Artificial intelligence in electrocardiogram signals for sudden cardiac death prediction: a systematic review and meta-analysis

Comprehensive Summary

The aim of this study was to evaluate the potential use of AI in detecting sudden cardiac death from an electrocardiogram (ECG). Previous research has shown the development of AI models that can successfully detect arrhythmias and predict sudden cardiac death by analyzing the ECG. However, there is a lot of concern around using these AI models clinically as healthcare workers are concerned about the generalization of the AI model, and the ability of them to interpret accurately for a wide range of people. The authors of this study examined this previous research to evaluate AI’s accuracy and efficiency in this area. The authors were focused on observing the AI model’s ability to accurately report heart rate variance, ECG signal segmentation, and ECG lead interpretation. A total of 27 studies were used for the final analysis.For heart rate variation, there was between study variation for the specificity of the analysis; hypothesized reasons for this were different types of AI programs used, different years, and different regions. There was a lot of variability in AI performance in regards to analyzing heart rate variability. The potential factors leading to this variability are AI model type, year, region, validation method. The same variation was present in the AI models’ in detecting ECG signal segmentation, and ECG lead interpretation. There were also only a few studies that had applied a standardized quality assessment. The results from this study show the the AI models created need to be better standardized in order to be applicable to a larger population and work accurately in clinical practice.

Outcomes and Implications

There have been many AI models developed to detect sudden cardiac death based on ECG results. The goal of this study specifically was to see if these models can be used in clinical use. The results showed that there was a lot of variation in the results from the difference studies indicating that more studies and validation needs to be done on these models before being used for a diverse population. However, it has a lot of potential to be useful in clinical practice as it will reduce the amount of time a healthcare worker needs to spend analyzing the ECG.