Anna Maria Meloni

Clinical application of magnetocardiography

Magnetocardiography is a noninvasive contactless method to measure the magnetic field generated by the same ionic currents that create the electrocardiogram. The time course of magnetocardiographic and electrocardiographic signals are similar. However, compared with surface potential recordings, multichannel magnetocardiographic mapping (MMCG) is a faster and contactless method for 3D imaging and localization of cardiac electrophysiologic phenomena with higher spatial and temporal resolution. For more than a decade, MMCG has been mostly confined to magnetically shielded rooms and considered to be at most an interesting matter for research activity. Nevertheless, an increasing number of papers have documented that magnetocardiography can also be useful to improve diagnostic accuracy. Most recently, the development of standardized instrumentations for unshielded MMCG, and its ease of use and reliability even in emergency rooms has triggered a new interest from clinicians for magnetocardiography, leading to several new installations of unshielded systems worldwide. In this review, clinical applications of magnetocardiography are summarized, focusing on major milestones, recent results of multicenter clinical trials and indicators of future developments.

Clinical validation of machine learning for automatic analysis of multichannel magnetocardiography

Magnetocardiographic (MCG) mapping measures magnetic fields generated by the electrophysiological activity of the heart. Quantitative analysis of MCG ventricular repolarization (VR) parameters may be useful to detect myocardial ischemia in patients with apparently normal ECG. However, manual calculation of MCG VR is time consuming and can be dependent on the examiner’s experience. Alternatively, the use of machine learning (ML) has been proposed recently to automate the interpretation of MCG recordings and to minimize human interference with the analysis. The aim of this study was to validate the predictive value of ML techniques in comparison with interactive, computer-aided, MCG analysis. ML testing was done on a set of 140 randomly analysed MCG recordings from 74 subjects: 41 patients with ischemic heart disease (IHD) (group 1), 32 of them untreated (group 2), and 33 subjects without any evidence of cardiac disease (group 3). For each case at least 2 MCG datasets, recorded in different sessions, were analysed. Two ML techniques combined identified abnormal VR in 25 IHD patients (group 1) and excluded VR abnormalities in 28 controls (group 3) providing 75% sensitivity, 85% specificity, 83% positive predictive value, 78% negative predictive value, 80% predictive accuracy This result was for the most part in agreement, but statistically better than that obtained with interactive analysis. This study confirms that ML, applied on MCG recording at rest, has a predictive accuracy of 80% in detecting electrophysiological alterations associated with untreated IHD. Further work is needed to test the ML capability to differentiate VR alterations due to IHD from those due to non-ischemic cardiomyopathies.