Key result
Automated machine learning offers a potential alternative to PISA for quantifying MR severity.
Why the study?
Does automated jet area segmentation using K-Means clustering and machine learning classifiers accurately quantify mitral regurgitation severity compared to the PISA method?
Does automated jet area segmentation using K-Means clustering and machine learning classifiers accurately quantify mitral regurgitation severity compared to the PISA method?
Automated segmentation and machine learning classification of color Doppler jet areas may provide a viable alternative to the PISA method for quantifying mitral regurgitation severity.
Requires prospective validation before clinical use; leaves open machine learning alternatives to PISA for mitral regurgitation quantification.
Mitral regurgitation (MR) is a disorder of mitral valve and it is one of the most common causes of cardiovascular morbidity and mortality. Mitral valve allows blood to flow from left atrium, to the left ventricle and Mitral Valve regurgitation results in poor apposition of the valvular leaflets, so that the heart's mitral valve doesn't close tightly, allowing blood to flow backward into the left atrium. Transthoracic Echocardiography (TTE) with Doppler is the widely used non-invasive technology for the detection and evaluation of severity of valvular regurgitation. Proximal isovelocity surface area (PISA) method has been widely accepted by clinicians as a means for grading MR severity. In this paper an alternate method to PISA to automatically quantify mitral valve regurgitation severity is proposed. This work attempts to automatically segment the jet region in color Doppler images using K-Means clustering. Further to quantify mitral regurgitation, jet area parameters and shape features are extracted from the segmented jet region which are then modeled using classifiers such as Support Vector machine (SVM) and Back Propagation Neural Network (BPNN). Quantifying MR with PISA calls for considerable expertise as a number of components must be taken into account to fully assess the severity of mitral regurgitation, however the results of the proposed method indicate that it could be used as an alternate method to automatically assess the severity of mitral regurgitation.
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Chidambaram et al. (2018) studied Mitral regurgitation. Automated segmentation of jet area using K-Means clustering and classification (SVM, BPNN) vs. Proximal isovelocity surface area (PISA) method was evaluated on Quantification of mitral regurgitation severity. An automated method using K-Means clustering for jet area segmentation and SVM/BPNN classifiers was proposed as a potential alternative to PISA for quantifying mitral regurgitation severity.
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