AI Revolutionizes Ocular Imaging to Tackle SANS in Space Exploration

By Ashutosh RoyReviewed by Susha Cheriyedath, M.Sc.Aug 1 2023

Space exploration presents unprecedented challenges, especially when it comes to monitoring the health of astronauts during long-duration space missions. One significant concern that has emerged is Spaceflight Associated Neuro-Ocular Syndrome (SANS), a condition affecting the eyes of astronauts due to prolonged exposure to the space environment. To tackle this issue, researchers are turning to cutting-edge technologies like Artificial Intelligence (AI) to enhance ocular imaging and improve diagnostic capabilities onboard the International Space Station (ISS). A review published in the journal Brain Sciences explores how AI-based tools such as Generative Adversarial Networks (GANs) and Supervised Learning revolutionize ocular imaging in space and provides new insights into SANS.

Understanding Spaceflight Associated Neuro-Ocular Syndrome (SANS)

SANS is a complex and enigmatic condition characterized by specific neuro-ophthalmic changes, including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. It poses a significant risk to astronauts' health and mission performance during long-duration spaceflight (LDSF) missions. The underlying pathophysiology of SANS is not fully understood, and it has become a crucial barrier to future space exploration. Proposed hypotheses for SANS development include cephalad fluid shifts, ocular glymphatic system dysfunction, mechanical brain shifting, genetics, and vitamin status. While these theories offer potential explanations, they require further investigation and validation.

Imaging Techniques Aboard the ISS

Before delving into AI applications in imaging, it is essential to understand the imaging techniques used onboard the ISS to diagnose and monitor ocular abnormalities in astronauts.

Fundus Photography: This non-invasive modality records high-resolution ocular images, including the retina, optic nerve, and blood vessels, aiding in the detection and monitoring of various ocular abnormalities. By analyzing these images, clinicians can identify early signs of SANS and assess its progression.

Optical Coherence Tomography (OCT): Using light waves, OCT produces cross-sectional images of the retina, providing valuable information on thickness changes in retinal layers and nerve fiber layers. OCT is particularly useful for detecting subtle structural changes associated with SANS.

Orbital Ultrasound: This non-invasive technique employs sound waves to visualize the optic nerve and surrounding tissues, helping diagnose and monitor conditions such as optic nerve swelling and retinal detachment.

AI's Role in Ocular Imaging: Supervised Learning

Supervised learning is a fundamental AI technique used for tasks like classification and segmentation. In ocular imaging, AI algorithms can process vast amounts of medical data quickly and accurately, detecting subtle changes indicative of SANS. For example, deep learning algorithms have shown remarkable accuracy in detecting diabetic retinopathy, and FDA-approved AI devices achieve diagnostic accuracy comparable to ophthalmologists.

In the context of SANS, supervised learning can help in several ways:

• Early Detection: By analyzing historical medical data from astronauts, AI models can recognize patterns associated with the early stages of SANS, allowing for timely intervention and preventive measures.
• Automated Diagnosis: AI-based systems can automate the analysis of medical images taken aboard the ISS, reducing the workload on astronauts and improving diagnostic efficiency.
• Risk Assessment: AI can assess individual astronauts' risk of developing SANS based on their medical history, genetics, and other relevant factors, allowing for personalized care during space missions.

AI's Role in Ocular Imaging: Unsupervised Learning

Unsupervised learning is another AI approach ideal for tasks requiring identifying hidden patterns in data without explicit supervision. It has been employed in medical imaging for image reconstruction and denoising. In the context of spaceflight, unsupervised learning can analyze fundus and OCT images without explicit labeling, providing valuable insights into SANS.

The advantages of unsupervised learning in SANS research include:

• Discovering Novel Features: Unsupervised learning can identify subtle or previously unknown patterns associated with SANS that may not be apparent in labeled datasets.
• Data Augmentation: AI models can use unsupervised learning to augment labeled datasets, thereby enhancing the performance of supervised learning models.

Future Prospects and Conclusions

The combination of AI, particularly GANs and supervised learning, holds great promise in advancing the understanding of SANS and improving the diagnosis and management of ocular abnormalities during space missions. By leveraging AI's capabilities, it is possible to detect early signs of SANS, develop personalized preventive measures, and ensure astronauts' well-being during extended space missions. Moreover, the data collected and analyzed through AI can contribute to a deeper understanding of the underlying mechanisms of SANS, eventually leading to the development of more effective countermeasures.

As AI continues to evolve, it holds the promise of even more significant breakthroughs in space exploration and medical diagnostics. AI-based techniques can deepen our understanding of SANS and develop effective countermeasures for safer and successful future missions to distant celestial bodies. With continued research and collaboration, AI technology will play a vital role in ensuring the success of human spaceflight missions beyond Earth's orbit.