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通过基础模型提取的成像特征重新识别患者

Giacomo Nebbia¹, Sourav Kumar¹, Stephen Michael McNamara¹

¹Ophthalmology Department, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

NPJ digital medicine

|July 22, 2025

概括

医学成像的基础模型存在重新识别的风险. 图像特征可以揭示患者身份和人口统计数据,影响眼科和放射学中的隐私.

科学领域:

人工智能的人工智能
医疗成像医学成像
数据隐私数据隐私数据

背景情况:

基础模型越来越多地用于医学成像.
潜在的隐私风险,例如患者重新识别,尚未得到充分了解.

研究的目的:

评估基础模型中的成像特征是否能够重新识别患者.
确定重新识别是否与人口特征预测相关.

主要方法:

使用了色底相片 (CFP),光学一致性断层扫描 (OCT) b扫描和胸部X射线.
训练有素的深度学习模型来评估重新识别和人口预测性能.

主要成果:

报告的患者重新识别率为40.3% (CFP),46.3% (OCT) 和25.9% (X射线).
根据重新识别状态,证明了不同的人口预测性能.
实现了高图像级重识别性能 (82.3%的CFP,93.9%的OCT,63.7%的X射线).

结论:

医学基础模型中的成像特征包含了使患者重新识别的信息.
这些发现突出了人工智能驱动的医学成像中的重大隐私问题.

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