Artificial intelligence and echocardiography. Issue 4 (December 2018)
- Record Type:
- Journal Article
- Title:
- Artificial intelligence and echocardiography. Issue 4 (December 2018)
- Main Title:
- Artificial intelligence and echocardiography
- Authors:
- Alsharqi, M
Woodward, W J
Mumith, J A
Markham, D C
Upton, R
Leeson, P - Abstract:
- Abstract : Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and haveAbstract : Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome. … (more)
- Is Part Of:
- Echo research and practice. Volume 5:Issue 4(2018)
- Journal:
- Echo research and practice
- Issue:
- Volume 5:Issue 4(2018)
- Issue Display:
- Volume 5, Issue 4 (2018)
- Year:
- 2018
- Volume:
- 5
- Issue:
- 4
- Issue Sort Value:
- 2018-0005-0004-0000
- Page Start:
- R115
- Page End:
- R125
- Publication Date:
- 2018-12
- Subjects:
- echocardiography -- artificial intelligence -- machine learning
Echocardiography -- Periodicals
Heart -- Imaging -- Periodicals
616.1207543 - Journal URLs:
- http://www.echorespract.com/ ↗
https://echo.biomedcentral.com/ ↗ - DOI:
- 10.1530/ERP-18-0056 ↗
- Languages:
- English
- ISSNs:
- 2055-0456
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 15448.xml