Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05872945 |
| Other study ID # |
1573N19 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
July 1, 2019 |
| Est. completion date |
July 30, 2024 |
Study information
| Verified date |
May 2023 |
| Source |
RCD Mallorca SAD |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
LIST OF PLANNED ORIGINAL PUBLICATIONS
1. T wave inversion detection with machine learning to prevent sudden death in professional
football players.
2. Machine learning applied to biological parameters for control and advisory in
professional football players (Machine learning applied to biological parameters for
control and advisory in professional football players.)
3. Machine learning applied to sport geolocation systems for injury prevention in
professional football players.
Description:
1. Introduction The approach of this project arises from the concern to use intelligence
systems artificial intelligence and machine learning in professional sports as assistance for
the optimization of health and performance in professional soccer players. In professional
sport, increasing physical, biological and physiological efforts are required and we need
help tools.
In this regard, the proposal of several publications within the project has been raised:
1. Detection of T-wave inversion with machine learning to prevent sudden death in
professional soccer players.
Players undergo various pre-competitive screening tests to assess their state of health,
specifically one of them is a resting 12-lead electrocardiogram. Based on the waveform
findings in this complementary test, the risk of a professional athlete and the need for
more complementary tests can be classified (Drezner et al., 2017). Our proposal is to
reanalyze these tests and subject them to a machine learning mathematical model that is
capable of detecting T wave inversions in said leads and presenting the results and
recommendations in accordance with international criteria for electrocardiographic study
in athletes.
2. Machine learning applied to biological parameters for control and advice in professional
soccer players.
During the season, routine analyzes are carried out to control biochemical parameters
related to health and performance that fluctuate or change throughout the season:
vitamin D, vitamin B12, vitamin B9, ferritin, etc. (Galan et al. ., 2012). Said data
will be subjected to a machine learning procedure that can notify us of alterations in
the habitual pattern of the players and that can cause alterations in performance, even
generating pathologies.
3. Machine learning applied to sports geolocation systems for the prevention of injuries in
professional soccer players.
The data obtained during training sessions and matches regarding physical data such as
duration, distance, distance at different speeds, training density, etc. Which are provided
by sports geolocation systems, are of great importance when studying the effort and
performance profile of each player. Obtaining the player's performance profile standardized
according to the training day, we can detect adverse situations such as: over-training or
lack of physical condition. Warning and alarm systems aimed at injury prevention can be
designed. (Rossi, Pappalardo, Marcello, Javier, & May, 2017).
2. Description The studies will be implemented by implementing artificial intelligence and
machine learning systems on the physical, biological and physiological data collected during
the routine sports and health activity of the professional football players in the 2019-20
and 2020-21, 2021-22, 2022-23 y 2023-24 seasons.
2.1 General Objectives
- Evaluate the installation of artificial intelligence systems such as automatic learning
to obtain models and results in the interpretation of physical, biomedical and
physiological parameters of the players.
- Develop advisory/advertising systems in the area of health and performance based on
profiles.
3. Practical application The project has great potential for practical applicability and
could generate a paradigm shift, since it is based on the generation of mathematical
and/or programming models that will help in health controls and sports load controls
that are applied to professional soccer players. A notable aspect is the possible
improvement in the calculation of the probabilistic weights of the risk factors on
health and performance.
