"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.

National Football League Game Officials Self-Rating of Knowledge in Neuro-Ophthalmic Principles and Practice: A Pilot Program to Improve Precision and Accuracy of Game Official Calls.

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.

Ethical Considerations of Neuralink and Brain-Computer Interfaces.

Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.

Concerns with OpenAI's Sora in Medicine.

Open AI's Sora represents a ground-breaking innovation in AI that can generate lifelike and imaginative visual scenes based on text prompts. However, Sora has also produced some new concerns surrounding artificial video generation in medicine. While Sora is highly promising to enhance patient education, facilitate remote consultations and simulate surgical procedures, AI-generated videos also bring technical, legal, and ethical challenges. In this paper, we explore the clinical and ethical implications of Sora's AI-generated videos in the field of medicine.

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.

Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review.

BACKGROUND: Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY: In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION: As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.

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.

Ultrasound Biomicroscopy as a Novel, Potential Modality to Evaluate Anterior Segment Ophthalmic Structures during Spaceflight: An Analysis of Current Technology.

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.

Pigment epithelial detachment composition indices in central serous chorioretinopathy as a biomarker for disease activity: A computational methodology and 1 year outcomes.

PURPOSE: Investigation of pigment epithelial detachment (PED) characteristics in central serous chorioretinopathy (CSCR) is underrepresented in the literature. We present a novel computational approach to quantify PED composition indices (PEDCI) in CSCR and track changes over time. METHODS: 34 eyes with active CSCR were analyzed quarterly over a 1-year period. Cases were categorized into acute and chronic CSCR depending on a symptom duration of less than 3 months or more than 3 months respectively. PED, retinal and choroidal dimensions were manually measured, and interval changes were compared using repeated measures of variance ANOVA. PED composition analysis involved manual segmentation followed by automated sub segmentation of PED areas to identify serous, neovascular and fibrous tissues. PEDCI for each component was compared among cases of acute and chronic CSCR. RESULTS: CMT and NSD-h decreased by 65.2 µm (p = 0.01), and 86.5 µm (p < 0.01) respectively at 12 months. At baseline, 7/17 acute CSCR eyes and 8/15 chronic CSCR eyes had a concomitant PED; acute cases had both serous and neovascular components (PEDCI-S: 16.95%, PEDCI-N: 40.3%), whereas chronic cases only had a neovascular component (PEDCI-S: 0%, PEDCI-N: 30.5%). At 12-month follow-up, 6/7 of acute CSCR group and 6/8 chronic CSCR group had a concomitant PED; PEDCI-S was largest for acute CSCR (53.4%) and PEDCI-N was largest for chronic CSCR (46.7%). CONCLUSION: We identify a novel biomarker PEDCI to differentiate acute and chronic CSCR with higher PEDCI-S in acute CSCR, and higher PEDCI-N in chronic CSCR.