Key result
Automated mid-diastole frame detection matches expert cardiologist opinion within ~1.4 frames.
Why the study?
Manual determination of the mid-diastole frame for mitral valve orifice planimetry in mitral stenosis is time-consuming and user-dependent.
Does an automatic mid-diastole frame detection algorithm accurately identify the mid-diastole frame compared to expert opinion in 2D echocardiography of patients with mitral valve stenosis?
Does an automatic mid-diastole frame detection algorithm accurately identify the mid-diastole frame compared to expert opinion in 2D echocardiography of patients with mitral valve stenosis?
A novel automatic algorithm using local linear embedding accurately detects the mid-diastole frame in 2D echocardiography for mitral valve planimetry, closely matching expert opinion.
Answers are grounded in this paper and cite their sources.
May support automated mid-diastole selection in mitral stenosis echocardiography; hypothesis-generating and requires larger validation before clinical use.
The mitral valve orifice area is a reliable measure for evaluating mitral valve stenosis (MS) severity, which is obtained by the planimetry of the mid‐diastole frame in the echocardiography sequences. Since the manual method for determining this frame is time‐consuming and user‐dependent, a novel automatic method has been proposed in this study. First, the region of interest (ROI) containing the mitral valve orifice region is detected using circular Hough transform and k‐means algorithms. Then, the dimension reduction method is applied to the ROI of each frame to map it into a point in a 2D space. The performance of the local linear embedding (LLE), isometric mapping, kernel principal component analysis (PCA), and linear PCA algorithms has been evaluated in this study. Finally, a distance curve is obtained by calculating the Euclidean distance between consecutive points in 2D space, and the mid‐diastole frame is determined by interpreting this curve. The proposed algorithm was validated using 2D echocardiography of the 20 MS patients. Finally, the LLE method showed the best result, and the average frame difference for 20 cases using the proposed method compared with the gold standard (the echo‐cardiologist opinion) was 1.40.
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Faraji et al. (2020) studied Mitral valve stenosis (n=20). Automatic mid-diastole frame detection algorithm (LLE method) vs. Gold standard (echo-cardiologist opinion) was evaluated on Average frame difference compared with the gold standard. An automatic mid-diastole frame detection algorithm using local linear embedding achieved an average frame difference of 1.40 compared with expert cardiologist opinion.
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