Artificial Intelligence Assistance in Point-of-Care Ultrasound Skill Retention for Novice Users in Space Medicine Scenarios.

Abstract

Introduction: As humanity progresses further into space, astronauts must be increasingly independent from mission control, especially in high-consequence medical scenarios. The high-utility and low-mass nature of point-of-care ultrasound (POCUS) makes this imaging modality ideal for spaceflight mission deployment. However, POCUS operator skill degrades over time, presenting an operational barrier to continuous, effective use. Further, formal medical education and POCUS-specific training are not requirements for astronaut candidates, potentially exacerbating skill degradation. Artificial intelligence (AI) assistance may mitigate skill decay, enabling long-term POCUS skill retention. To characterize the utility of this paradigm in space, we evaluated AI assistance effects on POCUS skill retention in subject pools with astronaut-mimicking educational demographics.

Methods: This prospective cohort study included 30 participants, evenly split into unassisted and AI-assisted cohorts. After undergoing 1 training session, participants collected 5 renal images, bladder images, and bladder volume estimations and completed usability and self-confidence surveys immediately, 2 wk, and 8 wk after initial training. Primary outcomes included target organ capture rates, image quality, bladder volume variability, perceived self-confidence, and perceived system usability.

Results: AI assistance minimized bladder volume variability aggregated over time (P=0.004) and 2 wk after training (P=0.009) and mitigated perceived system usability degradation with time (P=0.04). No trends were found in organ-capture abilities, image quality, or self-confidence.

Conclusion: POCUS AI decreased bladder volume variability and mitigated system usability decrement. We recommend increasing study duration or reducing the number of data collections in future study designs and the fieldwide adoption of objective ultrasound image-quality metrics.