Experts correctly discriminated AI-simulated from authentic cardiopulmonary exercise test data in 44% of cases, indicating Oxynet is an accurate tool for identifying exercise thresholds.
Does the AI-driven tool Oxynet accurately identify gas exchange and ventilatory thresholds from CPET data in healthy individuals?
An AI-driven tool, Oxynet, can accurately and objectively identify exercise thresholds from CPET data in healthy individuals.
BACKGROUND: A cardiopulmonary exercise test (CPET) provides the estimated lactate threshold (θ METHODS: Evaluation included three phases: In phase I, 50 simulated ventilatory and gas exchange CPET files were generated, mixed with 50 authentic files, presented sequentially and in randomized order to three independent evaluators, and judged to be real or fake. In phase II, a new set of 50 files were generated, θ RESULTS: Experts correctly discriminated simulated from authentic data in 44% of cases (phase I). One-way ANOVA revealed no main effect of identified CONCLUSIONS: Oxynet can be used as an accurate, reliable, and objective tool to identify or aid in the identification of exercise thresholds from gas exchange and ventilatory CPET data in healthy individuals.
Keir et al. (Thu,) conducted a other in Healthy individuals (n=100). Oxynet (AI-driven analysis of CPET) vs. Human expert evaluation was evaluated on Correct discrimination of simulated from authentic data by experts. Experts correctly discriminated AI-simulated from authentic cardiopulmonary exercise test data in 44% of cases, indicating Oxynet is an accurate tool for identifying exercise thresholds.