Article Text

Artificial intelligence for the echocardiographic assessment of valvular heart disease
  1. Rashmi Nedadur1,2,
  2. Bo Wang2,3,4,5,
  3. Wendy Tsang5,6
  1. 1Division of Cardiac Surgery, University of Toronto, Toronto, Ontario, Canada
  2. 2Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
  3. 3Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
  4. 4Vector Institute of Artificial Intelligence, University of Toronto, Toronto, Ontario, Canada
  5. 5Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
  6. 6Division of Cardiology, University of Toronto, Toronto, Ontario, Canada
  1. Correspondence to Dr Wendy Tsang, 200 Elizabeth Street, University of Toronto, Toronto, Ontario, Canada; wendy.tsang{at}uhn.ca

Abstract

Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.

  • echocardiography
  • heart valve diseases
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

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