Once confined to the world of science fiction, advances in information technology, particularly in computational and storage resources, have enabled use of artificial intelligence in medicine to become a reality. Two new studies report the use of deep learning — currently the most promising algorithmic artificial intelligence approach — in kidney pathology.
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References
Barisoni, L. & Hodgin, J. B. Digital pathology in nephrology clinical trials, research, and pathology practice. Curr. Opin. Nephrol. Hypertens. 26, 450–459 (2017).
Hermsen, M. et al. Deep learning-based histopathologic assessment of kidney tissue. J. Am. Soc. Nephrol. 30, 1968–1979 (2019).
Ginley, B. et al. Computational segmentation and classification of diabetic glomerulosclerosis. J. Am. Soc. Nephrol. 30, 1953–1967 (2019).
Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).
Gadermayr, M. et al. Segmenting renal whole slide images virtually without training data. Comput. Biol. Med. 90, 88–97 (2017).
Gadermayr, M. et al. Generative adversarial networks for facilitating stain-independent supervised & unsupervised segmentation: a study on kidney histology. IEEE Trans Med. Imaging 38, 2293–2302 (2019).
Bukowy, J. D. et al. Region-based convolutional neural nets for localization of glomeruli in trichrome-stained whole kidney sections. J. Am. Soc. Nephrol. 29, 2081–2088 (2018).
Gadermayr, M. et al. in Machine Learning in Medical Imaging Vol. 10019 (eds Wang, L. et al.) 18–26 (Springer, 2016).
Kather, J. N. et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLOS Med. 16, e1002730 (2019).
Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).
Acknowledgements
The author’s work is supported by grants of the German Research Foundation (DFG; SFB/TRR57 and SFB/TRR219, BO3755/3-1 and BO3755/6-1), the German Ministry of Education and Research (BMBF; STOP-FSGS-01GM1901A) and the German Ministry of Economic Affairs and Energy (BMWi; EMPAIA project).
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Boor, P. Artificial intelligence in nephropathology. Nat Rev Nephrol 16, 4–6 (2020). https://doi.org/10.1038/s41581-019-0220-x
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DOI: https://doi.org/10.1038/s41581-019-0220-x
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