The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.

中文翻译：

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利用人工智能的力量：超声心动图左心室射血分数评估的全面回顾。

左心室射血分数（LVEF）的量化在评估心功能方面具有重要的临床实用性，并且对于心血管疾病的诊断至关重要。经胸超声心动图作为 LVEF 评估最常用的工具有多种原因，包括其无创性、良好的安全性、实时图像处理能力、便携性和成本效益。然而，经胸超声心动图高度依赖于超声检查医师和解读医生的临床技能。此外，即使在训练有素的临床医生中，LVEF 的量化也存在显着的观察者间差异。为了寻找可能的解决方案，人工智能 (AI) 的使用在临床环境中得到了越来越多的测试。虽然人工智能衍生的射血分数正处于开发的初步阶段，但它在快速量化 LVEF、减少变异性、提高准确性和利用高阶处理能力方面已显示出前景。这篇综述将描述人工智能在通过超声心动图评估 LVEF 方面的最新进展，并探讨这一新兴领域的挑战和未来发展轨迹。