Artificial Intelligence for In-Flight Detection of Space-Related Ocular Trauma: Bridging Diagnostic Gaps in Microgravity

Ocular trauma represents a threat to crew safety and mission performance in space. Microgravity, confined environments, and exposure to particulate matter, chemicals, and mechanical hazards place astronauts at risk for corneal abrasions, open-globe injuries, chemical burns, lens dislocation, retinal detachment, orbital fractures, and barotrauma. Diagnostic capabilities during spaceflight remain limited by resources, lack of specialist expertise, and communication delays with Earth. Artificial intelligence, particularly convolutional neural networks and multimodal models, may help address these gaps through image interpretation, risk stratification, and longitudinal monitoring. Convolutional neural networks can extract hierarchical features from imaging data to identify subtle structural abnormalities, while multimodal models integrate imaging with clinical and environmental parameters to generate more comprehensive assessments. Terrestrial ophthalmology studies demonstrate the potential of these approaches across optical coherence tomography, ultrasound, fundus photography, and anterior-segment imaging. This review examines how these capabilities can be matched to ocular injuries during spaceflight, compares the suitability of different approaches across injury types, and identifies pathways toward autonomous care. Particular emphasis is given to spaceflight-related imaging and physiologic changes, constrained onboard hardware, and integration into workflows that support non-expert crew members. Collectively, these applications may expand diagnostic capabilities and enable earlier, more informed management during long-duration missions.