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Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests

Zignoli, A, Fornasiero, A, Rota, P, Muollo, V, Peyre-Tartaruga, LA, Low, DA, Fontana, FY, Besson, D, Puhringer, M, Ring-Dimitriou, S and Mourot, L (2021) Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests. European Journal of Sport Science. ISSN 1746-1391

Oxynetacollectiveintelligencethatdetectsventilatorythresholdsincardiopulmonaryexercisetests (1).pdf - Accepted Version

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The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web-application was used to crowd-source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web-application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO2/min (11.1%, r = 0.97) and 144 (149) mlO2/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in European Journal of Sport Science on 31/01/2021, available online: http://www.tandfonline.com/10.1080/17461391.2020.1866081
Uncontrolled Keywords: 0913 Mechanical Engineering, 1106 Human Movement and Sports Sciences
Subjects: Q Science > QM Human anatomy
Q Science > QP Physiology
R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Sport & Exercise Sciences
Publisher: Taylor & Francis
Related URLs:
Date Deposited: 01 Mar 2021 10:33
Last Modified: 31 Jan 2022 00:50
DOI or ID number: 10.1080/17461391.2020.1866081
URI: https://researchonline.ljmu.ac.uk/id/eprint/14531
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