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.

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.

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.

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.

Precisional modulation of translaminar pressure gradients for ophthalmic diseases.

The translaminar pressure gradient (TLPG) refers to two forces at the lamina cribosa of the optic nerve: the anteriorly acting intracranial pressure (ICP), and posteriorly-acting intraocular pressure (IOP). It has been proposed that controlling the translaminar pressure gradient at regular intervals may preserve the optic nerve and slow the course of glaucoma. The precisional modulation of this TLPG is a recently introduced concept that may play a role in the treatment of ophthalmic diseases such as glaucoma. In this manuscript, we review the applications of pressurized goggles on ophthalmic diseases. We also elaborate upon current investigations in modulation of the TLPG including goggles and the multi-pressure dial goggle. We discuss future research directions for ophthalmic diseases including spaceflight associated neuro-ocular syndrome (SANS), a large physiological barrier to future long-duration spaceflight.

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.

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.

Test-Retest Reliability of Virtual Reality Devices in Quantifying for Relative Afferent Pupillary Defect.

Background: The swinging flashlight test (SFT) is one of the most prominent clinical tests for detecting the relative afferent pupillary defect (RAPD). A positive RAPD localizes the lesion to the affected afferent pupil pathway and is a critical part of any ophthalmic exam. Testing for an RAPD, however, can be challenging (especially when small), and there is significant intrarater and interrater variability. Methods: Prior studies have shown that the pupillometer can improve the detection and measurement of RAPD. In our previous research, we have demonstrated an automatic SFT by utilizing virtual reality (VR), named VR-SFT. We applied our methods to two different brands of VR headsets and achieved comparable results by using a metric, called RAPD score, for differentiating between patients with and without (control) RAPD. We also performed a second VR-SFT on 27 control participants to compare their scores with their first assessments and measure test-retest reliability of VR-SFT. Results: Even in the absence of any RAPD positive data, the intraclass correlation coefficient produces results between 0.44 and 0.83 that are considered of good to moderate reliability. The same results are echoed by the Bland-Altman plots, indicating low bias and high accuracy. The mean of the differences of measurements from test-retest ranges from 0.02 to 0.07 for different protocols and different devices. Conclusions: As variability among various VR devices is an important factor that clinicians should consider, we discuss the test-retest reliability of VR-SFT and the variability among various assessments and between two devices. Translational Relevance: Our study demonstrates the critical necessity of establishing test-retest reliability measures when bridging virtual reality technology into the clinical setting for relevant afferent pupillary defect.

Dynamic visual acuity as a biometric for astronaut performance and safety.

Astronauts are exposed to an austere and constantly changing environment during space travel. To respond to these rapid environmental changes, high levels of dynamic visual acuity (DVA) are required. DVA is the ability to visualize objects that are in motion, or with head movement and has previously been shown to decrease significantly following spaceflight. Decreased DVA can potentially impact astronauts while performing mission critical tasks and drive space motion sickness. In this paper, we suggest that DVA assessment during spaceflight and during G-transitions should be considered to help further understand the vestibulo-ocular impacts of interplanetary spaceflight and ensure mission performance including potential manned missions to Mars.

Extended Reality Applications for Space Health.

INTRODUCTION: Spaceflight has detrimental effects on human health, imposing significant and unique risks to crewmembers due to physiological adaptations, exposure to physical and psychological stressors, and limited capabilities to provide medical care. Previous research has proposed and evaluated several strategies to support and mitigate the risks related to astronauts' health and medical exploration capabilities. Among these, extended reality (XR) technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR) have increasingly been adopted for training, real-time clinical, and operational support in both terrestrial and aerospace settings, and only a few studies have reported research results on the applications of XR technologies for improving space health. This study aims to systematically review the scientific literature that has explored the application of XR technologies in the space health field. We also discuss the methodological and design characteristics of the existing studies in this realm, informing future research and development efforts on applying XR technologies to improve space health and enhance crew safety and performance.Ebnali M, Paladugu P, Miccile C, Park SH, Burian B, Yule S, Dias RD. Extended reality applications for space health. Aerosp Med Hum Perform. 2023; 94(3):122-130.

Transfer learning as an AI-based solution to address limited datasets in space medicine.

The advent of artificial intelligence (AI) has a promising role in the future long-duration spaceflight missions. Traditional AI algorithms rely on training and testing data from the same domain. However, astronaut medical data is naturally limited to a small sample size and often difficult to collect, leading to extremely limited datasets. This significantly limits the ability of traditional machine learning methodologies. Transfer learning is a potential solution to overcome this dataset size limitation and can help improve training time and performance of a neural networks. We discuss the unique challenges of space medicine in producing datasets and transfer learning as an emerging technique to address these issues.

Telemedicine Hip and Knee Arthroplasty Experience During COVID-19.

BACKGROUND: Although telemedicine visits were essential and adopted by providers and patients alike, few studies have been conducted evaluating orthopedic patient perception of the care delivered during these visits. To our knowledge, no study has evaluated specific factors that affected patient satisfaction with telemedicine and the receptiveness to continue virtual visits post COVID-19 in total joint arthroplasty (TJA) patients. Thus, the purposes of our study are to determine the following: (1) patient satisfaction with using TJA telemedicine services, (2) whether patient characteristics might be associated with satisfaction, and (3) whether virtual clinic visits may be used post-COVID-19. METHODS: A prospective, cross-sectional survey study was completed by 126 TJA patients who participated in telemedicine visits with TJA surgeons from May 1, 2020 to August 31, 2020. The survey consisted of questions regarding demographics, satisfaction, and telemedicine experiences. RESULTS: One hundred one (80.2%) patients were satisfied with their telemedicine visit, with patients <80 years old (P = .008) and those with a longer commute time (P = .01) being more satisfied P = .01. There was a significant preference for in-person visits when meeting arthroplasty surgeons for the first time (P < .001), but patients were equally amenable to follow-up telemedicine visits once there was an established relationship with the surgeon. CONCLUSION: Younger patients, patients with longer commute distances, and patients who had established relationships with their provider expressed higher satisfaction with telemedicine arthroplasty visits. Although >80% of patients were satisfied with their telemedicine visit, an established patient-provider relationship may be integral to the success of an arthroplasty telemedicine practice.