Nociceptors respond to noxious stimuli and transmit pain signals to the central nervous system. In the cornea, the nociceptors located in the most external layer provide a myriad of sensation modalities. Damage to these corneal nerve fibers can induce neuropathic pain. In response, corneal nerves become sensitized to previously non-noxious stimuli. Assessing corneal pain origin is a complex ophthalmic challenge due to variations in its causes and manifestations. Current FDA-approved therapies for corneal nociceptive pain, such as acetaminophen and NSAIDs, provide only broad-acting relief with unwanted side effects, highlighting the need for precision medicine for corneal nociceptive pain. A few targeted treatments, including perfluorohexyloctane (F6H8) eye drops and Optive Plus (TRPV1 antagonist), are FDA-approved, while others are in preclinical development. Treatments that target signaling pathways related to neurotrophic factors, such as nerve growth factors and ion channels, such as the transient receptor potential (TRP) family or tropomyosin receptor kinase A, may provide a potential combinatory therapeutic approach. This review describes the roles of nociceptors in corneal pain. In addition, it evaluates molecules within nociceptor signaling pathways for their potential to serve as targets for efficient therapeutic strategies for corneal nociceptive pain aimed at modulating neurotrophic factors and nociceptive channel sensitivity.

This study aims to examine the geographic distribution of ocular oncologists in the United States and evaluates how social determinants of health (SDOH) and environmental factors influence access to ocular oncologists nationally and uveal melanoma (UM) outcomes locally at the University of Illinois (UI) Hospital. A national analysis mapped ocular oncologist locations using ArcGIS Pro, assessing accessibility via drive-time radii (15–60 minutes) and census tract population density. SDOH variables (income, education, and insurance) were extracted from US Census data. A retrospective chart review of 167 UM patients at UI Hospital (2010–2023) analyzed tumor characteristics, referral patterns, and SDOH/environmental factors linked to zip codes. Statistical analyses included t-tests, logistic regression, and False Discovery Rate correction. Nationally, 33.5% of the population, including 45.9% of rural residents, live beyond 60 minutes of an ocular oncologist. Areas outside of an accessible distance of an ocular oncologist were more likely to have low-income, less-educated, and uninsured populations (p < 0.001). At UI Hospital, patients outside a 60-minute radius required significantly more referrals (p = 0.046) but showed no differences in tumor stage at presentation. Trends suggested larger tumor thickness in areas with fewer naturalized citizens and more households under the Asset Limited, Income Constrained, Employed threshold. (q = 0.068 and q = 0.093, respectively). Environmental factors showed no significant associations, including Lifetime Inhalation Cancer Risk, Water Polluting Sites Environmental Justice Index, and Optometrists Per Capita. There may be geographic and socioeconomic barriers that limit access to UM care, particularly in rural and underserved communities. While proximity did not affect tumor presentation at UI Hospital, referral delays and socioeconomic trends highlight systemic inequities. Multicenter studies are needed to further explore these disparities and improve equitable UM care delivery.

Background/Objectives: Keratoconus (KC) is a chronic disease that causes progressive corneal thinning and steepening, thereby negatively impacting visual acuity. Although corneal topography and keratometry are the primary measures to diagnose KC, access to these methods can be limited by various factors. To address these limitations, this study evaluates a novel low-cost deep-learning algorithm that infers keratometric categories from smartphone-assisted Placido ring photographs. Methods: Development utilized 1240 healthy control eye images and 188 K1-labeled KC images for pretraining, without using their K1 labels. A Variational Autoencoder with KL divergence regularization (AutoEncoderKL) was trained on this pool; its encoder generated latent features for KC images (n = 535). A held-out set (n = 70) with Pentacam keratometry was labeled by K1 into 47 D. An ensemble classifier chosen via grid search and cross-validation used the encoder features. Performance was assessed for accuracy, precision, recall, and F1-score. Results: The model achieved 91% accuracy across all classes. Precision of the model was 0.77 (47 D); recall was 0.83, 0.91, and 1.00; and F1-scores were 0.80, 0.94, and 0.92, respectively. Notably, the model achieved perfect recall for the >47 D K1 category. Conclusions: A smartphone-assisted Placido ring imaging approach was able to predict K1-based keratometric categories without requiring tomographic or keratometric measurements as model inputs at inference. These findings provide preliminary proof-of-concept for the potential use of smartphone-assisted Placido ring images as a low-cost approach for K1-based stratification. Larger externally validated studies across different sites, devices, operators, printed Placido discs, acquisition conditions, and patient populations are required before clinical utility can be assessed.