Olender Max L, de la Torre Hernández José M, Athanasiou Lambros S, Nezami Farhad R, Edelman Elazer R
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA.
Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA.
Eur Heart J Digit Health. 2021 Jun 7;2(3):539-544. doi: 10.1093/ehjdh/ztab052. eCollection 2021 Sep.
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.
人工智能（AI）因其能够不懈地整合海量数据，在心脏病学乃至整个医学领域都展现出了巨大的潜力。医学成像方面的应用尤其具有吸引力，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。与心脏病学中其他专注于任务自动化和模式识别的人工智能方法不同，我们描述了一个数字健康平台，用于合成增强但仍熟悉的临床图像，以增强心脏病专家的视觉临床工作流程。在本文中，我们介绍了该方法的框架、技术基础和功能应用，特别是与血管内成像相关的部分。我们使用动脉粥样硬化病变动脉的标注图像训练了一个条件生成对抗网络，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。利用这种独特且灵活的结构（一对神经网络协同竞争训练）的系统可以快速生成有用的图像。这些合成图像复制了正常采集图像的风格，并且在多个方面超越了其内容和功能。通过在这类应用中使用该技术并运用人工智能，可以改善图像质量、可解释性、连贯性、完整性和粒度方面的挑战，从而加强医学教育和临床决策。
