Nyberg John, Østvik Andreas, Salte Ivar M, Olaisen Sindre, Karlsen Sigve, Dahlslett Thomas, Smistad Erik, Eriksen-Volnes Torfinn, Brunvand Harald, Edvardsen Thor, Haugaa Kristina H, Lovstakken Lasse, Dalen Havard, Grenne Bjørnar
Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Box 8905, 7491 Trondheim, Norway.
ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Box 4950, 0424 Oslo, Norway.
Eur Heart J Imaging Methods Pract. 2024 Oct 23;2(4):qyae092. doi: 10.1093/ehjimp/qyae092. eCollection 2024 Oct.
The clinical utility of regional strain measurements in echocardiography is challenged by suboptimal reproducibility. In this study, we aimed to evaluate the test-retest reproducibility of regional longitudinal strain (RLS) per coronary artery perfusion territory (RLS) and basal-to-apical level of the left ventricle (RLS), measured by a novel fully automated deep learning (DL) method based on point tracking.
We measured strain in a dual-centre test-retest data set that included 40 controls and 40 patients with suspected non-ST elevation acute coronary syndrome. Two consecutive echocardiograms per subject were recorded by different operators. The reproducibility of RLS and RLS measured by the DL method and by three experienced observers using semi-automatic software (2D Strain, EchoPAC, GE HealthCare) was evaluated as minimal detectable change (MDC). The DL method had MDC for RLS and RLS ranging from 3.6 to 4.3%, corresponding to a 33-35% improved reproducibility compared with the inter- and intraobserver scenarios (MDC 5.5-6.4% and 4.9-5.4%). Furthermore, the DL method had a lower variance of test-retest differences for both RLS and RLS compared with inter- and intraobserver scenarios (all < 0.001). Bland-Altman analyses demonstrated superior reproducibility by the DL method for the whole range of strain values compared with the best observer scenarios. The feasibility of the DL method was 93% and measurement time was only 1 s per echocardiogram.
The novel DL method provided fully automated measurements of RLS, with improved test-retest reproducibility compared with semi-automatic measurements by experienced observers. RLS measured by the DL method has the potential to advance patient care through a more detailed, more efficient, and less user-dependent clinical assessment of myocardial function.
超声心动图中局部应变测量的临床实用性受到再现性欠佳的挑战。在本研究中，我们旨在评估基于点追踪的新型全自动深度学习（DL）方法测量的每个冠状动脉灌注区域的局部纵向应变（RLS）以及左心室从心底到心尖水平的局部纵向应变（RLS）的重测再现性。
我们在一个双中心重测数据集中测量应变，该数据集包括40名对照者和40名疑似非ST段抬高型急性冠状动脉综合征患者。由不同操作人员为每个受试者记录连续两份超声心动图。通过最小可检测变化（MDC）评估DL方法以及三名经验丰富的观察者使用半自动软件（2D应变，EchoPAC，GE医疗）测量的RLS和RLS的再现性。DL方法测量RLS和RLS的MDC范围为3.6%至4.3%，与观察者间和观察者内情况相比，再现性提高了33%至35%（MDC为5.5%至6.4%和4.9%至5.4%）。此外，与观察者间和观察者内情况相比，DL方法测量的RLS和RLS的重测差异方差更低（均<0.001）。Bland-Altman分析表明，与最佳观察者情况相比，DL方法在整个应变值范围内具有更好的再现性。DL方法的可行性为93%，每份超声心动图的测量时间仅为1秒。
新型DL方法提供了RLS的全自动测量，与经验丰富的观察者进行的半自动测量相比，重测再现性有所提高。通过对心肌功能进行更详细、更高效且更少依赖用户的临床评估，DL方法测量的RLS有潜力推动患者护理的发展。
