Journal
KIDNEY INTERNATIONAL
Volume 98, Issue 1, Pages 65-75Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.kint.2020.02.027
Keywords
artificial intelligence; computer; convolutional neural network; image recognition; nephropathology
Categories
Funding
- Deutsche Forschungsgemeinschaft [BE-3801/3]
- German Research Foundation (DFG) [SFB/TRR57, SFB/TRR219, BO3755/3-1, BO3755/6-1]
- German Federal Ministry of Education and Research (BMBF) [STOP-FSGS-01GM1901A]
- RWTH Interdisciplinary Centre for Clinical Research (IZKF) [O3-7]
- National Institutes of Health/National Heart, Lung, and Blood Institute [R01HL146745]
- Cancer Prevention and Research Institute of Texas [RR140013]
- European Rare Kidney Disease Network (ERKNet) [739532]
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Arti ficial 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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