School of Computing and Engineering, University of West London, United Kingdom; National Heart and Lung Institute, Imperial College London, United Kingdom.
National Heart and Lung Institute, Imperial College London, United Kingdom.
Comput Biol Med. 2024 Mar;171:108192. doi: 10.1016/j.compbiomed.2024.108192. Epub 2024 Feb 23.
Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application.
多普勒超声心动图是一种广泛应用的非侵入性成像方式，用于评估心脏瓣膜的功能，包括二尖瓣。临床医生手动评估多普勒迹线会引入变异性，因此需要自动化解决方案。本研究引入了一种创新的深度学习模型，用于自动检测二尖瓣流入道多普勒图像中的峰值速度测量值，无需心电图信息。建立了一个由多位专家心脏病学家注释的多普勒图像数据集，作为一个强大的基准。该模型利用热图回归网络，实现了 96%的检测准确率。该模型与专家共识的差异在测量多普勒峰值速度的观察者内和观察者间变异性范围内。数据集和模型是开源的，促进了进一步的研究和临床应用。
