Heart failure is complicated, costly and is caused by a variety of underlying conditions, including coronary artery disease, valvular disease, arrythmias, myocarditis, infiltrative disorders (such as amyloidosis) and from chemotherapy.1,2 Regardless of etiology, robust and reproducible imaging tools are important to diagnose and monitor disease progression in heart failure. See how artificial intelligence (AI) and advanced automation bring this level of reproducibility and robustness, while addressing the need to efficiently complete the study and return results to the referring physician.
Heart failure generates an enormous clinical, social and economic burden, and is likely to increase in the coming years with an aging population and a greater number of therapies to treat heart failure. This burden especially affects areas of lower socio-demographic regions that lack the healthcare infrastructure to meet the challenge effectively.1 The estimated current worldwide economic burden of heart failure is $346.17 billion.1 Clearly, new answers are needed. Robust and reproducible 2D and 3D echocardiographic data are key to diagnosing and managing heart failure.
$346.17 billion estimated current worldwide economic burden of heart failure.
Echocardiography is the most commonly utilized imaging test in heart failure.2 It provides immediate information on chamber volumes and function, valve function, diastolic function and hemodynamics.2 Advances in AI and automation provide the results that are essential for effective diagnosis and management of heart failure.
I use information derived from both 2D and 3D analysis and strain to better understand my patients. AutoStrain, Dynamic HeartModel and Auto RV are invaluable tools that are used both in the diagnosis and surveillance of patients with heart failure.
Robust and reproducible imaging tools are important to diagnose and monitor disease progression in heart failure. Current guidelines support the use of advanced echocardiography tools such as longitudinal strain and 3D assessment of LV and RV volumes and function.
Advancement of echocardiography workflows for heart failure