Universität zu Köln

Becker, Jan U., Mayerich, David, Padmanabhan, Meghana, Barratt, Jonathan ORCID: 0000-0002-9063-7229, Ernst, Angela, Boor, Peter, Cicalese, Pietro A., Mohan, Chandra, Nguyen, Hien V. and Roysam, Badrinath (2020). Arti fi cial intelligence and machine learning in. Kidney Int., 98 (1). S. 65 - 76. NEW YORK: ELSEVIER SCIENCE INC. ISSN 1523-1755

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Abstract

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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Becker, Jan U. UNSPECIFIED UNSPECIFIED UNSPECIFIED Mayerich, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Padmanabhan, Meghana UNSPECIFIED UNSPECIFIED UNSPECIFIED Barratt, Jonathan UNSPECIFIED orcid.org/0000-0002-9063-7229 UNSPECIFIED Ernst, Angela UNSPECIFIED UNSPECIFIED UNSPECIFIED Boor, Peter UNSPECIFIED UNSPECIFIED UNSPECIFIED Cicalese, Pietro A. UNSPECIFIED UNSPECIFIED UNSPECIFIED Mohan, Chandra UNSPECIFIED UNSPECIFIED UNSPECIFIED Nguyen, Hien V. UNSPECIFIED UNSPECIFIED UNSPECIFIED Roysam, Badrinath UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-328262
Journal or Publication Title:	Kidney Int.
Volume:	98
Number:	1
Page Range:	S. 65 - 76
Date:	2020
Publisher:	ELSEVIER SCIENCE INC
Place of Publication:	NEW YORK
ISSN:	1523-1755
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language CELL-MEDIATED REJECTION; OXFORD CLASSIFICATION; LUPUS NEPHRITIS; NEURAL-NETWORK; INTEROBSERVER AGREEMENT; PRECISION MEDICINE; PATHOLOGY; MANAGEMENT; DEFINITIONS; ASSOCIATION Multiple languages Urology & Nephrology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/32826