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5. PROGnostic RolE of strain measurements in stress cardiac MRI in predicting major adverse cardiac events

6. DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR

11. Quantification of extracellular volume with cardiac computed tomography in patients with dilated cardiomyopathy

13. Assessing right atrial size in patients with tricuspid regurgitation: importance of the right ventricular-focused view.

17. DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR

18. PROGnostic RolE of strain measurements in stress cardiac MRI in predicting major adverse cardiac events

19. Peak atrial longitudinal strain and risk stratification in moderate and severe aortic stenosis

20. Deep Learning-based Quantification of Epicardial Adipose Tissue Volume in Stress CMR Predicts Major Adverse Cardiovascular Events in Patients with Known or Suspected Coronary Artery Disease

21. Circulating plasma exosomes reflect the severity of myocardial damage in STEMI patients

22. Circulating Small Extracellular Vesicles Reflect the Severity of Myocardial Damage in STEMI Patients

23. Identification of subclinical cardiac amyloidosis in aortic stenosis patients undergoing transaortic valve replacement using radiomic analysis of computed tomography myocardial texture

24. A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images

25. Kiosk 10R-TB-04 - Deep Learning-based Quantification of Epicardial Adipose Tissue Volume in Stress CMR Predicts Major Adverse Cardiovascular Events in Patients with Known or Suspected Coronary Artery Disease

26. Quantification of extracellular volume with cardiac computed tomography in patients with dilated cardiomyopathy

27. Identification of subclinical cardiac amyloidosis in aortic stenosis patients undergoing transaortic valve replacement using radiomic analysis of computed tomography myocardial texture

28. A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images

29. Cardiac computed tomography: from anatomy to function

31. Outcomes and mechanical complications of acute myocardial infarction during the second wave pandemic in a Milan HUB center for cardiac emergencies

33. Additional file 1 of Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment

34. Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair

35. Head to Head Comparison between Different 3-Dimensional Echocardiographic Rendering Tools in the Imaging of Percutaneous Edge-to-Edge Mitral Valve Repair

36. Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques

37. Novelties in 3D Transthoracic Echocardiography

39. Predictive Value of Pre-Operative 2D and 3D Transthoracic Echocardiography in Patients Undergoing Mitral Valve Repair: Long Term Follow Up of Mitral Valve Regurgitation Recurrence and Heart Chamber Remodeling

40. UTILITY OF THE RATIO BETWEEN THE SIZE OF THE RIGHT ATRIUM AND THE RIGHT VENTRICLE AT END-SYSTOLE TO DIAGNOSE ATRIAL SECONDARY TRICUSPID REGURGITATION

41. Advanced echocardiography and cluster analysis to identify secondary tricuspid regurgitation phenogroups at different risk.

42. A Novel Staging System of Cardiac Damage in Aortic Stenosis based on Multi-Chamber Myocardial Deformation.

43. Assessing right atrial size in patients with tricuspid regurgitation: importance of the right ventricular-focused view.

44. Peak atrial longitudinal strain and risk stratification in moderate and severe aortic stenosis.

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