Rahmouni Hind W, Ky Bonnie, Plappert Ted, Duffy Kevin, Wiegers Susan E, Ferrari Victor A, Keane Martin G, Kirkpatrick James N, Silvestry Frank E, St John Sutton Martin
Department of Medicine, Cardiovascular Division, University of Pennsylvania, School of Medicine, Philadelphia, PA, USA.
Am Heart J. 2008 Mar;155(3):562-70. doi: 10.1016/j.ahj.2007.11.002.
Ejection fraction (EF) calculated from 2-dimensional echocardiography provides important prognostic and therapeutic information in patients with heart disease. However, quantification of EF requires planimetry and is time-consuming. As a result, visual assessment is frequently used but is subjective and requires extensive experience. New computer software to assess EF automatically is now available and could be used routinely in busy digital laboratories (>15,000 studies per year) and in core laboratories running large clinical trials. We tested Siemens AutoEF software (Siemens Medical Solutions, Erlangen, Germany) to determine whether it correlated with visual estimates of EF, manual planimetry, and cardiac magnetic resonance (CMR).
Siemens AutoEF is based on learned patterns and artificial intelligence. An expert and a novice reader assessed EF visually by reviewing transthoracic echocardiograms from consecutive patients. An experienced sonographer quantified EF in all studies using Simpson's method of disks. AutoEF results were compared to CMR.
Ninety-two echocardiograms were analyzed. Visual assessment by the expert (R = 0.86) and the novice reader (R = 0.80) correlated more closely with manual planimetry using Simpson's method than did AutoEF (R = 0.64). The correlation between AutoEF and CMR was 0.63, 0.28, and 0.51 for EF, end-diastolic and end-systolic volumes, respectively.
The discrepancies in EF estimates between AutoEF and manual tracing using Simpson's method and between AutoEF and CMR preclude routine clinical use of AutoEF until it has been validated in a number of large, busy echocardiographic laboratories. Visual assessment of EF, with its strong correlation with quantitative EF, underscores its continued clinical utility.
二维超声心动图计算得出的射血分数（EF）可为心脏病患者提供重要的预后和治疗信息。然而，EF的量化需要进行面积测量，且耗时较长。因此，视觉评估经常被采用，但它具有主观性，且需要丰富的经验。现在有了新的自动评估EF的计算机软件，可在繁忙的数字化实验室（每年超过15000例检查）以及进行大型临床试验的核心实验室中常规使用。我们测试了西门子AutoEF软件（德国埃尔兰根西门子医疗解决方案公司），以确定其与EF的视觉估计值、手动面积测量以及心脏磁共振成像（CMR）是否相关。
西门子AutoEF基于学习模式和人工智能。一名专家和一名新手读者通过查看连续患者的经胸超声心动图对EF进行视觉评估。一名经验丰富的超声检查人员在所有研究中使用辛普森圆盘法对EF进行量化。将AutoEF的结果与CMR进行比较。
分析了92份超声心动图。与AutoEF（R = 0.64）相比，专家（R = 0.86）和新手读者（R = 0.80）的视觉评估与使用辛普森法的手动面积测量的相关性更高。AutoEF与CMR之间在EF、舒张末期容积和收缩末期容积方面的相关性分别为0.63、0.28和0.51。
在多个大型、繁忙的超声心动图实验室得到验证之前，AutoEF与使用辛普森法的手动描记法之间以及AutoEF与CMR之间在EF估计值上的差异使得AutoEF无法常规用于临床。EF的视觉评估与定量EF具有很强的相关性，突出了其持续的临床实用性。
