Artificial Intelligence Interpretation of Point-of-Care Lung Ultrasound in Microgravity.
Authors
Abstract
Long-duration space missions demand reliable, portable, and autonomous medical diagnostic tools. Lung ultrasound (LUS) is ideal for space exploration due to its safety and versatility, but interpreting LUS images requires training. Artificial intelligence (AI) can assist with image interpretation by detecting lung sliding, an ultrasound sign produced by the movement of lung and chest wall during breathing. The presence of lung sliding helps to rule out pneumothorax, or a collapsed lung, which is a condition that may arise from barotrauma or rapid changes in pressurization. LUS clips were acquired from two healthy volunteers during parabolic flight maneuvers simulating microgravity and lunar gravity, with +1-G clips used as controls. Clips were analyzed using an AI model to classify the presence of lung sliding and model performance and confidence were compared across gravity conditions. All clips were reviewed by an expert to establish whether lung sliding was present, which served as the reference standard for AI model evaluation. From 105 LUS clips, the model demonstrated an accuracy of 94%, with similar performance at +1 G (96%), lunar gravity (94%), and microgravity (92%). Prediction confidence varied by gravity, with median values of 93% at +1 G, 83% at lunar gravity, and 67% at microgravity. Confidence was significantly lower in microgravity compared with +1 G. These findings demonstrate that AI-assisted LUS can reliably detect lung sliding under reduced gravity, supporting its feasibility as an autonomous diagnostic support tool for spaceflight and highlighting the importance of further validation in microgravity environments. Côté M, Smith D, Orozco N, VanBerlo B, Huggard B, Arntfield R, Prager R. Artificial intelligence interpretation of point-of-care lung ultrasound in microgravity. Aerosp Med Hum Perform. 2026; 97(7):505-509.