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Review
. 2022 May 1;31(3):251-257.
doi: 10.1097/MNH.0000000000000784. Epub 2022 Feb 14.

The potential of artificial intelligence-based applications in kidney pathology

Roman D Büllow  1 , Jon N Marsh  2 , S Joshua Swamidass  2 , Joseph P Gaut  2 , Peter Boor  1   3   4
Affiliations
Review

The potential of artificial intelligence-based applications in kidney pathology

Roman D Büllow et al. Curr Opin Nephrol Hypertens. .

Abstract

Purpose of review: The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence [AI]) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice.

Recent findings: Through a multidisciplinary approach, nephropathologists, and computer scientists have made significant recent advances in developing AI technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology.

Summary: AI applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.

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Conflict of interest statement

Conflicts of interest

PB and RDB have nothing to disclose. JG, SJS, and JNM may receive royalty income based on digital image analysis technology developed by JG, JNM, and SJS and licensed by Washington University to PlatformSTL. SJS has an equity [ownership] interest in PlatformSTL.

References

    1. Loupy A, Haas M, Roufosse C, Naesens M, Adam B, Afrouzian M, Akalin E, Alachkar N, Bagnasco S, Becker JU, et al.: The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell- and antibody-mediated rejection. Am J Transplant Off J Am Soc Transplant Am Soc Transpl Surg 2020, 20:2318–2331. - PMC - PubMed
    1. Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UGJ: Digital pathology and computational image analysis in nephropathology. Nat Rev Nephrol 2020, 16:669–685. - PMC - PubMed
    1. Bülow RD, Dimitrov D, Boor P, Saez-Rodriguez J: How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade? Semin Immunopathol 2021, 43:739–752. - PMC - PubMed
    1. LeCun Y, Bengio Y, Hinton G: Deep learning. Nature 2015, 521:436–444. - PubMed
    1. Srinidhi CL, Ciga O, Martel AL: Deep neural network models for computational histopathology: A survey. Med Image Anal 2021, 67:101813. - PMC - PubMed

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