3D CT影像的自监督嵌入能准确预测年龄和性别，但需警惕潜在隐私风险。
Demographic Predictability in 3D CT Foundation Embeddings
- 用自监督模型提取3D CT图像特征，再用分类器预测患者信息
- 年龄预测误差仅3.8年，性别分类准确率高达99.8%
- 种族预测效果较差，提示需关注模型公平性与隐私保护
自监督基础模型已被成功应用于三维（3D）计算机断层扫描（CT）图像编码，在颅内出血检测和肺癌风险预测等下游任务中表现优异。然而，由于自监督模型从复杂数据分布中学习，其嵌入是否包含年龄、性别或种族等人口统计学信息成为关注焦点。本研究基于包含3D CT图像与人口统计信息的国家肺癌筛查试验（NLST）数据集，评估了多种分类器：软最大回归、线性回归、线性支持向量机、随机森林和决策树，用于预测患者的性别、种族和年龄。结果表明，嵌入能有效编码年龄和性别信息：线性回归模型在年龄预测上达到3.8年均方根误差（RMSE），软最大回归模型在性别分类上实现0.998的AUC。种族预测表现较弱，AUC为0.878。这些发现提示，必须深入探究自监督学习框架中编码的信息，以确保医疗AI的公平性、责任性和患者隐私保护。
原文摘要 · Abstract (English)
Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with excellent performance across several downstream tasks, such as intracranial hemorrhage detection and lung cancer risk forecasting. However, as self-supervised models learn from complex data distributions, questions arise concerning whether these embeddings capture demographic information, such as age, sex, or race. Using the National Lung Screening Trial (NLST) dataset, which contains 3D CT images and demographic data, we evaluated a range of classifiers: softmax regression, linear regression, linear support vector machine, random forest, and decision tree, to predict sex, race, and age of the patients in the images. Our results indicate that the embeddings effectively encoded age and sex information, with a linear regression model achieving a root mean square error (RMSE) of 3.8 years for age prediction and a softmax regression model attaining an AUC of 0.998 for sex classification. Race prediction was less effective, with an AUC of 0.878. These findings suggest a detailed exploration into the information encoded in self-supervised learning frameworks is needed to help ensure fair, responsible, and patient privacy-protected healthcare AI.
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