Artificial intelligence and machine learning in nephropathology.
Artificial intelligence and machine learning in nephropathology.
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DOI：
10.1016/j.kint.2020.02.027
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发表时间：
2020-07
影响因子：
19.6
通讯作者：
Roysam B
中科院分区：
文献类型：
--
作者：
Becker JU;Mayerich D;Padmanabhan M;Barratt J;Ernst A;Boor P;Cicalese PA;Mohan C;Nguyen HV;Roysam B
本综述中的人工智能（AI）是一个总括术语，用于模拟肾脏病理学家从自体或移植肾活检中提取诊断、预后和治疗反应信息的能力。虽然AI可用于分析各种活检相关数据，但本文主要关注传统上用于肾脏病理学的全切片图像。人工智能在肾脏病理学中的应用最近通过几种先进的技术变得可用，包括（i）广泛引入载玻片扫描仪，（ii）全球病理学部门的数据服务器，以及（iii）通过大大改进的计算机硬件来实现人工智能培训。在这篇综述中，我们解释了人工智能如何使用先进的架构（如卷积神经网络）在精准医学的背景下提高某些参数的肾脏病理学结果的再现性，这些架构是目前机器学习软件中最先进的技术。由于AI在肾脏病理学中的应用仍处于起步阶段，我们主要以肿瘤病理学为例展示了AI应用的力量和潜力。此外，我们还讨论了技术障碍以及当前利益相关者和监管机构对从肾脏病理学家和更广泛的肾脏学社区的角度开发肾脏病理学中AI应用的担忧。我们预计这些技术将逐步引入常规诊断和选择性任务的研究中，这表明这项技术将提高肾脏病理学家的表现，而不是使他们变得多余。
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.
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影响因子：
27.4
作者：
Bertsias GK;Tektonidou M;Amoura Z;Aringer M;Bajema I;Berden JH;Boletis J;Cervera R;Dörner T;Doria A;Ferrario F;Floege J;Houssiau FA;Ioannidis JP;Isenberg DA;Kallenberg CG;Lightstone L;Marks SD;Martini A;Moroni G;Neumann I;Praga M;Schneider M;Starra A;Tesar V;Vasconcelos C;van Vollenhoven RF;Zakharova H;Haubitz M;Gordon C;Jayne D;Boumpas DT;European League Against Rheumatism and European Renal Association-European Dialysis and Transplant Association
通讯作者：
European League Against Rheumatism and European Renal Association-European Dialysis and Transplant Association
影响因子：
0.6
作者：
Albarqouni, Shadi
通讯作者：
Albarqouni, Shadi
影响因子：
6.1
作者：
Bellur, Shubha S.;Roberts, Ian S. D.;Tardanico, Regina
通讯作者：
Tardanico, Regina
影响因子：
5.6
作者：
Furness, PN;Taub, N;Rosenthal, R
通讯作者：
Rosenthal, R
DOI：
10.1109/tpami.2017.2656884
发表时间：
2018-05
影响因子：
23.6
作者：
Chang H;Han J;Zhong C;Snijders AM;Mao JH
通讯作者：
Mao JH