beyond traditional imaging, additional modalities such as fundus autofluorescence and confocal scanning laser ophthalmoscopy offer metabolic and structural perspectives. Fundus autofluorescence, in particular, captures the metabolic state of retinal pigment epithelium (RPE) cells and assists in monitoring geographic atrophy in diseases like age-related macular degeneration. AI’s analytical prowess allows for automated longitudinal assessments, providing clinicians with vital information about disease progression. Meanwhile, laser scanning enhances image sharpness and structural clarity, supporting biomechanical analyses essential to understanding corneal and retinal pathophysiology.

Anterior segment imaging represents another frontier where AI has made impactful strides. Slit-lamp photographs, commonly used to examine the eye’s anterior structures, now benefit from AI algorithms capable of automatically grading cataract severity and detecting signs of corneal inflammation with remarkable accuracy. Ultrasound biomicroscopy, a specialized imaging technique for visualizing anterior chamber angle structures, complements clinical decision-making in glaucoma management through automated AI-assisted measurements, enhancing both efficiency and reproducibility in diagnosis.

While imaging offers a direct window into anatomical and pathological changes, non-imaging data is equally transformative, especially when decoded through AI for molecular insights. Genomic data analysis has evolved with AI facilitating the identification of risk loci—genetic variants associated with increased susceptibility to ocular diseases—as well as potential drug targets. Transcriptomic data, when mined using advanced AI, reveals differential gene expression patterns between diseased and healthy tissues, highlighting disease mechanisms at the cellular level. Proteomic analysis, empowered by AI, elucidates complex protein interaction networks, identifying key biomarkers that may serve diagnostic or therapeutic roles.

Metabolomics also benefits from AI algorithms that explore small molecular profiles in biological fluids such as blood or aqueous humor. Through these analyses, researchers have discovered novel metabolites implicated in intraocular pressure regulation, vital for understanding and treating glaucoma. Integration of electronic health records (EHR) into AI frameworks further enables phenotype extraction from vast clinical datasets, enhancing disease prediction and personalized management strategies.

The greatest potential lies in the fusion of these diverse data modalities, harnessing AI’s ability to integrate and synthesize complex datasets into holistic diagnostic models. Image-to-image integration techniques allow the transfer of learned features from one imaging modality—such as OCT—to conventional two-dimensional fundus photos, enriching routine screenings with deeper anatomical insights. Multimodal analysis of non-imaging molecular datasets combines genomic, transcriptomic, and proteomic information, constructing detailed biological blueprints and real-time execution profiles that enhance diagnostic robustness and interpretation in hereditary eye conditions.

Moreover, combining imaging and non-imaging data epitomizes the future of precision ophthalmology, where AI aligns phenotypic imaging characteristics with deep molecular profiles and unstructured clinical narratives. This comprehensive alignment not only improves diagnostic accuracy but also provides mechanistic insights into disease pathophysiology, guiding personalized therapeutic interventions.

Despite remarkable advancements, the review emphasizes that fundamental challenges remain. Data standardization across centers and devices is paramount to ensuring AI algorithms generalize well beyond their training environments. Algorithm robustness, interpretability, and the capability to emulate spatiotemporal responses characteristic of biological tissues represent frontiers for future research. The construction and curation of large, cross-center, multimodal databases will be critical to fuel the next wave of intelligent ophthalmic models.

This review marks a pivotal moment in ophthalmic research, illustrating the arc from traditional subjective clinical assessment toward an era defined by data-driven, AI-augmented decision-making. As digital transformation progresses, the fusion of diverse data streams through sophisticated computational frameworks promises to unravel the complexity of ocular diseases, ultimately delivering enhanced patient care worldwide.

Subject of Research: Not applicable

Article Title: Data-driven computational methods in ophthalmology: A multimodal perspective

News Publication Date: 13-May-2026

References: 10.1016/j.edisc.2026.100026

Image Credits: Shujie Zhang

Keywords
Eye, Eye diseases, Vision disorders, Health and medicine, Artificial intelligence

Retinitis pigmentosa presents a complex challenge due to its impact on the photoreceptor cells in the retina, leading to progressive vision loss. The mutation associated with RHO disrupts the production of rhodopsin, a crucial protein for light detection. The excitement surrounding the potential for gene editing as a therapeutic approach has driven researchers to explore novel techniques, particularly base editing, as a more refined tool compared to traditional CRISPR methods. Base editing offers precision, enabling the correction of specific base pairs within the DNA sequence without introducing double-strand breaks, thus minimizing unintended genetic alterations.

In this study, the research team utilized a fluorescence reporter cell system to optimize the base editing approach. They systematically assessed various base editors, single-guide RNAs (sgRNAs), and delivery techniques to determine the most effective combination for editing the targeted mutation. This meticulous experimentation laid the groundwork for further advancements in the application of base editing for therapeutic purposes.

