Department of Medicine, Hospital of Southern Norway, Kristiansand, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway.
Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
JACC Cardiovasc Imaging. 2021 Oct;14(10):1918-1928. doi: 10.1016/j.jcmg.2021.04.018. Epub 2021 Jun 16.
This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible and comparable with a conventional speckle-tracking application.
GLS is an important parameter when evaluating left ventricular function. However, analyses of GLS are time consuming and demand expertise, and thus are underused in clinical practice.
In this study, 200 patients with a wide range of left ventricle (LV) function were included. Three standard apical cine-loops were analyzed using the AI pipeline. The AI method measured GLS and was compared with a commercially available semiautomatic speckle-tracking software (EchoPAC v202, GE Healthcare.
The AI method succeeded to both correctly classify all 3 standard apical views and perform timing of cardiac events in 89% of patients. Furthermore, the method successfully performed automatic segmentation, motion estimates, and measurements of GLS in all examinations, across different cardiac pathologies and throughout the spectrum of LV function. GLS was -12.0 ± 4.1% for the AI method and -13.5 ± 5.3% for the reference method. Bias was -1.4 ± 0.3% (95% limits of agreement: 2.3 to -5.1), which is comparable with intervendor studies. The AI method eliminated measurement variability and a complete GLS analysis was processed within 15 s.
Through the range of LV function this novel AI method succeeds, without any operator input, to automatically identify the 3 standard apical views, perform timing of cardiac events, trace the myocardium, perform motion estimation, and measure GLS. Fully automated measurements based on AI could facilitate the clinical implementation of GLS.
本研究旨在探讨基于深度学习和人工智能（AI）的新型运动估计技术全自动测量整体纵向应变（GLS）是否可行，并与传统斑点追踪应用相媲美。
GLS 是评估左心室功能的重要参数。然而，GLS 分析既耗时又需要专业知识，因此在临床实践中未得到充分应用。
本研究纳入了 200 例左心室（LV）功能广泛的患者。使用 AI 流水线分析 3 个标准心尖电影环。AI 方法测量 GLS，并与商业上可用的半自动斑点追踪软件（EchoPAC v202，GE Healthcare）进行比较。
AI 方法成功地正确分类了所有 3 个标准心尖视图，并在 89%的患者中正确进行了心脏事件的定时。此外，该方法在所有检查中均成功执行了自动分割、运动估计和 GLS 测量，涵盖了不同的心脏病理学和 LV 功能谱。AI 方法的 GLS 为-12.0±4.1%，参考方法为-13.5±5.3%。偏差为-1.4±0.3%（95%置信区间：2.3 至-5.1），与不同厂家的研究结果相当。AI 方法消除了测量变异性，并且可以在 15 秒内完成完整的 GLS 分析。
通过 LV 功能范围，该新型 AI 方法无需任何操作人员的输入，即可自动识别 3 个标准心尖视图，进行心脏事件定时，追踪心肌，进行运动估计并测量 GLS。基于 AI 的全自动测量方法可以促进 GLS 的临床应用。
