Improvement of automated analysis of coronary Doppler echocardiograms.
Improvement of automated analysis of coronary Doppler echocardiograms.
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DOI：
10.1038/s41598-022-11402-6
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发表时间：
2022-05-06
影响因子：
4.6
通讯作者：
Trask, Aaron J.
中科院分区：
文献类型：
--
作者：
Bossenbroek, Jamie;Ueyama, Yukie;McCallinhart, Patricia E.;Bartlett, Christopher W.;Ray, William C.;Trask, Aaron J.
冠状动脉疾病是心脏病的主要原因，虽然可以通过观察冠状动脉血流变化通过经胸多普勒超声心动图（TTDE）进行评估，但手工分析TTDE既耗时又容易产生偏差。在之前的一项研究中，创建了一个程序，通过将多普勒视频解析为单个连续图像，将图像二值化并分离为心动周期，并从每个周期中提取数据值，从而自动分析冠状动脉血流模式。该程序显著减少了完成TTDE分析的可变性和时间，但一些障碍，如干扰噪声和不同的视频大小，为提高程序的准确性留下了空间。当前研究的目标是通过以下方式改进现有的自动化算法和启发式方法：(1)将程序移到Python环境中，(2)提高程序处理具有挑战性的案例和视频变化的能力，以及(3)从最终数据集中删除不具代表性的心脏周期。有了这种改进的分析，检查人员可以使用自动程序轻松准确地识别严重心脏病的早期迹象。
Coronary artery disease is the leading cause of heart disease, and while it can be assessed through transthoracic Doppler echocardiography (TTDE) by observing changes in coronary flow, manual analysis of TTDE is time consuming and subject to bias. In a previous study, a program was created to automatically analyze coronary flow patterns by parsing Doppler videos into a single continuous image, binarizing and separating the image into cardiac cycles, and extracting data values from each of these cycles. The program significantly reduced variability and time to complete TTDE analysis, but some obstacles such as interfering noise and varying video sizes left room to increase the program’s accuracy. The goal of this current study was to refine the existing automation algorithm and heuristics by (1) moving the program to a Python environment, (2) increasing the program’s ability to handle challenging cases and video variations, and (3) removing unrepresentative cardiac cycles from the final data set. With this improved analysis, examiners can use the automatic program to easily and accurately identify the early signs of serious heart diseases.
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DOI：
10.1109/iembs.2010.5627571
发表时间：
2010
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Hartley CJ;Reddy AK;Michael LH;Entman ML;Chintalagattu V;Khakoo AY;Taffet GE
通讯作者：
Taffet GE
影响因子：
9.5
作者：
Katz PS;Trask AJ;Souza-Smith FM;Hutchinson KR;Galantowicz ML;Lord KC;Stewart JA Jr;Cismowski MJ;Varner KJ;Lucchesi PA
通讯作者：
Lucchesi PA
影响因子：
3.2
作者：
Magagnin, V.;Delfino, L.;Caiani, E. G.
通讯作者：
Caiani, E. G.
影响因子：
24
作者：
Taqueti VR;Di Carli MF
通讯作者：
Di Carli MF
影响因子：
3.5
作者：
Finegold, Judith A.;Manisty, Charlotte H.;Francis, Darrel P.
通讯作者：
Francis, Darrel P.