A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes
<b>Background/Objectives</b>: This study aimed to evaluate an artificial intelligence (AI)-enhanced electrocardiogram (ECG) screening protocol for improved accuracy, efficiency, and risk stratification across six sports: handball, football, athletics, weightlifting, judo, and karate. <...
Saved in:
| Main Authors: | Luiza Camelia Nechita, Dana Tutunaru, Aurel Nechita, Andreea Elena Voipan, Daniel Voipan, Anca Mirela Ionescu, Teodora Simina Drăgoiu, Carmina Liana Musat |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/4/477 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring
by: Luiza Camelia Nechita, et al.
Published: (2025-03-01) -
The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
by: Luiza Camelia Nechita, et al.
Published: (2025-03-01) -
ECG Screening in Athletes: A Systematic Review of Sport, Age, and Gender Variations
by: Adela Caramoci, et al.
Published: (2025-05-01) -
Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods
by: Carmina Liana Musat, et al.
Published: (2024-11-01) -
Norwegian Endurance Athlete ECG Database
by: Bjorn-Jostein Singstad
Published: (2022-01-01)