PDF(Pubmed)
Abstract：
Artificial intelligence (AI) offers great promise in cardiology, and medicine broadly, for its ability to tirelessly integrate vast amounts of data. Applications in medical imaging are particularly attractive, as images are a powerful means to convey rich information and are extensively utilized in cardiology practice. Departing from other AI approaches in cardiology focused on task automation and pattern recognition, we describe a digital health platform to synthesize enhanced, yet familiar, clinical images to augment the cardiologist\'s visual clinical workflow. In this article, we present the framework, technical fundamentals, and functional applications of the methodology, especially as it pertains to intravascular imaging. A conditional generative adversarial network was trained with annotated images of atherosclerotic diseased arteries to generate synthetic optical coherence tomography and intravascular ultrasound images on the basis of specified plaque morphology. Systems leveraging this unique and flexible construct, whereby a pair of neural networks is competitively trained in tandem, can rapidly generate useful images. These synthetic images replicate the style, and in several ways exceed the content and function, of normally acquired images. By using this technique and employing AI in such applications, one can ameliorate challenges in image quality, interpretability, coherence, completeness, and granularity, thereby enhancing medical education and clinical decision-making.
摘要：
人工智能（AI）在心脏病学中提供了巨大的希望，和广泛的医学，因为它能够不知疲倦地集成大量数据。在医学成像中的应用特别有吸引力，因为图像是传达丰富信息的有力手段，并且在心脏病学实践中被广泛使用。与心脏病学中其他人工智能方法不同，侧重于任务自动化和模式识别，我们描述了一个数字健康平台来综合增强，然而熟悉，临床图像以增强心脏病专家的视觉临床工作流程。在这篇文章中,我们提出了框架，技术基础,以及方法论的功能应用，尤其是血管内成像。使用动脉粥样硬化病变动脉的注释图像训练条件生成对抗网络，以根据指定的斑块形态生成合成光学相干断层扫描和血管内超声图像。利用这种独特而灵活的结构的系统，一对神经网络被竞争地串联训练，可以快速生成有用的图像。这些合成图像复制了风格，在几个方面超越了内容和功能，正常采集的图像。通过使用这种技术并在此类应用程序中使用AI，可以改善图像质量方面的挑战，可解释性,连贯性,完整性，和粒度，从而加强医学教育和临床决策。
