Science AI Journal
Medicine · Research topic

Open research questions in Retinal Imaging and Analysis

273 unresolved questions extracted from the limitations and future-work sections of 400 Retinal Imaging and Analysis papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The interpretation of IVCM images relies heavily on a limited number of subspecialty experts, - Diagnostic support was rated slightly lower, indicating limited emphasis on differential diagnosis or treatment implications, - Most AI models based on IVCM remain focused on image-level deep learning

    IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy · 2026 · DOI

  • Advanced AI approaches to improve IVCM interpretation and communication, - Development of AI-driven tools to aid both interpretation and report generation, - Investigation of corneal nerve and immune-cell-associated readouts from IVCM as minimally invasive biomarkers

    IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy · 2026 · DOI

  • Low-quality slit-lamp images due to acquisition-related artifacts. Geospatial disparities in ophthalmologic care infrastructure. The need for a practical solution for keratitis diagnosis from degraded images. The challenge of evaluating the framework's performance on a large dataset.

    Automated diagnosis of keratitis from low-quality slit-lamp images using an improved generative adversarial network · 2026 · DOI

  • Further evaluation of the AKDF-LQSI framework in different clinical settings, - Investigation of the application of the SCUNet model to other medical image-based disease recognition tasks, - Development of more advanced models to improve the automatic diagnosis of keratitis

    Automated diagnosis of keratitis from low-quality slit-lamp images using an improved generative adversarial network · 2026 · DOI

  • Existing methods often do not fully utilize complementary clinical information. Traditional diagnosis methods are slow and dependent on medical expertise. There is a need for automated systems to assist ophthalmologists in retinal disease classification.

    SSLVNet: an explainable multi-modal hybrid framework integrating self-supervised learning and vision transformers for multi-label classification of retinal diseases · 2026 · DOI

  • A multi-modal automated retinal eye disease classification using the ODIR-5K dataset was implemented. The model com- bines self-supervised representation learning with hybrid deep neural architectures, including CNNs and ViT, to efficiently cap- ture both localized retinal abnormalities and wider structural designs. Additionally, attention-based mechanisms and graph- oriented feature modeling are incorporated to enhance the under- standing of complex lesion relationships within retinal images. Experimental results indicate the proposed system delivers reliable performance across multiple retinal disease categories. Moreover, interpretability is improved through Grad-CAM visualizations that highlight influential retinal regions, while uncertainty estima- tion increases the confidence and trustworthiness of predictions in clinical use. Further research may also focus on developing lightweight transformer architectures that reduce computational cost while maintaining accuracy. In addition, deploying the model in real-time clinical screening environments and integrating it into hospital decision-support systems would improve practical usability and assist clinicians in the early detection of retinal diseases.

    SSLVNet: an explainable multi-modal hybrid framework integrating self-supervised learning and vision transformers for multi-label classification of retinal diseases · 2026 · DOI

  • The current detection methods for diabetic retinopathy have limitations, including time-consuming manual evaluation and variability due to subjective judgment. There is a need for automated and accurate detection methods. Deep learning algorithms have the potential to fill this gap.

    Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis · 2025 · DOI

  • investigate the mechanisms by which retinal microvascular function affects depressive symptoms, - examine the relationship between retinal microvascular function and depressive symptoms in different populations, - explore the potential of retinal microvascular function as a biomarker for depression

    Retinal microvascular function and incidence and trajectories of clinically relevant depressive symptoms: the Maastricht Study · 2024 · DOI

  • Feature degradation during structure extraction. Confusion during learning due to the feature values of the retinal vessels when learning the feature values of the optic disc. Limited dataset size.

    Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images · 2024 · DOI

  • It points out the system’s strategy of multi-scale feature fusion as a key method to dealing with issues like lack of data, unbalanced class distribution, and poor generalization.

    Feature fusion deep learning approach for detection and grading assessment of cataracts from fundus images · 2026 · DOI

  • Abstract Accurate retinal disease classification from optical coherence tomography (OCT) images depends critically on CNN hyperparameter settings; hand-picked configurations yield inconsistent results, and grid search over an eight-parameter space requires 384 model evaluations.

    Automated retinal disease classification from OCT images using particle swarm-optimized deep learning · 2026 · DOI

  • Future research should focus on standardizing datasets, improving model interpretability, and validating performance across diverse populations.

    Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis · 2025 · DOI

  • Few studies have examined obesity indicators for retinopathy, and our research is intended to elucidate this relationship.

    Association between obesity indicators and retinopathy in US adults: NHANES 2005–2008 · 2025 · DOI

  • Unlike conventional U-Net variants, Y-Net introduces dual decoders optimized separately for arteries and veins, a parallel multi-scale input branch to enhance thin-vessel detection, and DropBlock regularization to improve robustness under limited data.

