Myocardial Ischemia: literature research about standard algorithms vs. CineECG approach

Myocardial ischemia remains one of the leading causes of morbidity and mortality worldwide. Early and accurate diagnosis is essential to prevent irreversible myocardial damage and improve patient outcomes. Electrocardiography (ECG) is widely used for detecting ischemic events, particularly through ST-segment analysis. However, traditional ECG-based algorithms often face limitations in sensitivity and specificity, particularly for transient or silent ischemia. To address these limitations, CineECG emerges as an innovative diagnostic tool, providing a dynamic and three-dimensional representation of the heart's electrical activity. The aim of the study was to investigate the effectiveness of CineECG with respect to standard ECG algorithms, highlighting the potential diagnostic advantages of this approach in identifying ischemic episodes. The core of literature review focuses on various algorithms developed between three decades for ischemia detection, emphasizing their methodologies, performances, and limitations. The results of this study demonstrate that CineECG offers greater sensitivity in detecting early changes in the activation pattern of the ST-segment, even when minimal changes are present in standard ECG. Clinically, its adoption could enhance early diagnosis, reducing the risk of irreversible cardiac tissue damage and improving patient outcomes. Moreover, the integration of CineECG with automated machine learning models presents an opportunity to streamline clinical workflows and minimize diagnostic errors. Despite these promising findings, further large-scale clinical studies are necessary to confirm the effectiveness of CineECG across diverse patient populations and to establish standardized protocols for its implementation. In conclusion, CineECG emerges as a transformative approach. By visualizing electrical vector trajectories in three dimensions, it offers a dynamic assessment of ischemic episodes that surpasses the static limitations of traditional ECG analysis. By addressing these aspects, the thesis contributes to the ongoing discussion on improving ischemia detection.