Journal article
Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology
Current cardiology reports, Vol.22(12), pp.161-161
10/10/2020
DOI: 10.1007/s11886-020-01416-9
PMID: 33037949
Abstract
Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography.
ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.
Details
- Title: Subtitle
- Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology
- Creators
- Amanda Chang - University of IowaLinda M Cadaret - University of IowaKan Liu - Roy J. and Lucille A. Carver College of Medicine
- Resource Type
- Journal article
- Publication Details
- Current cardiology reports, Vol.22(12), pp.161-161
- DOI
- 10.1007/s11886-020-01416-9
- PMID
- 33037949
- ISSN
- 1523-3782
- eISSN
- 1534-3170
- Language
- English
- Date published
- 10/10/2020
- Academic Unit
- Radiology; Cardiovascular Medicine; Internal Medicine
- Record Identifier
- 9984318796202771
Metrics
4 Record Views