    Enhancing artery/vein classification in Retinal images using Y-Net convolutional networks · 2025 · DOI

  • Heterogeneous data formats and the lack of standardized metadata hinder seamless integration, while privacy risks necessitate advanced techniques such as anonymization.

    Artificial intelligence technology in ophthalmology public health: current applications and future directions · 2025 · DOI

  • However, insufficient data standardization, the "black box" characteristics of the model, and privacy ethics issues are still the bottlenecks in clinical application.

    Deep learning-driven approach for cataract management: towards precise identification and predictive analytics · 2025 · DOI

  • The association between retinal microvascular function and incidence of clinically relevant depressive symptoms is not well understood. The 'vascular depression hypothesis' is not fully explored in relation to retinal microvascular function.

    Retinal microvascular function and incidence and trajectories of clinically relevant depressive symptoms: the Maastricht Study · 2024 · DOI

  • Future research should explore potential factors that underlie the association between onset timing of visual difficulty and cognitive function.

    Associations Between Self-Reported Visual Difficulty, Age of Onset, and Cognitive Function Trajectories Among Chinese Older Adults · 2024 · DOI

  • Different studies have demonstrated contradictory results for the association between HbA1c variability and diabetic retinopathy.

    Glycemic profile variability as an independent predictor of diabetic retinopathy in patients with type 2 diabetes: a prospective cohort study · 2024 · DOI

  • Future research will focus on incorporating additional fundus structures such as the macula, refining extraction algorithms, and expanding the types of classified eye diseases.

    Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images · 2024 · DOI

  • However, it is imperative to acknowledge that further targeted investigations are warranted to address the inherent limitations of the existing body of literature.

    Unlocking the Potential of Vessel Density and the Foveal Avascular Zone in Optical Coherence Tomography Angiography as Biomarkers in Alzheimer’s Disease · 2024 · DOI

  • The dataset was limited to 182 FFA images. The method requires expert-annotated labels. The method may not generalize to other datasets.

    An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images · 2026 · DOI

  • The quantitative comparison was conducted on only 36 images out of 40 test FFA images where leakage points were successfully detected, limiting the generalizability…

    An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images · 2026 · DOI

  • The study identifies a gap in the existing literature on the impact of preprocessing on CNN architectures for eye disease detection. The study highlights the need for a novel model that can accurately diagnose and classify eye diseases.

    Evaluating the impact of preprocessing on CNN architectures: comparative analysis of a novel proposed model EyeDiagNet and existing models for eye disease detection · 2026 · DOI

  • Future research and development opportunities saturate when artificial intelligence (AI) is applied to the diagnosis of ocular disorders. Improving hybrid models which fuse deep learning and conventional machine learning remains a primary goal to achieve improved generalizability and performance. While the two-centre validation provides an impor- tant step toward clinical applicability, further large-scale, multi-centre investigations outside Kerala are still required. Expanding validation to include more diverse popula- tions, imaging devices, and clinical workflows will be crucial to establish EyeDiagNet’s robustness, scalability, and readiness for widespread real-world deployment. Optimized models designed for specific eye diseases, including cataract, diabetic retinopathy (DR) or glaucoma, may lead to more accurate diagnoses [46].

    Evaluating the impact of preprocessing on CNN architectures: comparative analysis of a novel proposed model EyeDiagNet and existing models for eye disease detection · 2026 · DOI

Most-cited papers in Retinal Imaging and Analysis

Most recent work

Find a gap in your own Retinal Imaging and Analysis sub-topic

This page shows what the Retinal Imaging and Analysis literature already flags as unresolved. To narrow it to your specific question, run the guided finder — it searches the gap library on demand and checks candidates against 250M+ OpenAlex works.

Open the Research Gap Finder →

Related topics in Medicine

Artificial Intelligence in Healthcare and Education
2,044 open questions
Substance Abuse Treatment and Outcomes
1,964 open questions
Smoking Behavior and Cessation
990 open questions
Dementia and Cognitive Impairment Research
974 open questions
Innovations in Medical Education
960 open questions
Physical Activity and Health
923 open questions
Attention Deficit Hyperactivity Disorder
839 open questions
Schizophrenia research and treatment
709 open questions
273 open questions have been extracted from the limitations and future-work passages of 400 Retinal Imaging and Analysis papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

Tools for your next paper

Gaps & Literature SearchFind open questions in any field — 120,000+ open gaps mined from our own library.Pre-CheckIs your paper ready? A Tier 1–5 acceptance probability in seconds.Journal FinderA ranked shortlist of target journals from a 17,500-venue index.Duplicate Publication CheckerCheck prior-publication & salami-slicing risk across 8 sources.Citation GeneratorFreeA DOI or title → APA, MLA, Vancouver, Chicago, BibTeX, RIS. No signup.Graphical Abstract MakerFreeTurn your findings into a colorblind-safe graphical abstract.

Compare the category — Honest roundups of the AI research tools, ours listed alongside the alternatives.

Command palette

Jump anywhere, run any action.