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 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.
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.
BACKGROUND: To determine whether a neuro-ophthalmic curriculum would improve National Football League (NFL) game officials' self-rated knowledge and interest in neuro-ophthalmic principles to improve precision and accuracy of NFL play-calling. METHODS: The formalized and structured neuro-ophthalmic principles (NOP) curriculum was introduced to 121 NFL game officials, 17 replay officials, and 4 officiating staff who attended the NFL Official Training Camp in Irving, Texas, on September 8 and 9, 2023. Before and after the lecture and videos were introduced, participants completed an optional hard-copy feedback form pertaining to self-reported NOP knowledge, likelihood of using said terms, and interest in future content of NOP applicable NFL officiating. Paired 2-tailed t tests were used for statistical analysis to directly compare the self-reported knowledge before and after the neuro-ophthalmic curriculum introduction. RESULTS: One hundred forty-two participants completed the prelecture and postlecture feedback forms self-reported knowledge after the NOP curriculum was given to the NFL officiating staff. All (142/142) participants completed a survey. There was a statistically significant improvement in the mean ratings of the prelecture vs. postlecture understanding of the specific neuro-ophthalmic terms pertinent to NFL game officials (2.6 [95% CI, 2.3-3.0] vs. 7.9 [95% CI, 7.6-8.2], P < 0.001) and 2.7 [95% CI, 2.3-3.0] vs. 7.7 [95% CI, 7.4-8.0]), respectively. There was a statistically significant greater likelihood of using said terms prelecture vs. postlecture (2.9 [95% CI, 2.4-3.4] vs. 7.5 [95% CI, 7.2-7.9], P < 0.001). CONCLUSIONS: This study found a statistically significant improvement in neuro-ophthalmic knowledge and a greater likelihood of using NOP terms following the NOP curriculum. NFL game officials, replay officials, and staff are interested in expanding their knowledge in the vision science of neuro-ophthalmic concepts and applications involved in play-calling. We hope that our pilot data will lead to a model of education that will improve the precision and accuracy of NFL play-calls by officials on game days.
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.
The phrase "Bench-to-Bedside" is a well-known phrase in medicine, highlighting scientific discoveries that directly translate to impacting patient care. Key examples of translational research include identification of key molecular targets in diseases and development of diagnostic laboratory tests for earlier disease detection. Bridging these scientific advances to the bedside/clinic has played a meaningful impact in numerous patient lives. The spaceflight environment poses a unique opportunity to also make this impact; the nature of harsh extraterrestrial conditions and medically austere and remote environments push for cutting-edge technology innovation. Many of these novel technologies built for the spaceflight environment also have numerous benefits for human health on Earth. In this manuscript, we focus on "Spaceflight-to-Eye Clinic" and discuss technologies built for the spaceflight environment that eventually helped to optimize ophthalmic health on Earth (e.g., LADAR for satellite docking now utilized in eye-tracking technology for LASIK). We also discuss current technology research for spaceflight associated neuro-ocular syndrome (SANS) that may also be applied to terrestrial ophthalmic health. Ultimately, various advances made to enable to the future of space exploration have also advanced the ophthalmic health of individuals on Earth.
Delivery of Health Care , Space Flight , Humans , Eye Diseases , Aerospace Medicine/methods , Translational Research, Biomedical/methods , Weightlessness , Ophthalmology/methods
Ocular health is currently a major concern for astronauts on current and future long-duration spaceflight missions. Spaceflight-associated neuro-ocular syndrome (SANS) is a collection of ophthalmic and neurologic findings that is one potential physiologic barrier to interplanetary spaceflight. Since its initial report in 2011, our understanding of SANS has advanced considerably, with a primary focus on posterior ocular imaging including fundus photography and optical coherence tomography. However, there may be changes to the anterior segment that have not been identified. Additional concerns to ocular health in space include corneal damage and radiation-induced cataract formation. Given these concerns, precision anterior segment imaging of the eye would be a valuable addition to future long-duration spaceflights. The purpose of this paper is to review ultrasound biomicroscopy (UBM) and its potential as a noninvasive, efficient imaging modality for spaceflight. The analysis of UBM for spaceflight is not well defined in the literature, and such technology may help to provide further insights into the overall anatomical changes in the eye in microgravity.
