1970
October 1, 2022
Freenome
October 1, 2022Article of the Month – October 2022
Machine Learning in Electrocardiography and Echocardiography: Technical Advances in Clinical Cardiology
Amanda Chang, Linda M. Cadaret & Kan Liu
Purpose of Review
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.
Recent Findings
ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions. Echocardiography has been used to recognize image views, quantify measurements, and identify pathologic patterns.
The synergistic application of ML in electrocardiography and echocardiography has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms disease diagnoses and outcome prediction with ECG and echocardiography compared to trained healthcare professionals.
Outcomes and Measures...
ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions. Echocardiography has been used to recognize image views, quantify measurements, and identify pathologic patterns.
The synergistic application of ML in electrocardiography and echocardiography has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms disease diagnoses and outcome prediction with ECG and echocardiography compared to trained healthcare professionals.
Conclusions
Embedded in traditional ECG and modern echocardiography workflows, ML has already made data acquisition and processing more efficient and standardized. Much effort has been made to integrate ECG and echocardiography interpretation into an automated ML framework, improve the quality of disease diagnosis and risk stratification, and support clinical decision-making. Improved reading effectiveness and efficiency will potentially contribute to addressing the supply-demand mismatch in current cardiovascular healthcare systems.
Summary
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. Automating data acquisition, processing, and interpretation helps streamline the workflows of ECG and echocardiography in contemporary cardiology practice.
Relevance to Healthcare Field
Electrocardiograms (ECGs) and echocardiography are two of the most used diagnostic tools for cardiovascular disorders. Machine learning performs significantly better in disease diagnoses and outcome prediction with ECG and echocardiography in comparison with trained healthcare professionals. Machine learning algorithms in electrocardiography and echocardiography allow for improvements in technical quality assurance, arrhythmia identification, prognostic predictions, and image acquisition, processing, and interpretation, respectively. Novel diagnostic and prognostic prediction models have been developed through machine learning, increasing its role in medical research and clinical practice.