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

Abstract

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.

Keywords:

• Automatic methods
• artificial intelligence
• machine learning
• deep learning

Acknowledgements

We are thankful to Amedeo Setti (ProM Facility, Trentino Sviluppo) for developing the web-based applications and for managing the data collection and storage on the cluster of servers. We are thankful to the CARITRO Foundation for partially supporting this project and for establishing the Deep Learning Lab at the ProM Facility (Trentino Sviluppo). Appreciation is expressed to Filippo Degasperi for supporting the Oxynet web-application development within the ‘Restitution Project’.

A.Z. and A.F. conceived of the original idea and drafted the manuscript. A.Z. developed the theory and performed the computations. P.R. assisted A.Z. in the creation of the models and contributed to the interpretation of the results. A.F., V.M., L.A.P.T., D.A.L., F.Y.F., D.B., M.P., S.R.D. and L.M. supervised and carried out the experiments, contributed to sample preparation and results interpretation. All authors discussed the results and contributed to the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed at https://doi.org/10.1080/17461391.2020.1866081

Additional information

Funding

This work was supported by Fondazione Cassa Di Risparmio Di Trento E Rovereto: [Grant Number 2017.0379].