Ultrasound Examination Center, Tokushima University Hospital, Tokushima, Japan.
Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
J Echocardiogr. 2024 Sep;22(3):162-170. doi: 10.1007/s12574-023-00636-6. Epub 2024 Feb 3.
Manual interpretation of echocardiographic data is time-consuming and operator-dependent. With the advent of artificial intelligence (AI), there is a growing interest in its potential to streamline echocardiographic interpretation and reduce variability. This study aimed to compare the time taken for measurements by AI to that by human experts after converting the acquired dynamic images into DICOM data.
Twenty-three consecutive patients were examined by a single operator, with varying image quality and different medical conditions. Echocardiographic parameters were independently evaluated by human expert using the manual method and the fully automated US2.ai software. The automated processes facilitated by the US2.ai software encompass real-time processing of 2D and Doppler data, measurement of clinically important variables (such as LV function and geometry), automated parameter assessment, and report generation with findings and comments aligned with guidelines. We assessed the duration required for echocardiographic measurements and report creation.
The AI significantly reduced the measurement time compared to the manual method (159 ± 66 vs. 325 ± 94 s, p < 0.01). In the report creation step, AI was also significantly faster compared to the manual method (71 ± 39 vs. 429 ± 128 s, p < 0.01). The incorporation of AI into echocardiographic analysis led to a 70% reduction in measurement and report creation time compared to manual methods. In cases with fair or poor image quality, AI required more corrections and extended measurement time than in cases of good image quality. Report creation time was longer in cases with increased report complexity due to human confirmation of AI-generated findings.
This fully automated software has the potential to serve as an efficient tool for echocardiographic analysis, offering results that enhance clinical workflow by providing rapid, zero-click reports, thereby adding significant value.
手动解释超声心动图数据既耗时又依赖于操作人员。随着人工智能（AI）的出现，人们越来越关注其在简化超声心动图解释和减少变异性方面的潜力。本研究旨在比较将获得的动态图像转换为 DICOM 数据后，由 AI 进行测量与由人类专家进行测量所花费的时间。
由一名操作人员对 23 例连续患者进行检查，图像质量和患者的医疗状况各不相同。人类专家使用手动方法和完全自动化的 US2.ai 软件分别独立评估超声心动图参数。US2.ai 软件提供的自动化处理过程包括实时处理 2D 和多普勒数据、测量临床重要变量（如 LV 功能和几何形状）、自动评估参数以及生成带有与指南一致的发现和评论的报告。我们评估了进行超声心动图测量和报告生成所需的时间。
与手动方法相比，AI 显著缩短了测量时间（159±66 秒与 325±94 秒，p<0.01）。在报告生成步骤中，AI 也明显快于手动方法（71±39 秒与 429±128 秒，p<0.01）。与手动方法相比，将 AI 纳入超声心动图分析可将测量和报告生成时间缩短 70%。在图像质量较差或一般的情况下，AI 需要进行更多的校正并延长测量时间，而在图像质量较好的情况下则不需要。由于需要人工确认 AI 生成的结果，因此报告复杂性增加时，报告生成时间也会延长。
这种完全自动化的软件有潜力成为一种高效的超声心动图分析工具，通过提供快速、无需点击的报告，增强临床工作流程，从而带来显著的价值。