Spaceflight associated neuro-ocular syndrome (SANS) is one of the largest physiologic barriers to spaceflight and requires evaluation and mitigation for future planetary missions. As the spaceflight environment is a clinically limited environment, the purpose of this research is to provide automated, early detection and prognosis of SANS with a machine learning model trained and validated on astronaut SANS optical coherence tomography (OCT) images. In this study, we present a lightweight convolutional neural network (CNN) incorporating an EfficientNet encoder for detecting SANS from OCT images titled "SANS-CNN." We used 6303 OCT B-scan images for training/validation (80%/20% split) and 945 for testing with a combination of terrestrial images and astronaut SANS images for both testing and validation. SANS-CNN was validated with SANS images labeled by NASA to evaluate accuracy, specificity, and sensitivity. To evaluate real-world outcomes, two state-of-the-art pre-trained architectures were also employed on this dataset. We use GRAD-CAM to visualize activation maps of intermediate layers to test the interpretability of SANS-CNN's prediction. SANS-CNN achieved 84.2% accuracy on the test set with an 85.6% specificity, 82.8% sensitivity, and 84.1% F1-score. Moreover, SANS-CNN outperforms two other state-of-the-art pre-trained architectures, ResNet50-v2 and MobileNet-v2, in accuracy by 21.4% and 13.1%, respectively. We also apply two class-activation map techniques to visualize critical SANS features perceived by the model. SANS-CNN represents a CNN model trained and validated with real astronaut OCT images, enabling fast and efficient prediction of SANS-like conditions for spaceflight missions beyond Earth's orbit in which clinical and computational resources are extremely limited.
Apple unveiled its highly anticipated mixed-reality headset, called the Apple Vision Pro on June 5, 2023. The primary user interface relies on eye tracking, hand, gestures, cameras, and sensors, eliminating the need for physical controllers such as keyboards or touch screens. The refined capabilities of this technology can be utilized for diverse purposes, including but not limited to medical and surgical education, and remote medical consultations. All things considered, virtual reality is a highly promising area for the future of medicine, from improving medical education and vision screening to physical and psychological rehabilitation. We look forward to further innovations in this exciting area for years to come.
Education, Medical , Malus , Virtual Reality , Humans , User-Computer Interface
PURPOSE: To evaluate the impacts of the COVID-19 on neuro-ophthalmology practice in the United States. DESIGN: Cross-sectional study. METHODS: The North American Neuro-ophthalmology Society distributed a survey on the impact of COVID-19 on neuro-ophthalmic practice to its members. The survey consisted of 15 questions regarding the impact of the pandemic on neuro-ophthalmic practice and perspectives. RESULTS: Twenty-eight neuro-ophthalmologists practicing in the United States responded to our survey. In this survey, 64% of survey respondents were male (n = 18), while 36% were female (n = 10). The average age of a respondent was 55 years old. According to 77% of survey respondents, various neuro-ophthalmic diseases were reported to have worsened during the pandemic including idiopathic intracranial hypertension, compressive optic neuropathy, optic neuritis, and giant cell arteritis. CONCLUSIONS: This survey represents one of the largest studies to describe the impact of the COVID-19 pandemic of neuro-ophthalmology. Given the underrepresentation of neuro-ophthalmology in the United States as described in the literature, this study strengthens the need for more neuro-ophthalmologists to provide timely care, particularly during the pandemic. Further interventions to incentivize the pursuit of neuro-ophthalmology training may help combat the effects of COVID-19 on neuro-ophthalmic conditions.
COVID-19 , Ophthalmology , Optic Nerve Diseases , Humans , Male , United States/epidemiology , Female , Middle Aged , COVID-19/epidemiology , Pandemics , Cross-Sectional Studies
Head-mounted visualization technology, often in the form of virtual, augmented, and mixed reality (VAMR), has revolutionized how visual disorders may be approached clinically. In this manuscript, we review the available literature on VAMR for visual disorders and provide a clinically oriented guide to how VAMR technology has been deployed for visual impairments. The chief areas of clinical investigation with VAMR are divided include (1) vision assessment, (2) vision simulation, and (3) vision rehabilitation. We discuss in-depth the current literature of these areas in VAMR and upcoming/future applications to combat the detrimental impact of visual impairment worldwide.
Augmented Reality , Humans , Computer Simulation , Vision Disorders , Technology
