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.

"Spaceflight-to-Eye Clinic": Terrestrial advances in ophthalmic healthcare delivery from space-based innovations.

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.

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.

SANS-CNN: An automated machine learning technique for spaceflight associated neuro-ocular syndrome with astronaut imaging data.

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.

Impact of COVID-19 on Neuro-Ophthalmology in the United States: A National Survey.

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.

Radiation-induced ophthalmic risks of long duration spaceflight: Current investigations and interventions.

PURPOSE: As the average duration of space missions increases, astronauts will experience longer periods of exposure to risks of long duration space flight including microgravity and radiation. The risks from long-term exposure to space radiation remains ill-defined. We review the current literature on the possible and known risks of radiation on the eye (including radiation retinopathy) after long duration spaceflight. METHODS: A PubMed and Google Scholar search of the English language ophthalmic literature was performed from inception to July 11, 2022. The following search terms were utilized independently or in conjunction to build this manuscript: "Radiation Retinopathy", "Spaceflight", "Space Radiation", "Spaceflight Associated Neuro-Ocular Syndrome", "Microgravity", "Hypercapnia", "Radiation Shield", "Cataract", and "SANS". A concise and selective approach of references was conducted in including relevant original studies and reviews. RESULTS: A total of 65 papers were reviewed and 47 papers were included in our review. CONCLUSION: We discuss the potential and developing countermeasures to mitigate these radiation risks in preparation for future space exploration. Given the complex nature of space radiation, no single approach will fully reduce the risks of developing radiation maculopathy in long-duration spaceflight. Understanding and appropriately overcoming the risks of space radiation is key to becoming a multi-planetary species.

Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys.

Pendant drops of oxide-coated high-surface tension fluids frequently produce perturbed shapes that impede interfacial studies. Eutectic gallium indium or Galinstan are high-surface tension fluids coated with a ∼5 nm gallium oxide (Ga2O3) film and falls under this fluid classification, also known as liquid metals (LMs). The recent emergence of LM-based applications often cannot proceed without analyzing interfacial energetics in different environments. While numerous techniques are available in the literature for interfacial studies- pendant droplet-based analyses are the simplest. However, the perturbed shape of the pendant drops due to the presence of surface oxide has been ignored frequently as a source of error. Also, exploratory investigations of surface oxide leveraging oscillatory pendant droplets have remained untapped. We address both challenges and present two contributing novelties- (a) by utilizing the machine learning (ML) technique, we predict the approximate surface tension value of perturbed pendant droplets, (ii) by leveraging the oscillation-induced bubble tensiometry method, we study the dynamic elastic modulus of the oxide-coated LM droplets. We have created our dataset from LM's pendant drop shape parameters and trained different models for comparison. We have achieved >99% accuracy with all models and added versatility to work with other fluids. The best-performing model was leveraged further to predict the approximate values of the nonaxisymmetric LM droplets. Then, we analyzed LM's elastic and viscous moduli in air, harnessing oscillation-induced pendant droplets, which provides complementary opportunities for interfacial studies alternative to expensive rheometers. We believe it will enable more fundamental studies of the oxide layer on LM, leveraging both symmetric and perturbed droplets. Our study broadens the materials science horizon, where researchers from ML and artificial intelligence domains can work synergistically to solve more complex problems related to surface science, interfacial studies, and other studies relevant to LM-based systems.

Retina Oculomics in Neurodegenerative Disease.

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging technologies including electroretinography, optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy, fluorescence lifetime imaging ophthalmoscopy, and OCT angiography have revolutionized the ability to understand systemic diseases and even detect them earlier than clinical manifestations for earlier intervention. With the advent of increasingly large ophthalmic imaging datasets, machine learning models can be integrated into these ocular imaging biomarkers to provide further insights and prognostic predictions of neurodegenerative disease. In this manuscript, we review the use of ophthalmic imaging to provide insights into neurodegenerative diseases including Alzheimer Disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, and Huntington Disease. We discuss recent advances in ophthalmic technology including eye-tracking technology and integration of artificial intelligence techniques to further provide insights into these neurodegenerative diseases. Ultimately, oculomics opens the opportunity to detect and monitor systemic diseases at a higher acuity. Thus, earlier detection of systemic diseases may allow for timely intervention for improving the quality of life in patients with neurodegenerative disease.

