Artificial intelligence and anaerobic threshold: the winner is human physiology

Piergiuseppe Agostoni^{1

2}, Gaia Cattadori^{1

3}, Elisabetta Salvioni², Susanna Sciomer⁴

Affiliations

¹ Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Via Parea, 4, Milan 20138, Italy.
² Centro Cardiologico Monzino, IRCCS, Via Parea, 4, Milano 20138, Italy.
³ Unità Operativa Cardiologia Riabilitativa, IRCCS Multimedica, Milano, Italy.
⁴ Dipartimento di Scienze Cliniche, Internistiche, Anestesiologiche e Cardiovascolari, 'Sapienza', Rome University, Rome, Italy.

Editorial

Piergiuseppe Agostoni et al. Eur J Prev Cardiol. 2024.

. 2024 Mar 4;31(4):445-447.

doi: 10.1093/eurjpc/zwae015.

Piergiuseppe Agostoni^{1

2}, Gaia Cattadori^{1

3}, Elisabetta Salvioni², Susanna Sciomer⁴

¹ Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Via Parea, 4, Milan 20138, Italy.
² Centro Cardiologico Monzino, IRCCS, Via Parea, 4, Milano 20138, Italy.
³ Unità Operativa Cardiologia Riabilitativa, IRCCS Multimedica, Milano, Italy.
⁴ Dipartimento di Scienze Cliniche, Internistiche, Anestesiologiche e Cardiovascolari, 'Sapienza', Rome University, Rome, Italy.

No abstract available

Conflict of interest statement

Conflict of interest: none declared.

Development of deep-learning models for real-time anaerobic threshold and peak VO2 prediction during cardiopulmonary exercise testing.
Watanabe T, Tohyama T, Ikeda M, Fujino T, Hashimoto T, Matsushima S, Kishimoto J, Todaka K, Kinugawa S, Tsutsui H, Ide T. Watanabe T, et al. Eur J Prev Cardiol. 2024 Mar 4;31(4):448-457. doi: 10.1093/eurjpc/zwad375. Eur J Prev Cardiol. 2024. PMID: 38078901