Artificial intelligence driven next-generation renal histomorphometry.
Authors Santo BA, Rosenberg AZ, Sarder P
Submitted By Pinaki Sarder on 5/7/2020
Status Published
Journal Current opinion in nephrology and hypertension
Year 2020
Date Published
Volume : Pages 29 : 265 - 272
PubMed Reference 32205581
Abstract 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., The primordial digital pathology informatics work
employed classical image analysis and machine learning to prognosticate renal
disease. Although this classical approach demonstrated tremendous potential,
subsequent advancements in hardware technology rendered artificial neural
networks '(ANNs) the method of choice for machine vision in computational
pathology'. Offering rapid and reproducible detection, characterization and
classification of kidney morphology, ANNs have facilitated the development of
diagnostic and prognostic applications. In addition, modern machine learning
with ANNs has revealed novel biomarkers in kidney disease, demonstrating the
potential for machine vision to elucidate novel pathologic mechanisms beyond
extant clinical knowledge., Despite the revolutionary developments potentiated
by modern machine learning, several challenges remain, including data quality
control and curation, image annotation and ontology, integration of multimodal
data and interpretation of machine vision or 'opening the black box'. Resolution
of these challenges will not only revolutionize diagnostic pathology but also
pave the way for precision medicine and integration of artificial intelligence
in the process of care.


Investigators with authorship
NameInstitution
Pinaki SarderSUNY at Buffalo

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