使用贝叶斯神经网络基于现场复杂性特征和流动性地图的不确定性量化指导的患者特定质量保证
Xueying Yang1,2, Xiangxiang Cui3, Xile Zhang2
1School of Physics, Beihang University, Beijing 102206, People's Republic of China.
Physics in medicine and biology
|July 24, 2025
概括
本研究引入了针对患者的质量保证 (PSQA) 的AI框架,该框架量化了不确定性,减少了手工工作量并提高了安全性. 该方法通过将不确定性量化整合到AI预测中来确保可靠的自动化PSQA.
科学领域:
- 医学物理 医学物理
- 人工智能的人工智能
- 辐射瘤学 辐射瘤学
背景情况:
- 针对患者特异性质量保证 (PSQA) 的人工智能的进步需要对临床安全进行强大的不确定性量化 (UQ).
- 目前用于PSQA的AI模型需要方法来确保可靠性并指导临床决策.
研究的目的:
- 为人工智能驱动的PSQA预测开发和验证一个以不确定性为导向的框架.
- 提高自动化PSQA系统的临床安全和效率.
主要方法:
- 使用现场复杂性和流动性地图训练了一种AI分类模型,以分类PSQA结果.
- 蒙特卡洛近似贝叶斯推理用于UQ,通过正确-确定 (CC) 和不正确-不确定 (IU) 曲线定义的门.
- 一个多层感知器预测了马传递率 (GPR),整合了分类嵌入和不确定性指标.
主要成果:
- 该分类模型在不同的玛标准中显示出高灵敏度 (例如,在2%/2毫米时94.74%).
- 该框架减少了42.73%的手动干预,同时实现了3%/3毫米的100%临床灵敏度.
- 整合不确定性使"失败"病例的GPR精度提高了21.03%,前性测试显示了100%的精度,灵敏度和特异性.
结论:
- 拟议的不确定性引导框架提高了基于AI的PSQA的可靠性.
- 这种方法显著减少了手工工作量,提高了预测准确性,促进了自动化PSQA的安全临床采用.
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