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Artificial intelligence approaches to improve kidney care

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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.


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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.).

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Corresponding author

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).

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