AI-enabled ECG analysis using machine learning and deep learning shows promise for disease diagnosis, but significant limitations currently prevent its implementation in clinical practice.
Does AI-enabled ECG analysis improve disease diagnosis and health monitoring?
While AI-enabled ECG analysis shows significant potential for broad disease diagnosis, critical methodological and practical limitations must be resolved before clinical implementation.
Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers are using deep learning (ML) and deep learning (DL) with ECG data to detect different types of cardiac issues as well as other health problems such as respiration rate, sleep apnea, and blood pressure, etc. This study presents an extensive literature review based on research performed in the last few years where ML and DL have been applied with ECG data for many diagnoses. However, the review found that, in published work, the results showed promise. However, some significant limitations kept that technique from implementation in reality and being used for medical decisions; examples of such limitations are imbalanced and the absence of standardized dataset for evaluation, lack of interpretability of the model, inconsistency of performance while using a new dataset, security, and privacy of health data and lack of collaboration with physicians, etc. AI using ECG data accompanied by modern wearable biosensor technologies has the potential to allow for health monitoring and early diagnosis within reach of larger populations. However, researchers should focus on resolving the limitations.
Mamun et al. (Fri,) conducted a review in Cardiac issues and other health problems. Machine learning and deep learning with ECG data was evaluated. AI-enabled ECG analysis using machine learning and deep learning shows promise for disease diagnosis, but significant limitations currently prevent its implementation in clinical practice.