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Applicability of digital analysis and imaging technology in neuropathology assessment
Author(s) -
Dunn William D.,
Gearing Marla,
Park Yuna,
Zhang Lifan,
Hanfelt John,
Glass Jonathan D.,
Gutman David A.
Publication year - 2016
Publication title -
neuropathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.701
H-Index - 61
eISSN - 1440-1789
pISSN - 0919-6544
DOI - 10.1111/neup.12273
Subject(s) - neuropathology , categorical variable , dementia , medicine , pathology , artificial intelligence , computer science , disease , machine learning
Alzheimer's disease (AD) is a progressive neurological disorder that affects more than 30 million people worldwide. While various dementia‐related losses in cognitive functioning are its hallmark clinical symptoms, ultimate diagnosis is based on manual neuropathological assessments using various schemas, including Braak staging, C ERAD (Consortium to Establish a Registry for Alzheimer's Disease) and Thal phase scoring. Since these scoring systems are based on subjective assessment, there is inevitably some degree of variation between readers, which could affect ultimate neuropathology diagnosis. Here, we report a pilot study investigating the applicability of computer‐driven image analysis for characterizing neuropathological features, as well as its potential to supplement or even replace manually derived ratings commonly performed in medical settings. In this work, we quantitatively measured amyloid beta (Aβ) plaque in various brain regions from 34 patients using a robust digital quantification algorithm. We next verified these digitally derived measures to the manually derived pathology ratings using correlation and ordinal logistic regression methods, while also investigating the association with other AD‐related neuropathology scoring schema commonly used at autopsy, such as Braak and CERAD. In addition to successfully verifying our digital measurements of Aβ plaques with respective categorical measurements, we found significant correlations with most AD‐related scoring schemas. Our results demonstrate the potential for digital analysis to be adapted to more complex staining procedures commonly used in neuropathological diagnosis. As the efficiency of scanning and digital analysis of histology images increases, we believe that the basis of our semi‐automatic approach may better standardize quantification of neuropathological changes and AD diagnosis, ultimately leading to a more comprehensive understanding of neurological disorders and more efficient patient care.