Poster Session: Comparison of Dynamic Visual Acuity Assessments in Head-Mounted Technology and Traditional Laptop-based Method.

An impairment in dynamic visual acuity (DVA) has been observed in astronauts shortly after they return to Earth.1 These transitional effects may lead to safety risks during interplanetary spaceflight. At this time, functional vision assessments are performed via laptop onboard the International Space Station. However, DVA is not performed as a standard assessment, and optimization of traditional assessments may aid in more efficient and frequent testing. As part of our group's NASA-funded head-mounted visual assessment system to detect subtle vision changes in long-duration spaceflight2, we present a method to measure DVA in virtual reality. An early validation study was conducted with 5 subjects comparing our novel assessment with a traditional laptop-based test. All participants had a best correctable visual acuity of 20/20, had no past ocular history, balancing disorders, or neurological history. Our DVA assessment framework was built in UnrealEngine 4. The early validation study confirmed that our VR-based DVA assessment performed similarly to traditional laptop-based test (0.485 and 0.525 LogMar respectively, Pearson Correlation = 0.911). A Bland-Altman plot and analysis demonstrated that our DVA assessment data fell within the upper and lower limits of agreement. Future studies are required to further validate this technology; however, these early results showcase VR-based DVA assessment as a promising alternative to laptop-based methods.

Poster Session: Detecting spaceflight associated neuro-ocular syndrome (SANS) using light-weight convolutional neural networks.

Spaceflight-associated neuro-ocular syndrome (SANS) is a collection of neuro-ophthalmic findings that occurs in astronauts as a result of prolonged microgravity exposure in space. Due to limited resources on board long-term spaceflight missions, early disease diagnosis and prognosis of SANS become unviable. Moreover, the current retinal imaging techniques onboard the international space station (ISS), such as optical coherence tomography (OCT), ultrasound imaging, and fundus photography, require an expert to distinguish between SANS and similar ophthalmic diseases. With the advent of Deep Learning, diagnosing diseases (such as diabetic retinopathy) from structural retinal images are being automated. In this study, we propose a lightweight convolutional neural network incorporating an EfficientNet encoder for detecting SANS from OCT images. We used 6303 OCT B-scan images for training/validation (80%/20% split) and 945 for testing. Our model achieved 84.2% accuracy on the test set, i.e., 85.6% specificity, and 82.8% sensitivity. Moreover, it outperforms two other state-of-the-art pre-trained architectures, ResNet50-v2 and MobileNet-v2, by 21.4% and 13.1%. Additionally, we use GRAD-CAM to visualize activation maps of intermediate layers to test the interpretability of our model's prediction. The proposed architecture enables fast and efficient prediction of SANS-like conditions for future long-term spaceflight mission in which computational and clinical resources are limited.

Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS).

Spaceflight associated neuro-ocular syndrome (SANS) is a unique phenomenon that has been observed in astronauts who have undergone long-duration spaceflight (LDSF). The syndrome is characterized by distinct imaging and clinical findings including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. SANS serves a large barrier to planetary spaceflight such as a mission to Mars and has been noted by the National Aeronautics and Space Administration (NASA) as a high risk based on its likelihood to occur and its severity to human health and mission performance. While it is a large barrier to future spaceflight, the underlying etiology of SANS is not well understood. Current ophthalmic imaging onboard the International Space Station (ISS) has provided further insights into SANS. However, the spaceflight environment presents with unique challenges and limitations to further understand this microgravity-induced phenomenon. The advent of artificial intelligence (AI) has revolutionized the field of imaging in ophthalmology, particularly in detection and monitoring. In this manuscript, we describe the current hypothesized pathophysiology of SANS and the medical diagnostic limitations during spaceflight to further understand its pathogenesis. We then introduce and describe various AI frameworks that can be applied to ophthalmic imaging onboard the ISS to further understand SANS including supervised/unsupervised learning, generative adversarial networks, and transfer learning. We conclude by describing current research in this area to further understand SANS with the goal of enabling deeper insights into SANS and safer spaceflight for future missions.

Extended reality quantification of pupil reactivity as a non-invasive assessment for the pathogenesis of spaceflight associated neuro-ocular syndrome: A technology validation study for astronaut health.

