Open AccessArtificial intelligence driven next-generation renal histomorphometry
Author(s) -
Briana A. Santo,
Avi Z. Rosenberg,
Pinaki Sarder
Publication year - 2020
Publication title -
current opinion in nephrology and hypertension/current opinion in nephrology and hypertension, with evaluated medline
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.158
H-Index - 95
eISSN - 1080-8221
pISSN - 1062-4821
DOI - 10.1097/mnh.0000000000000598
Subject(s) - artificial intelligence , computer science , machine learning , digital pathology , workflow , extant taxon , database , evolutionary biology , biology
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.