Artificial intelligence in corneal diseases: A narrative review.

Corneal diseases represent a growing public health burden, especially in resource-limited settings lacking access to specialized eye care. Artificial intelligence (AI) offers promising solutions for automating the diagnosis and management of corneal conditions. This narrative review examines the application of AI in corneal diseases, focusing on keratoconus, infectious keratitis, pterygium, dry eye disease, Fuchs endothelial corneal dystrophy, and corneal transplantation. AI models integrating diverse imaging modalities (e.g., corneal topography, slit-lamp, and anterior segment OCT images) and clinical data have demonstrated high diagnostic accuracy, often outperforming human experts. Emerging trends include the incorporation of biomechanical data to enhance keratoconus detection, leveraging in vivo confocal microscopy for diagnosing infectious keratitis, and employing multimodal approaches for comprehensive disease analysis. Additionally, AI has shown potential in predicting disease progression, treatment outcomes, and postoperative complications in corneal transplantation. While challenges remain such as population heterogeneity, limited external validation, and the "black box" nature of some models, ongoing advancement in explainable AI, data augmentation, and improved regulatory frameworks can serve to address these limitations.

Imaging in spaceflight associated neuro-ocular syndrome (SANS): Current technology and future directions in modalities.

With plans for future long-duration crewed exploration, NASA has identified several high priority potential health risks to astronauts in space. One such risk is a collection of neurologic and ophthalmic findings termed spaceflight associated neuro-ocular syndrome (SANS). The findings of SANS include optic disc edema, globe flattening, retinal nerve fiber layer thickening, chorioretinal folds, hyperopic shifts, and cotton-wool spots. The cause of SANS was initially thought to be a cephalad fluid shift in microgravity leading to increased intracranial pressure, venous stasis and impaired CSF outflow, but the precise etiology of SANS remains ill defined. Recent studies have explored multiple possible pathogenic mechanisms for SANS including genetic and hormonal factors; a cephalad shift of fluid into the orbit and brain in microgravity; and disruption to the brain glymphatic system. Orbital, ocular, and cranial imaging, both on Earth and in space has been critical in the diagnosis and monitoring of SANS (e.g., fundus photography, optical coherence tomography (OCT), magnetic resonance imaging (MRI), and orbital/cranial ultrasound). In addition, we highlight near-infrared spectroscopy and diffusion tensor imaging, two newer modalities with potential use in future studies of SANS. In this manuscript we provide a review of these modalities, outline their current and potential use in space and on Earth, and review the reported major imaging findings in SANS.

Lower body negative pressure as a research tool and countermeasure for the physiological effects of spaceflight: A comprehensive review.

Lower Body Negative Pressure (LBNP) redistributes blood from the upper body to the lower body. LBNP may prove to be a countermeasure for the multifaceted physiological changes endured by astronauts during spaceflight related to cephalad fluid shift. Over more than five decades, beginning with the era of Skylab, advancements in LBNP technology have expanded our understanding of neurological, ophthalmological, cardiovascular, and musculoskeletal adaptations in space, with particular emphasis on mitigating issues such as bone loss. To date however, no comprehensive review has been conducted that chronicles the evolution of this technology or elucidates the broad-spectrum potential of LBNP in managing the diverse physiological challenges encountered in the microgravity environment. Our study takes a chronological perspective, systematically reviewing the historical development and application of LBNP technology in relation to the various pathophysiological impacts of spaceflight. The primary objective is to illustrate how this technology, as it has evolved, offers an increasingly sophisticated lens through which to interpret the systemic effects of space travel on human physiology. We contend that the insights gained from LBNP studies can significantly aid in formulating targeted and effective countermeasures to ensure the health and safety of astronauts. Ultimately, this paper aspires to promote a more cohesive understanding of the broad applicability of LBNP as a countermeasure against multiple bodily effects of space travel, thereby contributing to a safer and more scientifically informed approach to human space exploration.

Biophysics of ophthalmic medications during spaceflight: Principles of ocular fluid dynamics and pharmacokinetics in microgravity.

As spaceflight becomes increasingly accessible and expansive to humanity, it is becoming ever more essential to consider the treatment of various eye diseases in these challenging environments. This paper delves into the increasing fascination with interplanetary travel and its implications for health management in varying environments. It specifically discusses the pharmacological management of ocular diseases, focusing on two key delivery methods: topical eye drops and intravitreal injections. The paper explores how microgravity impacts the administration of these treatments, a vital aspect in understanding drug delivery in space. An extensive analysis is presented on the pharmacokinetics of eye medications, examining the interaction between pharmaceuticals and ocular tissues in zero gravity. The goal of the paper is to bridge the understanding of fluid dynamics, microgravity and the human physiological systems to pave the way for innovative solutions faced by individuals in microgravity.

Oculomics analysis in multiple sclerosis: Current ophthalmic clinical and imaging biomarkers.

