Artificial intelligence driven next-generation renal histomorphometry.

Journal: Current opinion in nephrology and hypertension

Published Date: May 1, 2020

Abstract

PURPOSE OF REVIEW: Successful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications.

Authors

Briana A Santo

Department of Pathology and Anatomical Sciences, The State University of New York, Buffalo, New York Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Avi Z Rosenberg
Pinaki Sarder

Keywords

Artificial Intelligence Humans Image Processing, Computer-Assisted Kidney Machine Learning Neural Networks, Computer Prognosis

External Resources

View on PubMed Access via DOI PubMed (32205581)

Artificial intelligence driven next-generation renal histomorphometry.

Abstract

Authors

Keywords

External Resources

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