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通过医学遗传学中的生成人工智能对诊断准确性的面部表情影响的近似估计

Tanviben Patel¹, Amna A Othman¹, Ömer Sümer²

¹Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research Institute, Bethesda, MA 20892, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

概括

遗传条件图像中的面部表情会影响人工智能和人类诊断. 威廉姆斯综合症和安吉尔曼综合症的非微笑面孔显示出较低的AI预测准确度,突出了临床遗传学生成AI的偏见.

科学领域:

基因组学就是基因组学.
人工智能的人工智能
临床遗传学临床遗传学

背景情况:

生成性AI在基因组学中越来越多地使用,但数据集偏差可能会影响临床应用.
遗传病患者的图像中的面部表情可能会引入偏见.
在威廉姆斯综合征 (WS) 和安吉尔曼综合征 (AS) 中,刻板印象的"快乐"行为可以影响诊断可能性.

研究的目的:

调查面部表情对人工智能驱动和人类遗传疾病诊断准确性的影响.
分析面部表情如何混人工智能模型中的诊断预测.
评估计算机改变面部表情对专家临床医生的表现的影响.

主要方法:

通过使用深度学习图像分类器,分析了约3500个患有遗传疾病的个体的面部图像.
采用了HyperStyle (GAN-倒置) 和InterfaceGAN来计算改变图像中的面部表情.
进行了在线调查和与人类专家一起的眼睛跟踪实验,以评估使用改变图像的诊断性能.

主要成果:

深度学习分类器显示,与微笑相比,非微笑表情的WS和AS图像的预测概率明显较低.
对于22q11.2删除和诺南综合征,这些综合征缺乏特征性的微笑表情,没有观察到这种效应.
人类专家在诊断遗传疾病方面的表现受到改变的面部表情的影响.

结论:

面部表情是基于AI的诊断工具中的一个重要的混因素,用于某些遗传疾病,如WS和AS.
对面部表情的计算操纵可以改变人工智能和人类专家的诊断准确性.
缓解与面部表情相关的数据集偏差对于临床遗传学中可靠的生成AI至关重要.

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