Researchers in the WSU Elson S. Floyd College of Medicine\u2019s Department of Nutrition and Exercise Physiology are using artificial intelligence (AI) technology in clinical exercise testing. Their novel approach could open new doors in a research area that is traditionally difficult to measure.<\/p>\n\n\n\n
Identification of ventilatory constraint is a key objective in clinical exercise testing, and expiratory flow-limitation (EFL) is a well-known type of ventilatory constraint. However, EFL is difficult to measure. WSU researchers think deep machine learning, an application of AI, might provide improved results.<\/p>\n\n\n\n
\u201cThe current methods for assessment of ventilatory constraints and ventilatory limitations to exercise are often imprecise and nuanced,\u201d said Hans Haverkamp, PhD, associate professor of exercise physiology. \u201cWe built a convolutional neural network (an application of deep machine learning) to identify exercise breaths that are constrained in adults during high-intensity exercise.\u201d<\/p>\n\n\n\n
The results of Haverkamp\u2019s recent testing and research have been published in the journal Scientific Reports. His team\u2019s work demonstrated an accuracy of 90 percent at identifying exercise breaths that were constrained.<\/p>\n\n\n\n
\u201cBased on these initial and promising findings, this technology could become an effective tool for measuring exercise ventilatory limitation in many clinical populations,\u201d Haverkamp said. \u201cOur approach holds promise for significantly improving the accuracy and ease of identifying such constraints.\u201d<\/p>\n\n\n\n
Haverkamp\u2019s team is already conducting a follow-up project with a larger sample size and a wider age range.<\/p>\n\n\n