Identifying images in the biology literature that are problematic for people with a color-vision deficiency

Abstract

AbstractTo help maximize the impact of scientific journal articles, authors must ensure that article figures are accessible to people with color-vision deficiencies. Up to 8% of males and 0.5% of females experience a color-vision deficiency. For deuteranopia, the most common color-vision deficiency, we evaluated images published in biology-oriented research articles between 2012 and 2022. Out of 66,253 images, 56,816 (85.6%) included at least one color contrast that could be problematic for people with moderate-to-severe deuteranopia (“deuteranopes”). However, after informal evaluations, we concluded that spatial distances and within-image labels frequently mitigated potential problems. We systematically reviewed 4,964 images, comparing each against a simulated version that approximates how it appears to deuteranopes. We identified 636 (12.8%) images that would be difficult for deuteranopes to interpret. Although still prevalent, the frequency of this problem has decreased over time. Articles from cell-oriented biology subdisciplines were most likely to be problematic. We used machine-learning algorithms to automate the identification of problematic images. For a hold-out test set of 879 additional images, a convolutional neural network classified images with an area under the receiver operating characteristic curve of 0.89. To enable others to apply this model, we created a Web application where users can upload images, view deuteranopia-simulated versions, and obtain predictions about whether the images are problematic. Such efforts are critical to ensuring the biology literature is interpretable to diverse audiences.