The National Aeronautics and Space Administration (NASA) has rigorously documented a group of neuro-ophthalmic findings in astronauts during and after long-duration spaceflight known as spaceflight associated neuro-ocular syndrome (SANS). For astronaut safety and mission effectiveness, understanding SANS and countermeasure development are of utmost importance. Although the pathogenesis of SANS is not well defined, a leading hypothesis is that SANS might relate to a sub-clinical increased intracranial pressure (ICP) from cephalad fluid shifts in microgravity. However, no direct ICP measurements are available during spaceflight. To further understand the role of ICP in SANS, pupillometry can serve as a promising non-invasive biomarker for spaceflight environment as ICP is correlated with the pupil variables under illumination. Extended reality (XR) can help to address certain limitations in current methods for efficient pupil testing during spaceflight. We designed a protocol to quantify parameters of pupil reactivity in XR with an equivalent time duration of illumination on each eye compared to pre-existing, non-XR methods. Throughout the assessment, the pupil diameter data was collected using HTC Vive Pro-VR headset, thanks to its eye-tracking capabilities. Finally, the data was used to compute several pupil variables. We applied our methods to 36 control subjects. Pupil variables such as maximum and minimum pupil size, constriction amplitude, average constriction amplitude, maximum constriction velocity, latency and dilation velocity were computed for each control data. We compared our methods of calculation of pupil variables with the non-XR methods existing in the literature. Distributions of the pupil variables such as latency, constriction amplitude, and velocity of 36 control data displayed near-identical results from the non-XR literature for normal subjects. We propose a new method to evaluate pupil reactivity with XR technology to further understand ICP's role in SANS and provide further insight into SANS countermeasure development for future spaceflight.

Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence.

The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of medicine. Although highly effective, the rapid expansion of this technology has created some anticipated and unanticipated bioethical considerations. With these powerful applications, there is a necessity for framework regulations to ensure equitable and safe deployment of technology. Generative Adversarial Networks (GANs) are emerging ML techniques that have immense applications in medical imaging due to their ability to produce synthetic medical images and aid in medical AI training. Producing accurate synthetic images with GANs can address current limitations in AI development for medical imaging and overcome current dataset type and size constraints. Offsetting these constraints can dramatically improve the development and implementation of AI medical imaging and restructure the practice of medicine. As observed with its other AI predecessors, considerations must be taken into place to help regulate its development for clinical use. In this paper, we discuss the legal, ethical, and technical challenges for future safe integration of this technology in the healthcare sector.

Generative Pre-Trained Transformers (GPT) and Space Health: A Potential Frontier in Astronaut Health During Exploration Missions.

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

GPT-4 and Ophthalmology Operative Notes.

Operative notes are an essential piece of documentation made by an ophthalmic team following ocular or ophthalmic surgery. While the advanced language processing capabilities of GPT-4 can be utilized to enhance the comprehension of natural language in healthcare applications, the utilization of GPT-4 in writing operative notes is not explicitly referenced in the current literature. In this paper, we describe the potential applications for GPT-4 to write ophthalmic operative notes. It is imperative to acknowledge that despite the impressive capabilities exhibited by GPT-4, it remains constrained by certain limitations, and therefore, ought to be employed in tandem with human expertise and subjected to critical evaluation.

The Case for Expanding Visual Assessments During Spaceflight.

Spaceflight associated neuro-ocular syndrome (SANS) is one of the potential barriers to human long-duration spaceflight (LDSF), including a manned mission to Mars. While a large barrier, the pathophysiology of SANS is not well understood, and functional and structural findings from SANS continue to be further characterized. Currently on the International Space Station (ISS), scheduled visual assessments are static visual acuity, Amsler grid, and a self-reported survey. Additional visual assessments may help the understanding of this neuro-ophthalmic phenomenon, as well as the effects of spaceflight of overall ocular health. In this paper, a case is made for expanding scheduled visual assessments to include dynamic visual, contrast sensitivity (CS), visual field testing, and virtual reality-based metamorphopsia assessment during spaceflight. These further assessments may play a key role in helping to determine the structural and functional changes associated with SANS, which are crucial to maintain astronaut vision during LDSF, as well as for developing countermeasures. Finally, a brief discussion is provided about current challenges to expanding visual testing during spaceflight and potential solutions to these barriers, specifically head-mounted visual assessment technology.