PDF(Pubmed)
Abstract：
Artificial intelligence (AI) is increasingly used in genomics research and practice, and generative AI has garnered significant recent attention. In clinical applications of generative AI, aspects of the underlying datasets can impact results, and confounders should be studied and mitigated. One example involves the facial expressions of people with genetic conditions. Stereotypically, Williams (WS) and Angelman (AS) syndromes are associated with a \"happy\" demeanor, including a smiling expression. Clinical geneticists may be more likely to identify these conditions in images of smiling individuals. To study the impact of facial expression, we analyzed publicly available facial images of approximately 3500 individuals with genetic conditions. Using a deep learning (DL) image classifier, we found that WS and AS images with non-smiling expressions had significantly lower prediction probabilities for the correct syndrome labels than those with smiling expressions. This was not seen for 22q11.2 deletion and Noonan syndromes, which are not associated with a smiling expression. To further explore the effect of facial expressions, we computationally altered the facial expressions for these images. We trained HyperStyle, a GAN-inversion technique compatible with StyleGAN2, to determine the vector representations of our images. Then, following the concept of InterfaceGAN, we edited these vectors to recreate the original images in a phenotypically accurate way but with a different facial expression. Through online surveys and an eye-tracking experiment, we examined how altered facial expressions affect the performance of human experts. We overall found that facial expression is associated with diagnostic accuracy variably in different genetic conditions.
摘要：
人工智能（AI）越来越多地用于基因组学研究和实践，而生成AI最近引起了极大的关注。在生成式AI的临床应用中，基础数据集的各个方面可能会影响结果，和混杂因素应该研究和减轻。一个例子涉及具有遗传条件的人的面部表情。陈规定型观念,威廉姆斯(WS)和安格曼(AS)综合征与“快乐”的举止有关，包括微笑的表情。临床遗传学家可能更有可能在微笑个体的图像中识别这些状况。为了研究面部表情的影响，我们分析了大约3500名患有遗传疾病的个体的公开面部图像.使用深度学习(DL)图像分类器，我们发现,非微笑表情的WS和AS图像对正确综合征标签的预测概率明显低于微笑表情的图像.22q11.2缺失和Noonan综合征没有看到这种情况，与微笑的表情无关。为了进一步探索面部表情的影响，我们通过计算改变了这些图像的面部表情。我们训练过HyperStyle,与StyleGAN2兼容的GAN反演技术，以确定我们图像的矢量表示。然后，遵循InterfaceGAN的概念，我们编辑了这些向量，以表型准确的方式重新创建原始图像，但面部表情不同。通过在线调查和眼动追踪实验，我们研究了面部表情的改变如何影响人类专家的表现。我们总体上发现，在不同的遗传条件下，面部表情与诊断准确性相关。
