Abstract Background Identification of myocardial ischemia in patients suspected of having coronary artery disease (CAD) remains a challenging issue. Functional or stress testing is widely recognized as the gold standard method for diagnosing myocardial ischemia but is hindered by low diagnostic accuracy and limitations such as radiation exposure. Magnetocardiography (MCG) is a non-contact, non-invasive, and radiation-free method that records magnetic fields produced by the heart's electrical activity. MCG offers numerous advantages. It is highly sensitive and can detect early signs of myocardial ischemia and microvascular dysfunction that may be missed by other diagnostic tools. However, further refinement is needed to improve its diagnostic accuracy. Magnetoionography (MIG), an advanced extension of MCG, enables the assessment of intracellular cardiac currents, providing deeper insights into myocardial electrophysiology. Purpose This study evaluates the efficacy of MIG in improving the accuracy of CAD detection compared to conventional MCG analysis. By incorporating intracellular current dynamics, MIG aims to refine the differentiation between CAD and non-CAD cases, thereby enhancing diagnostic precision. Methods A total of 129 participants (93 CAD patients, 36 healthy controls) underwent non-invasive MCG recordings prior to coronary angiography. The study assessed conventional MCG parameters alongside novel MIG-derived indices, focusing on intracellular ion flux dynamics during repolarization. Stepwise linear discriminant analysis was applied to determine the most informative parameters for CAD classification. Key features such as Heart Rate Stress, Current Moment Dynamics Stress, and Dipolarity Index for ST-Segment Stress were analyzed to distinguish between CAD patients and healthy controls. Results The inclusion of MIG-derived parameters significantly improved CAD detection. MCG alone demonstrated a sensitivity of 90.3% and a specificity of 76.5%. With the integration of MIG parameters, sensitivity increased to 93.5%, while specificity improved to 85.3%. Key parameters contributing to this enhancement included the Current Moment Dynamics Stress and the Dipolarity Index for ST-Segment Stress, which provide a more detailed characterization of myocardial repolarization abnormalities. Statistical analyses confirmed significant differences in these parameters between CAD patients and controls (p 0.01). Conclusion The study demonstrates that MIG substantially enhances the diagnostic accuracy of MCG by incorporating intracellular cardiac current analysis. MIG is a promising advancement in MCG diagnostics, providing a highly sensitive and specific approach to detecting CAD. Future studies will focus on validating these findings in larger and more diverse patient populations. Adding MIG-derived parameters to the traditional MCG Score for CAD will enhance early CAD detection and improve risk stratification.
Dischl et al. (Sat,) studied this question.