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基于对应的预测不确定性 (CPU) 指数:概念验证,用于提高低剂量查CT肺癌的特异性

Yuqi Wang¹, Aarzu Gupta¹, Fakrul Islam Tushar¹

¹Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America.

Artificial intelligence in medicine

|December 25, 2024

概括

我们开发了一种新的基于一致性的预测不确定性 (CPU) 指数,以改善肺癌查 (LCS) 风险评估. 这种人工智能驱动的工具比传统方法提高了预测的准确性和可靠性.

科学领域:

医疗成像医学成像
人工智能的人工智能
无线电学 (Radiomics) 是一种辐射学.

背景情况:

肺癌查 (LCS) 依赖于风险评估工具,如肺CT查报告和数据系统 (Lung-RADS),这些工具在个体变异性和预定义特征上有局限性.
个性化的AI时间到事件模型提供先进的预测,但可能会受到透明度问题和偏见的影响,特别是在审查数据的情况下.

研究的目的:

引入一种新的基于一致性的预测不确定性 (CPU) 指数,该指数集成了子组分析和个性化的AI时间到事件模型.
通过评估偏差差异权衡,提高肺癌风险评估的透明度,可靠性和准确性.

主要方法:

通过使用一致性指数和定义的相似性损失来测量子组分析和AI时间到事件模型之间的预测一致性,开发了CPU-Index.
应用了CPU-Index来完善AI时间到事件模型的预测,该模型以合低剂量CT (LDCT) 放射学和患者人口统计学进行训练.
利用深度学习模型 (MONAI) 来检测LDCT上的病变,提取放射性特征,并将它们与位置编码的人口数据合并.

主要成果:

整合放射学和人口统计学的人工智能模型实现了从0.81 ± 0.04到0.89 ± 0.02的改善曲线下的面积 (AUC).
提出的方法将假阳性率从0.41±0.02降低到0.30±0.12,同时保持假阴性率.
与标准的肺RADS相比,CPU-Index框架在风险预测中显示出更高的透明度和可靠性.

结论:

通过量化预测不确定性和提高预测准确度,CPU-Index有效地提高了肺癌风险评估.
放射学和患者人口统计学的最佳融合显著提高了诊断性能,突出了它们的互补作用.
这种方法为肺癌风险分层提供了更加细致和可靠的方法.

关键词:

肺癌查肺癌查肺癌查个性化的人工智能个性化的不确定性量化量化.亚组分析亚组分析时间到事件的时间.

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