Digital pathology and computational image analysis in nephropathology

Laura Barisoni, Kyle J. Lafata, Stephen M. Hewitt, Anant Madabhushi, Ulysses G.J. Balis

Research output: Contribution to journalReview articlepeer-review

Abstract

The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

Original languageEnglish (US)
Pages (from-to)669-685
Number of pages17
JournalNature Reviews Nephrology
Volume16
Issue number11
DOIs
StatePublished - Nov 1 2020

ASJC Scopus subject areas

  • Nephrology

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