Becker Jan U, Mayerich David, Padmanabhan Meghana, Barratt Jonathan, Ernst Angela, Boor Peter, Cicalese Pietro A, Mohan Chandra, Nguyen Hien V, Roysam Badrinath
Institute of Pathology, University Hospital of Cologne, Cologne, Germany.
Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, USA.
Kidney Int. 2020 Jul;98(1):65-75. doi: 10.1016/j.kint.2020.02.027. Epub 2020 Apr 1.
Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.
就本综述而言，人工智能（AI）是一个统称，涵盖了各种技术，这些技术旨在模拟肾病理学家从原发性或移植肾活检中提取有关诊断、预后和治疗反应信息的能力。尽管人工智能可用于分析各种与活检相关的数据，但本综述重点关注肾病理学中传统使用的全切片图像。肾病理学中的人工智能应用最近已通过多种先进技术得以实现，包括：（i）玻片扫描仪的广泛应用；（ii）全球病理科的数据服务器；以及（iii）通过大幅改进的计算机硬件以实现人工智能训练。在本综述中，我们将解释在精准医学背景下，人工智能如何利用先进架构（如卷积神经网络，这是目前用于此任务的机器学习软件的最新技术）提高某些参数的肾病理学结果的可重复性。由于肾病理学中的人工智能应用仍处于起步阶段，我们主要以肿瘤病理学为例展示人工智能应用的力量和潜力。此外，我们从肾病理学家和更广泛的肾脏病学界的角度，讨论了肾病理学中开发人工智能应用的技术障碍以及当前利益相关者和监管方面的担忧。我们预计这些技术将逐步引入常规诊断和针对特定任务的研究中，这表明该技术将提高肾病理学家的工作效率，而不是使他们变得多余。
