Why the study?
Detecting and determining the exact size of the LAA is difficult due to cardiac musculature and irregular LAA anatomy.
Does a fully automatic neural network approach accurately measure LAA size from echocardiography images for WATCHMAN device implantation in AFib patients?
Does a fully automatic neural network approach accurately measure LAA size from echocardiography images for WATCHMAN device implantation in AFib patients?
A fully automatic neural network-based approach can analyze echocardiography images to accurately measure LAA dimensions for WATCHMAN device sizing.
Nowadays Atrial Fibrillation (AFib) has been highly epidemic that is a heart condition causes the heart in an irregular rhythm and can increase the risk of stroke. The Left Atrial Appendage (LAA) which is a part of the heart is the main source of thrombosis in patients with AFib. Since AFib patients are at risk of developing clots in LAA, it is, therefore, necessary to detect the LAA anatomy and closure of LAA to prevent stroke. For some reason, such as the presence of different muscles in the heart and the irregular anatomy of LAA, detecting and determining the exact size of the LAA is difficult. In this paper, we present a fully automatic approach to diagnose the LAA and measure its size based on the inlet orifice and the depth of the LAA primary lobe for the implant by the WATCHMAN device. These processes utilize images that were taken by echocardiography (Echo) from the heart of patients and analyses at high speed with precision, and used the standard table provided by the manufacturer (WATCHMAN) and Neural Network (NN) to determine the accuracy the appropriate size of LAA for left atrial appendage closure (LAAC).
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Khezri et al. (2019) studied this question.
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