Multiple Sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal damage. Early recognition and treatment are important for preventing or minimizing the long-term effects of the disease. Current gold standard modalities of diagnosis (e.g., CSF and MRI) are invasive and expensive in nature, warranting alternative methods of detection and screening. Oculomics, the interdisciplinary combination of ophthalmology, genetics, and bioinformatics to study the molecular basis of eye diseases, has seen rapid development through various technologies that detect structural, functional, and visual changes in the eye. Ophthalmic biomarkers (e.g., tear composition, retinal nerve fibre layer thickness, saccadic eye movements) are emerging as promising tools for evaluating MS progression. The eye's structural and embryological similarity to the brain makes it a potentially suitable assessment of neurological and microvascular changes in CNS. In the advent of more powerful machine learning algorithms, oculomics screening modalities such as optical coherence tomography (OCT), eye tracking, and protein analysis become more effective tools aiding in MS diagnosis. Artificial intelligence can analyse larger and more diverse data sets to potentially discover new parameters of pathology for efficiently diagnosing MS before symptom onset. While there is no known cure for MS, the integration of oculomics with current modalities of diagnosis creates a promising future for developing more sensitive, non-invasive, and cost-effective approaches to MS detection and diagnosis.

Generative artificial intelligence in ophthalmology.

Generative AI has revolutionized medicine over the past several years. A generative adversarial network (GAN) is a deep learning framework that has become a powerful technique in medicine, particularly in ophthalmology and image analysis. In this paper we review the current ophthalmic literature involving GANs, and highlight key contributions in the field. We briefly touch on ChatGPT, another application of generative AI, and its potential in ophthalmology. We also explore the potential uses for GANs in ocular imaging, with a specific emphasis on 3 primary domains: image enhancement, disease identification, and generating of synthetic data. PubMed, Ovid MEDLINE, Google Scholar were searched from inception to October 30, 2022 to identify applications of GAN in ophthalmology. A total of 40 papers were included in this review. We cover various applications of GANs in ophthalmic-related imaging including optical coherence tomography, orbital magnetic resonance imaging, fundus photography, and ultrasound; however, we also highlight several challenges, that resulted in the generation of inaccurate and atypical results during certain iterations. Finally, we examine future directions and considerations for generative AI in ophthalmology.

Dynamic Visual Acuity, Vestibulo-Ocular Reflex, and Visual Field in National Football League (NFL) Officiating: Physiology and Visualization Engineering for 3D Virtual On-Field Training.

The ability to make on-field, split-second decisions is critical for National Football League (NFL) game officials. Multiple principles in visual function are critical for accuracy and precision of these play calls, including foveation time and unobstructed line of sight, static visual acuity, dynamic visual acuity, vestibulo-ocular reflex, and sufficient visual field. Prior research has shown that a standardized curriculum in these neuro-ophthalmic principles have demonstrated validity and self-rated improvements in understanding, confidence, and likelihood of future utilization by NFL game officials to maximize visual performance during officiating. Virtual reality technology may also be able to help optimize understandings of specific neuro-ophthalmic principles and simulate real-life gameplay. Personal communication between authors and NFL officials and leadership have indicated that there is high interest in 3D virtual on-field training for NFL officiating. In this manuscript, we review the current and past research in this space regarding a neuro-ophthalmic curriculum for NFL officials. We then provide an overview our current visualization engineering process in taking real-life NFL gameplay 2D data and creating 3D environments for virtual reality gameplay training for football officials to practice plays that highlight neuro-ophthalmic principles. We then review in-depth the physiology behind these principles and discuss strategies to implement these principles into virtual reality for football officiating.

FA4SANS-GAN: A Novel Machine Learning Generative Adversarial Network to Further Understand Ophthalmic Changes in Spaceflight Associated Neuro-Ocular Syndrome (SANS).

Purpose: To provide an automated system for synthesizing fluorescein angiography (FA) images from color fundus photographs for averting risks associated with fluorescein dye and extend its future application to spaceflight associated neuro-ocular syndrome (SANS) detection in spaceflight where resources are limited. Design: Development and validation of a novel conditional generative adversarial network (GAN) trained on limited amount of FA and color fundus images with diabetic retinopathy and control cases. Participants: Color fundus and FA paired images for unique patients were collected from a publicly available study. Methods: FA4SANS-GAN was trained to generate FA images from color fundus photographs using 2 multiscale generators coupled with 2 patch-GAN discriminators. Eight hundred fifty color fundus and FA images were utilized for training by augmenting images from 17 unique patients. The model was evaluated on 56 fluorescein images collected from 14 unique patients. In addition, it was compared with 3 other GAN architectures trained on the same data set. Furthermore, we test the robustness of the models against acquisition noise and retaining structural information when introduced to artificially created biological markers. Main Outcome Measures: For GAN synthesis, metric Fréchet Inception Distance (FID) and Kernel Inception Distance (KID). Also, two 1-sided tests (TOST) based on Welch's t test for measuring statistical significance. Results: On test FA images, mean FID for FA4SANS-GAN was 39.8 (standard deviation, 9.9), which is better than GANgio model's mean of 43.2 (standard deviation, 13.7), Pix2PixHD's mean of 57.3 (standard deviation, 11.5) and Pix2Pix's mean of 67.5 (standard deviation, 11.7). Similarly for KID, FA4SANS-GAN achieved mean of 0.00278 (standard deviation, 0.00167) which is better than other 3 model's mean KID of 0.00303 (standard deviation, 0.00216), 0.00609 (standard deviation, 0.00238), 0.00784 (standard deviation, 0.00218). For TOST measurement, FA4SANS-GAN was proven to be statistically significant versus GANgio (P = 0.006); versus Pix2PixHD (P < 0.00001); and versus Pix2Pix (P < 0.00001). Conclusions: Our study has shown FA4SANS-GAN to be statistically significant for 2 GAN synthesis metrics. Moreover, it is robust against acquisition noise, and can retain clear biological markers compared with the other 3 GAN architectures. This deployment of this model can be crucial in the International Space Station for detecting SANS. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.