Sabo Sigbjorn, Pasdeloup David, Pettersen Hakon Neergaard, Smistad Erik, Østvik Andreas, Olaisen Sindre Hellum, Stølen Stian Bergseng, Grenne Bjørnar Leangen, Holte Espen, Lovstakken Lasse, Dalen Havard
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, PO Box 8905, 7491 Trondheim, Norway.
Clinic of Cardiology, St.Olavs University Hospital, Prinsesse Kristinas gate 3, 7030 Trondheim, Norway.
Eur Heart J Imaging Methods Pract. 2023 Nov 27;1(2):qyad040. doi: 10.1093/ehjimp/qyad040. eCollection 2023 Sep.
Impaired standardization of echocardiograms may increase inter-operator variability. This study aimed to determine whether the real-time guidance of experienced sonographers by deep learning (DL) could improve the standardization of apical recordings.
Patients ( = 88) in sinus rhythm referred for echocardiography were included. All participants underwent three examinations, whereof two were performed by sonographers and the third by cardiologists. In the first study period (Period 1), the sonographers were instructed to provide echocardiograms for the analyses of the left ventricular function. Subsequently, after brief training, the DL guidance was used in Period 2 by the sonographer performing the second examination. View standardization was quantified retrospectively by a human expert as the primary endpoint and the DL algorithm as the secondary endpoint. All recordings were scored in rotation and tilt both separately and combined and were categorized as standardized or non-standardized. Sonographers using DL guidance had more standardized acquisitions for the combination of rotation and tilt than sonographers without guidance in both periods (all ≤ 0.05) when evaluated by the human expert and DL [except for the apical two-chamber (A2C) view by DL evaluation]. When rotation and tilt were analysed individually, A2C and apical long-axis rotation and A2C tilt were significantly improved, and the others were numerically improved when evaluated by the echocardiography expert. Furthermore, all, except for A2C rotation, were significantly improved when evaluated by DL ( < 0.01).
Real-time guidance by DL improved the standardization of echocardiographic acquisitions by experienced sonographers. Future studies should evaluate the impact with respect to variability of measurements and when used by less-experienced operators.
NCT04580095.
超声心动图标准化的受损可能会增加操作者之间的变异性。本研究旨在确定深度学习（DL）对经验丰富的超声检查人员的实时指导是否可以提高心尖部记录的标准化。
纳入因超声心动图检查而窦性心律的患者（n = 88）。所有参与者均接受了三项检查，其中两项由超声检查人员进行，第三项由心脏病专家进行。在第一个研究阶段（阶段1），指导超声检查人员提供用于分析左心室功能的超声心动图。随后，经过简短培训后，在阶段2中，进行第二次检查的超声检查人员使用了DL指导。由人类专家将视图标准化作为主要终点进行回顾性量化，将DL算法作为次要终点进行量化。对所有记录的旋转和倾斜分别以及综合进行评分，并分为标准化或非标准化。当由人类专家和DL评估时，在两个阶段中，使用DL指导的超声检查人员在旋转和倾斜组合方面的采集标准化程度均高于未使用指导的超声检查人员（所有P≤0.05）[DL评估的心尖两腔（A2C）视图除外]。当单独分析旋转和倾斜时，超声心动图专家评估时，A2C以及心尖长轴旋转和A2C倾斜均有显著改善，其他方面在数值上有所改善。此外，当由DL评估时，除A2C旋转外，所有其他方面均有显著改善（P < 0.01）。
DL的实时指导提高了经验丰富的超声检查人员超声心动图采集的标准化程度。未来的研究应评估其对测量变异性的影响以及经验较少的操作者使用时的情况。
NCT04580095。
