| 000 | 04028nlm1a2200565 4500 | ||
|---|---|---|---|
| 001 | 665305 | ||
| 005 | 20231030041958.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\36504 | ||
| 035 | _aRU\TPU\network\25352 | ||
| 090 | _a665305 | ||
| 100 | _a20210910a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aUS | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aOxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests _fA. Zignoli, A. Fornasiero, P. Rota [et al.] |
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| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 320 | _a[References 43 tit.] | ||
| 330 | _aThe 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. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tEuropean Journal of Sport Science | ||
| 463 |
_tVol. XX, iss. X _v[11 p.] _d2021 |
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| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aautomatic methods | |
| 610 | 1 | _aartificial intelligence | |
| 610 | 1 | _adeep learning | |
| 610 | 1 | _aавтоматические методы | |
| 610 | 1 | _aискусственный интеллект | |
| 610 | 1 | _aмашинное обучение | |
| 610 | 1 | _aглубокое обучение | |
| 610 | 1 | _aдыхание | |
| 610 | 1 | _aнагрузки | |
| 701 | 1 |
_aZignoli _bA. _gAndrea |
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| 701 | 1 |
_aFornasiero _bA. _gAlessandro |
|
| 701 | 1 |
_aRota _bP. |
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| 701 | 1 |
_aMuollo _bV. _gValentina |
|
| 701 | 1 |
_aPeyre-Tartaruga _bL. A. |
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| 701 | 1 |
_aLow _bD. A. _gDavid |
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| 701 | 1 |
_aFontana _bF. Y. _gFederico |
|
| 701 | 1 |
_aBesson _bD. |
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| 701 | 1 |
_aPuhringer _bM. _gMartin |
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| 701 | 1 |
_aRing-Dimitriou _bS. _gSusanne |
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| 701 | 1 |
_aMourot _bL. _cspecialist in the field of physical training and sports _cSenior Researcher of Tomsk Polytechnic University, Candidate of philological sciences _f1977- _gLaurent _2stltpush _3(RuTPU)RU\TPU\pers\41001 |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет (ТПУ) _bШкола базовой инженерной подготовки (ШБИП) _bОтделение физической культуры (ОФК) _h8034 _2stltpush _3(RuTPU)RU\TPU\col\23545 |
| 801 | 2 |
_aRU _b63413507 _c20220624 _gRCR |
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| 856 | 4 | _uhttps://doi.org/10.1080/17461391.2020.1866081 | |
| 942 | _cCF | ||