ARTIFICIAL INTELLIGENCE IN NEPHROLOGY IN 2019

Artificial intelligence approaches to improve kidney care

Artificial intelligence is increasingly being used to improve diagnosis and prognostication for acute and chronic kidney diseases. Studies with this objective published in 2019 relied on a variety of available data sources, including electronic health records, intraoperative physiological signals, kidney ultrasound imaging, and digitized biopsy specimens.

Key advances

  • A deep recurrent neural network model using data from electronic health records enables the prediction of inpatient episodes of acute kidney injury (AKI) with lead times of up to 48 hours5.

  • Integrating intraoperative physiological signals into an AKI risk model that dynamically integrates preoperative and intraoperative data improves the prediction of postoperative AKI6.

  • A convolutional deep learning model enables the noninvasive classification of chronic kidney disease stage and estimated glomerular filtration rate using kidney ultrasound images8.

  • A convolutional neural network trained for multiclass segmentation enables automated analysis of transplant biopsy and nephrectomy samples9.

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Fig. 1: A conceptual framework for the future use of artificial intelligence in nephrology.

Change history

  • 13 January 2020

    In the original html and PDF versions of this article published online, the 2 in 1.73 m2 was incorrectly formatted as a reference citation. This error has been corrected in print and online.

References

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    United States Department of Health and Human Services. Advancing American kidney health. HHS https://aspe.hhs.gov/system/files/pdf/262046/AdvancingAmericanKidneyHealth.pdf (2019).

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    Vinyals, O. et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575, 350–354 (2019).

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    American Medical Association. Augmented intelligence in health care. AMA https://www.ama-assn.org/system/files/2019-08/ai-2018-board-policy-summary.pdf (2019).

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    Al-Jaghbeer, M. et al. Clinical decision support for in-hospital AKI. J. Am. Soc. Nephrol. 29, 654–660 (2018).

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    Tomašev, N. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019).

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    Adhikari, L. et al. Improved predictive models for acute kidney injury with IDEA: intraoperative data embedded analytics. PLOS ONE 14, e0214904 (2019).

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    Bihorac, A. et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann. Surg. 269, 652–662 (2019).

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    Kuo, C.-C. et al. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit. Med. 2, 29 (2019).

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    Hermsen, M. et al. Deep learning-based histopathologic assessment of kidney tissue. J. Am. Soc. Nephrol. 30, 1968–1979 (2019).

  10. 10.

    Davoudi, A. et al. Intelligent ICU for autonomous patient monitoring using pervasive sensing and deep learning. Sci. Rep. 9, 8020 (2019).

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Acknowledgements

The authors received research funding from the NIH (R21 EB027344 and R01GM110240 awarded to A.B.; R21 EB027344 and R01GM110240 awarded to P.R.) and the National Science Foundation (Career IIS 1750192 awarded to P.R.).

Author information

Correspondence to Azra Bihorac.

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Competing interests

The University of Florida has two pending patent applications related to some of algorithms published in the highlighted articles: Bihorac, A., Li, X., Rashidi, P., Pardalos, P., Ozrazgat-Baslanti, T., Hogan, W., Wang, D. Z., Momcilovic, P. & Lipori, G. Method and apparatus for prediction of complications after surgery. Appl. No. PCT/IB2018/053956, 1 June 2018, A&B ref. 049648/514983, UF ref.16671; Rashidi, P., Bihorac, A. & Tighe, P. J. Method and apparatus for pervasive patient monitoring. Nonprovisional Appl. No. 16/388,351, 18 April 2019, A&B ref. 049648/529839, UF ref.17317.

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Rashidi, P., Bihorac, A. Artificial intelligence approaches to improve kidney care. Nat Rev Nephrol 16, 71–72 (2020). https://doi.org/10.1038/s41581-019-0243-3

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