Cothren Steven D, Meyer Joel N, Hartman Jessica H
Solibyte Solutions, Durham, NC, USA.
Nicholas School of the Environment, Duke University, Durham, NC, USA.
Bio Protoc. 2018 Dec 5;8(23). doi: 10.21769/BioProtoc.3103.
In nearly all subfields of biomedical sciences, there are phenotypes that are currently classified by expert visual scoring. In research applications, these classifications require the experimenter to be blinded to the treatment group in order to avoid unintentional bias in scoring. Currently, many labs either use laborious and tedious methods to manually blind the images, require multiple experimenters to gather and score the data blindly or fail to properly blind the data altogether. In this protocol, we present a simple, freely available software that we created that allows the experimenter to blindly score images. In our protocol, the user loads unblinded images and defines a scoring system. The software then shows the user the images in a random order, allowing the user to select a score from their defined scoring system for each image. Furthermore, the software has an optional "quality control" mechanism where the user will be shown some images multiple times to test the robustness of the visual scoring. Finally, the software summarizes the results in an exportable file that includes unblinded summary data for each group and a full list of images with their scores. In this protocol, we briefly present directions for using the software, potential applications, and caveats/limitations to this approach.
在生物医学科学的几乎所有子领域中,都存在目前通过专家视觉评分进行分类的表型。在研究应用中,这些分类要求实验者对治疗组不知情,以避免评分时出现无意的偏差。目前,许多实验室要么使用费力且繁琐的方法手动对图像进行盲法处理,要么要求多个实验者盲目收集和评分数据,要么完全没有对数据进行适当的盲法处理。在本方案中,我们介绍一种我们创建的简单且免费的软件,该软件可让实验者对图像进行盲法评分。在我们的方案中,用户加载未盲法处理的图像并定义评分系统。然后,软件以随机顺序向用户展示图像,允许用户从其定义的评分系统中为每张图像选择一个分数。此外,该软件有一个可选的“质量控制”机制,会多次向用户展示一些图像,以测试视觉评分的稳健性。最后,软件将结果汇总到一个可导出的文件中,该文件包括每组未盲法处理的汇总数据以及带有分数的完整图像列表。在本方案中,我们简要介绍了使用该软件的说明、潜在应用以及这种方法的注意事项/局限性。