The researchers employed multiple methodologies to confirm the efficacy of their editing strategy. Flow cytometry provided quantitative evidence of rhodopsin restoration within the edited cells, demonstrating a marked improvement in protein levels post-editing. Additionally, western blotting and immunofluorescence microscopy techniques were employed to visualize and quantify the presence of full-length rhodopsin protein. These complementary approaches collectively supported the researchers’ claims of successful editing and protein restoration.

Significantly, DNA sequencing was leveraged to confirm not only the precise editing at the target nucleotide but also to ensure the absence of off-target or bystander edits within the designated editing window. This critical step reinforces the safety profile of the base editing technique, suggesting that it could be a promising avenue for therapeutic intervention in genetic disorders linked to specific mutations.

The research further explored different delivery methods to enhance the uptake and efficacy of the base editing complex. One of the standout results came from using polyethylenimine cationic polymer transfection. When cells were transfected with a plasmid carrying the NG-ABE8e adenine base editor and the A6 guide RNA, which strategically positioned the targeted adenine in the editing window, impressive results were achieved. The data indicated a 31.0% correction in genomic DNA sequence and a 26.3% correction in rhodopsin protein levels, showcasing the potential of this method.

However, the scientists did not stop there; they advanced their approach by isolating the NG-ABE8e enzyme complexed with the A6-sgRNA. This purified protein delivery led to even greater outcomes, with 32.2% genomic DNA editing and a remarkable 44.5% correction in rhodopsin levels observed. These findings underline the importance of optimizing both the editing tool and its delivery for maximizing therapeutic impact.

In a further refinement of their technique, the researchers encapsulated the NG-ABE8e plasmid and A6-sgRNA into lipid nanoparticles (LNPs) for transfection into the reporter cell system. This innovative approach yielded the highest editing efficiency recorded in the study, achieving 42.6% genomic DNA editing and an astounding 65.9% correction in rhodopsin protein levels. The enhanced efficiency of lipid nanoparticle-mediated delivery may open new pathways for the use of base editing in clinical settings, particularly for genetic disorders like RP.

As researchers continue to unravel the complexities of genetic disorders, the findings from this study provide crucial insights into the therapeutic potential of base editing. The successful correction of the c.1030C>T RHO mutation marks a significant milestone in the quest to find effective treatments for retinitis pigmentosa. By demonstrating the capacity of this cutting-edge technology to restore function at the genetic level, this research lays the foundation for future advancements in genome editing and personalized medicine approaches.

Importantly, this study not only emphasizes the success of base editing in a controlled laboratory environment but also raises hopes for its application in real-world scenarios involving patient care. The ongoing pursuit of safe and effective gene therapies reflects the vibrant landscape of scientific inquiry aimed at combating genetic blindness and other inherited conditions. As the understanding of genetic mutations expands, the potential for breakthroughs in treatments for RP and related disorders becomes increasingly attainable.

The collective efforts of the research team, highlighted by their innovative approach to base editing and comprehensive evaluation of delivery strategies, propel us toward a future where genetic disorders may no longer be synonymous with irreversible decline. Therapeutic horizons appear brighter as we explore the intersection of genetic engineering and clinical application, bringing new hope to millions impacted by retinitis pigmentosa and other genetic diseases.

The developments in adenine base editing represent a significant advance not just for retinitis pigmentosa, but also for the field of gene therapy as a whole. Innovation in genetic engineering holds the potential for recalibrating the trajectory of patient outcomes, offering a renewed sense of optimism in the realm of inherited conditions. As we continue to push the boundaries of what’s possible through science, the findings from this research serve as a testament to the relentless pursuit of knowledge—a pursuit that may ultimately yield life-altering therapies for those living with genetic disorders.

In conclusion, the successful demonstration of lipid nanoparticle mediated adenine base editing in correcting a specific mutation associated with retinitis pigmentosa marks a watershed moment in the field of genetic therapy. By harnessing advanced delivery mechanisms and precise editing techniques, researchers are paving the way for transformative treatments that could redefine the landscape of genetic diseases. As we stand on the precipice of a new era in healthcare, the integration of cutting-edge gene editing technologies into patient care represents the dawn of a hope-filled future for those affected by hereditary blindness.

Subject of Research: Base editing for the correction of retinitis pigmentosa-associated RHO mutation.

Article Title: Lipid nanoparticle mediated base editing of the Q344X rhodopsin mutation associated with retinitis pigmentosa.

Article References:

Palmgren, V.A.C., Cheng, M.H.Y., Zhang, Y. et al. Lipid nanoparticle mediated base editing of the Q344X rhodopsin mutation associated with retinitis pigmentosa.
Gene Ther (2025). https://doi.org/10.1038/s41434-025-00584-z

Image Credits: AI Generated

DOI: 10 December 2025

Keywords: retinitis pigmentosa, rhodopsin gene, adenine base editing, gene therapy, genetic disorders.