Based on cardiovascular magnetic resonance imaging for predicting right ventricular dysfunction in STEMI patients by Machine learning

In clinical work, right ventricular (RV) dysfunction has been ignored in acute ST-Segment-Elevation myocardial infarction (STEMI) patients. And previous studies have shown the interaction between left ventricular (LV) and RV. The aim of this study was to assess RV function by cardiovascular magnetic resonance feature tracking (CMR-FT) and to explore what factors affect RV dysfunction by machine learning techniques. The results showed that the incidence of RV dysfunction was 32.28% in STEMI patients and the occurrence of RV dysfunction was associated with RV end-systolic volume index, LV ejection fraction (EF), and interventricular septum radial strain by machine learning technique.