Key result
Echo-based computer vision algorithm correlates strongly with expert aortic valve calcium assessment at r=0.92.
Why the study?
Aortic valve calcium identification requires an echocardiography expert and CT quantification exposes patients to radiation, limiting monitoring, while computer vision on echocardiography could facilitate tracking.
Does a Computer Vision algorithm accurately identify and quantify aortic valve calcium from echocardiography compared to expert manual assessment in patients with calcific aortic stenosis?
Does a Computer Vision algorithm accurately identify and quantify aortic valve calcium from echocardiography compared to expert manual assessment in patients with calcific aortic stenosis?
Effect estimate: Pearson r 0.92
p-value: p=0.00048
A novel Computer Vision algorithm applied to echocardiography images demonstrated a strong correlation with expert manual assessment for quantifying aortic valve calcification, potentially reducing the need for CT scans.
May facilitate echo calcium scoring in aortic stenosis; extends technical validation but leaves open CT replacement.
Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient’s monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images—being always the darker region—blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images.
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Elvas et al. (2021) studied Aortic Valve Stenosis (n=12). Computer Vision-based calcium identification and scoring on echocardiography vs. Manual planimetry by echocardiography experts was evaluated on Correlation between CV-based calcium pixel count and manual calcium area measurement (Pearson r 0.92, p=0.00048). A Computer Vision algorithm for semi-automatic calcium identification on echocardiography demonstrated a strong correlation (Pearson r=0.92, p=0.00048) with manual expert assessment of calcium area in patients with aortic stenosis.
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