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Review
. 2020 May;29(3):265-272.
doi: 10.1097/MNH.0000000000000598.

Artificial intelligence driven next-generation renal histomorphometry

Briana A Santo  1 , Avi Z Rosenberg, Pinaki Sarder
Affiliations
Review

Artificial intelligence driven next-generation renal histomorphometry

Briana A Santo et al. Curr Opin Nephrol Hypertens. 2020 May.

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.

Recent findings: 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.

Summary: 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.

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Conflict of interest statement

Conflicts of interest

There are no conflicts of interest.

Figures

FIGURE 1.
FIGURE 1.
Clinical and computational workflow for artificial intelligence driven assessment of digital renal pathology. Following data acquisition and preparation, classical and modern artificial intelligence approaches provide novel insights for patient assessment and medical image analysis.
See this image and copyright information in PMC

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