Development of Machine-Learning–Based Algorithms for Remote Respiratory Disease Monitoring and Assessment

Determining the correct breathing pattern relates to the patient's work of breathing, a marker of clinical deterioration of key importance for deployed soldiers. Early and accurate detection of the patient's respiratory condition can potentially improve outcomes in resource-constrained settings, particularly field hospitals and injured soldiers in transit from the point of injury. This work supports the potential of AI-driven respiratory monitoring to enhance remote respiratory monitoring, allowing for timely intervention and better long-term care.