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AI-ECG Algorithms to Identify Cardiac Disease in Youth Athletes

Overview

About this study

 

The purpose of this study is to determine if a deep-learning artificial intelligence convolutional neural network that was trained from raw electrocardiogram (ECG) signals to identify life-threatening cardiac diseases will have favorable performance for the detection of HCM and differentiation from athletic heart adaptation in young athletes.

Participation eligibility

Participant eligibility includes age, gender, type and stage of disease, and previous treatments or health concerns. Guidelines differ from study to study, and identify who can or cannot participate. There is no guarantee that every individual who qualifies and wants to participate in a trial will be enrolled. Contact the study team to discuss study eligibility and potential participation.

Inclusion Criteria:

  • Any middle or high-school student-athlete self-reporting participation in sports is eligible.
  • Any type of sport will be eligible for inclusion.
  • All athletes with or without a known diagnosis of HCM diagnoses by standard ESC and ACC/AHA criteria are eligible for inclusion.
  • There will be no restrictions for inclusion in terms of patient sex, age, race, ethnicity, geographical region of origin, or type of sport.

Exclusion Criteria: 

  • < 12 years of age. 

Eligibility last updated 9/12/22. Questions regarding updates should be directed to the study team contact.

Participating Mayo Clinic locations

Study statuses change often. Please contact the study team for the most up-to-date information regarding possible participation.

Mayo Clinic Location Status Contact

Rochester, Minn.

Mayo Clinic principal investigator

Konstantinos Siontis, M.D.

Closed for enrollment

Contact information:

Jennifer Dugan

(507) 538-1125

Dugan.Jennifer@mayo.edu

More information

Publications

Publications are currently not available

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CLS-20583239

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