Artificial Intelligence for Assessing Maximum Oxygen Consumption: Scoping Review

Maximum oxygen consumption (VO2max) is the ability to absorb, transport, and use oxygen in the body to produce useful energy for muscle activation in a unit of time. For years, devices have been developed to estimate physical performance, including VO2max. In view of the above, it can be considered that the use of artificial intelligence can facilitate interpretation and even generate estimates based on available data. In this regard, this study aims to review the use of artificial intelligence in the assessment of maximum oxygen consumption. A scoping review was conducted in accordance with the following stages: (i) identification of the research question: What would be the use of AI to predict VO2? (ii) identification of relevant studies: searching academic databases and AI search engines; (iii) selection of studies: the PRISMA ScR protocol was applied, selecting 50 studies; (iv) graphing the data in the results (v): finding studies published since 2009 with a higher publication rate in countries in the Americas and Asia; it is concluded that the use of deep learning fed with validated algorithms allows for a more accurate estimation of VO2max and that its evaluation requires the use of explainable AI training, starting with the linear regressions available in the literature and continuing with decision trees, to predict performance and offer a classification of it.