Berg Erik Andreas Rye, Taskén Anders Austlid, Nordal Trym, Grenne Bjørnar, Espeland Torvald, Kirkeby-Garstad Idar, Dalen Håvard, Holte Espen, Stølen Stian, Aakhus Svend, Kiss Gabriel
Centre for Innovative Ultrasound Solutions, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Science, Norwegian University of Science and Technology, Prinsesse Kristinas gate 3, Trondheim 7030, Norway.
Department of Circulation and Medical Imaging, Faculty of Medicine and Health Science, Norwegian University of Science and Technology, Prinsesse Kristinas gate 3, Trondheim 7030, Norway.
Eur Heart J Imaging Methods Pract. 2023 Jul 4;1(1):qyad007. doi: 10.1093/ehjimp/qyad007. eCollection 2023 May.
To improve monitoring of cardiac function during major surgery and intensive care, we have developed a method for fully automatic estimation of mitral annular plane systolic excursion (auto-MAPSE) using deep learning in transoesophageal echocardiography (TOE). The aim of this study was a clinical validation of auto-MAPSE in patients with heart disease.
TOE recordings were collected from 185 consecutive patients without selection on image quality. Deep-learning-based auto-MAPSE was trained and optimized from 105 patient recordings. We assessed auto-MAPSE feasibility, and agreement and inter-rater reliability with manual reference in 80 patients with and without electrocardiogram (ECG) tracings. Mean processing time for auto-MAPSE was 0.3 s per cardiac cycle/view. Overall feasibility was >90% for manual MAPSE and ECG-enabled auto-MAPSE and 82% for ECG-disabled auto-MAPSE. Feasibility in at least two walls was ≥95% for all methods. Compared with manual reference, bias [95% limits of agreement (LoA)] was -0.5 [-4.0, 3.1] mm for ECG-enabled auto-MAPSE and -0.2 [-4.2, 3.6] mm for ECG-disabled auto-MAPSE. Intra-class correlation coefficient (ICC) for consistency was 0.90 and 0.88, respectively. Manual inter-observer bias [95% LoA] was -0.9 [-4.7, 3.0] mm, and ICC was 0.86.
Auto-MAPSE was fast and highly feasible. Inter-rater reliability between auto-MAPSE and manual reference was good. Agreement between auto-MAPSE and manual reference did not differ from manual inter-observer agreement. As the principal advantages of deep-learning-based assessment are speed and reproducibility, auto-MAPSE has the potential to improve real-time monitoring of left ventricular function. This should be investigated in relevant clinical settings.
为改善重大手术及重症监护期间的心功能监测，我们开发了一种利用经食管超声心动图（TOE）中的深度学习技术全自动估算二尖瓣环平面收缩期位移（自动MAPSE）的方法。本研究旨在对心脏病患者的自动MAPSE进行临床验证。
连续收集了185例患者的TOE记录，未对图像质量进行筛选。基于105例患者记录对基于深度学习的自动MAPSE进行了训练和优化。我们评估了80例有或没有心电图（ECG）记录的患者中自动MAPSE的可行性、与手动参考值的一致性及评分者间可靠性。自动MAPSE每个心动周期/视图的平均处理时间为0.3秒。手动MAPSE和启用ECG的自动MAPSE的总体可行性>90%，未启用ECG的自动MAPSE的可行性为82%。所有方法在至少两个壁中的可行性≥95%。与手动参考值相比，启用ECG的自动MAPSE的偏差[一致性的95%界限（LoA）]为-0.5[-4.0, 3.1]mm，未启用ECG的自动MAPSE的偏差为-0.2[-4.2, 3.6]mm。一致性的组内相关系数（ICC）分别为0.90和0.88。手动观察者间偏差[95% LoA]为-0.9[-4.7, 3.0]mm，ICC为0.86。
自动MAPSE快速且高度可行。自动MAPSE与手动参考值之间的评分者间可靠性良好。自动MAPSE与手动参考值之间的一致性与手动观察者间一致性无差异。由于基于深度学习评估的主要优势在于速度和可重复性，自动MAPSE有潜力改善左心室功能的实时监测。这应在相关临床环境中进行研究。
