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中华肾病研究电子杂志 ›› 2020, Vol. 09 ›› Issue (03) : 135 -137. doi: 10.3877/cma.j.issn.2095-3216.2020.03.010

所属专题: 文献

综述

人工智能在肾脏病理诊断中的应用
卓莉1, 邹古明1, 李文歌1,()   
  1. 1. 100029 北京,中日友好医院肾内科
  • 收稿日期:2019-07-29 出版日期:2020-06-28
  • 通信作者: 李文歌
  • 基金资助:
    国家自然科学基金(81870495)

Application of artificial intelligence in renal pathological diagnosis

Li Zhuo1, Guming Zou1, Wenge Li1,()   

  1. 1. Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China
  • Received:2019-07-29 Published:2020-06-28
  • Corresponding author: Wenge Li
  • About author:
    Corresponding author: Li Wenge, Email:
引用本文:

卓莉, 邹古明, 李文歌. 人工智能在肾脏病理诊断中的应用[J/OL]. 中华肾病研究电子杂志, 2020, 09(03): 135-137.

Li Zhuo, Guming Zou, Wenge Li. Application of artificial intelligence in renal pathological diagnosis[J/OL]. Chinese Journal of Kidney Disease Investigation(Electronic Edition), 2020, 09(03): 135-137.

随着计算机技术和互联网的高速发展,基于深度学习的人工智能(artificial intelligence, AI)已经影响了病理学以及与其相关行业的工作模式。本文介绍了人工智能相关知识。由于目前肾脏病理数字切片资源有限,人工智能在肾脏组织病理学领域中的研究尚属起步阶段,但已经在肾小球数字化形态评价系统建立、预测慢性肾脏病预后方面做了很好的尝试。此外,还存在图像特征和临床资料的复杂性会影响AI诊断准确性、罕见肾脏疾病病例收集困难等情况。这些都是AI在肾脏病理诊断应用中所面临的问题,本文将逐一论述,并介绍可能的解决办法。

With the rapid development of computer technology and internet, artificial intelligence (AI) based on depth learning has affected the working mode of pathology and its related industries. This article introduced the knowledge of AI. At present, due to the limited resources of digital sections of renal pathology, the research of AI in renal histopathology is still in its infancy. Good attempts have been made to establish a digital glomerular morphological evaluation system and to predict the prognosis of chronic kidney disease. In addition, there are situations that the complexity of image features and clinical data will affect the accuracy of AI diagnosis, and that the cases collection of rare kidney diseases are difficult, etc. These are the problems faced by application of AI in the process of renal pathological diagnosis, which were discussed one by one together with their possible solutions.

图1 病理医师和人工智能专家参与病理图像人工智能识别流程图
[1]
郑闪,孙丰龙,张慧娟,等. 人工智能在肿瘤组织病理学的研究现状[J]. 中华肿瘤杂志,2018, 40(12): 885-889.
[2]
张世豪,冼丽英,高敏,等. 基于深度学习的人工智能在病理诊断的应用进展与展望[J]. 中国医学创新,2018, 15(25): 130-133.
[3]
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507.
[4]
Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features [J]. Nat Commun, 2016, 7: 12474.
[5]
邓杨,包骥. 数字病理中计算机辅助诊断研究展望[J]. 实用医院临床杂志,2017, 14(5): 10-12.
[6]
张楠,鲁海珍,应建明,等. 人工智能在诊断病理中的应用进展[J]. 诊断病理学杂志,2019, 26(3): 183-185.
[7]
许燕,汤烨,闫雯,等. 病理人工智能的现状和展望[J]. 中华病理学杂志,2017, 46(9): 593-595.
[8]
Barisoni L, Nast CC, Jennette JC, et al. Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE) [J]. Clin J Am Soc Nephrol, 2013, 8(8): 1449-1459.
[9]
Rosenberg AZ, Palmer M, Merlino L, et al. The application of digital pathology to improve accuracy in glomerular enumeration in renal biopsies [J]. PLoS One, 2016, 11(6): e0156441.
[10]
Zee J, Hodgin JB, Mariani LH, et al. Reproducibility and feasibility of strategies for morphologic assessment of renal biopsies using the nephrotic syndrome study network digital pathology scoring system [J]. Arch Pathol Lab Med, 2018, 142(5): 613-625.
[11]
Royal V, Zee J, Liu Q, et al. Ultrastructural characterization of proteinuric patients predicts clinical outcomes [J]. J Am Soc Nephrol, 2020, 31(4): 841-854.
[12]
Mariani LH, Martini S, Barisoni L, et al. Interstitial fibrosis scored on whole-slide digital imaging of kidney biopsies is a predictor of outcome in proteinuric glomerulopathies [J]. Nephrol Dial Transplant, 2018, 33(2): 310-318.
[13]
Simon O, Yacoub R, Jain S, et al. Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images [J]. Sci Rep, 2018, 8(1): 2032.
[14]
Bukowy JD, Dayton A, Cloutier D, et al. Region-based convolutional neural nets for localization of glomeruli in trichrome-stained whole kidney sections [J]. J Am Soc Nephrol, 2018, 29(8): 2081-2088.
[15]
Kolachalama VB, Singh P, Lin CQ, et al. Association of pathological fibrosis with renal survival using deep neural networks [J]. Kidney Int Rep, 2018, 3(2): 464-475.
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