# Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation

## TL;DR

This paper introduces a machine learning tool to automate the analysis of brain arteriolosclerosis, improving accuracy and reliability in neuropathology.

## Contribution

The novel contribution is an end-to-end ML algorithm (ArtSeg and VasMorph) for automated morphometric assessment of brain arteriolosclerosis.

## Key findings

- ArtSeg achieved AUC-ROC values of 0.79 (internal) and 0.77 (external) for blood vessel detection.
- VasMorph successfully derived sclerotic indices and vessel wall metrics comparable to expert assessments.

## Abstract

Objective quantification of brain arteriolosclerosis remains an area of ongoing
refinement in neuropathology, with current methods primarily utilizing
semi-quantitative scales completed through manual histological examination.
These approaches offer modest inter-rater reliability and do not provide precise
quantitative metrics. To address this gap, we present a prototype end-to-end
machine learning (ML)-based algorithm, Arteriolosclerosis Segmentation (ArtSeg),
followed by Vascular Morphometry (VasMorph) – to assist persons in the
morphometric analysis of arteriolosclerotic vessels on whole slide images
(WSIs). We digitized hematoxylin and eosin-stained glass slides (13
participants, total 42 WSIs) of human brain frontal or occipital lobe cortical
and/or periventricular white matter collected from three brain banks (University
of California, Davis, Irvine, and Los Angeles Alzheimer’s Disease Research
Centers). ArtSeg comprises three ML models for blood vessel detection,
arteriolosclerosis classification, and segmentation of arteriolosclerotic vessel
walls and lumens. For blood vessel detection, ArtSeg achieved area under the
receiver operating characteristic curve (AUC-ROC) values of 0.79 (internal
hold-out testing) and 0.77 (external testing), Dice scores of 0.56 (internal
hold-out) and 0.74 (external), and Hausdorff distances of 2.53 (internal
hold-out) and 2.15 (external). Arteriolosclerosis classification demonstrated
accuracies of 0.94 (mean, 3-fold cross-validation), 0.86 (internal hold-out),
and 0.77 (external), alongside AUC-ROC values of 0.69 (mean, 3-fold
cross-validation), 0.87 (internal hold-out), and 0.83 (external). For
arteriolosclerotic vessel segmentation, ArtSeg yielded Dice scores of 0.68
(mean, 3-fold cross-validation), 0.73 (internal hold-out), and 0.71 (external);
Hausdorff distances of 7.63 (mean, 3-fold cross-validation), 6.93 (internal
hold-out), and 7.80 (external); and AUC-ROC values of 0.90 (mean, 3-fold
cross-validation), 0.92 (internal hold-out), and 0.87 (external). VasMorph
successfully derived sclerotic indices, vessel wall thicknesses, and vessel wall
to lumen area ratios from ArtSeg-segmented vessels, producing results comparable
to expert assessment. This integrated approach shows promise as an assistive
tool to enhance current neuropathological evaluation of brain
arteriolosclerosis, offering potential for improved inter-rater reliability and
quantification.