Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Not Color Blind: AI Predicts Racial Identity from Black and White
  Retinal Vessel Segmentations   Background: Artificial intelligence (AI) may demonstrate racial bias when
skin or choroidal pigmentation is present in medical images. Recent studies
have shown that convolutional neural networks (CNNs) can predict race from
images that were not previously thought to contain race-specific features. We
evaluate whether grayscale retinal vessel maps (RVMs) of patients screened for
retinopathy of prematurity (ROP) contain race-specific features.
  Methods: 4095 retinal fundus images (RFIs) were collected from 245 Black and
White infants. A U-Net generated RVMs from RFIs, which were subsequently
thresholded, binarized, or skeletonized. To determine whether RVM differences
between Black and White eyes were physiological, CNNs were trained to predict
race from color RFIs, raw RVMs, and thresholded, binarized, or skeletonized
RVMs. Area under the precision-recall curve (AUC-PR) was evaluated.
  Findings: CNNs predicted race from RFIs near perfectly (image-level AUC-PR:
0.999, subject-level AUC-PR: 1.000). Raw RVMs were almost as informative as
color RFIs (image-level AUC-PR: 0.938, subject-level AUC-PR: 0.995).
Ultimately, CNNs were able to detect whether RFIs or RVMs were from Black or
White babies, regardless of whether images contained color, vessel segmentation
brightness differences were nullified, or vessel segmentation widths were
normalized.
  Interpretation: AI can detect race from grayscale RVMs that were not thought
to contain racial information. Two potential explanations for these findings
are that: retinal vessels physiologically differ between Black and White babies
or the U-Net segments the retinal vasculature differently for various fundus
pigmentations. Either way, the implications remain the same: AI algorithms have
potential to demonstrate racial bias in practice, even when preliminary
attempts to remove such information from the underlying images appear to be
successful.

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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an