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Artificial intelligence driven next-generation renal histomorphometry.
Santo BA, Rosenberg AZ, Sarder P
Pinaki Sarder on 5/7/2020
Current opinion in nephrology and hypertension
Volume : Pages
29 : 265 - 272
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
University of Florida
Neuropathy & Neurocognition
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Please acknowledge all posters, manuscripts or scientific materials that were generated in part or whole using funds from the Diabetic Complications Consortium(DiaComp) using the following text:
Financial support for this work provided by the NIDDK Diabetic Complications Consortium (RRID:SCR_001415, www.diacomp.org), grants DK076169 and DK115255
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