面向医疗器械制造质量控制的认知不确定性感知缺陷检测
Epistemic Uncertainty-Aware Defect Detection for Quality Control in Medical Device Manufacturing
- Hofstra University（霍夫斯特拉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出认知不确定性感知的拒绝策略，通过弃权高不确定性预测，在医疗器械缺陷检测中提升可靠性，实证显示10%弃权率降低48%错误。
AI中文摘要：
目的：我们研究考虑认知不确定性是否能够提高医疗器械制造中自动化缺陷检测的可靠性。方法：我们考虑一个机器学习框架，该框架基于用知识图谱表示的异构制造和器械报告数据运行。为减轻决策模型不确定性引起的错误，我们分析了一种原则性的拒绝策略，即对估计认知不确定性超过指定阈值的预测进行弃权（不执行）。我们使用标准合成基准和真实世界医疗器械报告数据评估该方法。结果：理论结果确立了该方法的有效性，刻画了其预期有效的条件。实证上，拒绝策略能够显式控制覆盖率，即模型发出预测的样本比例，同时提高保留样本上的性能。在266,170份真实世界FDA MAUDE器械报告中，10%的弃权率将分类错误降低48%，而更激进的拒绝（约70%覆盖率）在保留样本上实现了近乎完美的准确率。在标准合成制造基准上，对9%的决策进行弃权，我们的方法相比标准无弃权方法将风险降低高达63%。结论：对高认知不确定性的预测进行弃权可以为控制基于机器学习的缺陷检测可靠性提供实用工具，尤其是在高风险医疗器械制造应用中。意义：认知不确定性感知的缺陷检测可能通过识别需要额外检查的案例而非发出潜在有害预测，支持医疗器械制造中更安全、更可靠的质量保证。
英文摘要：
Objective: We investigate whether accounting for epistemic uncertainty can improve the reliability of automated defect detection in medical device manufacturing. Methods: We consider a machine learning framework that operates on heterogeneous manufacturing and device-report data represented with Knowledge Graphs. To mitigate errors arising from uncertainty in the decision model, we analyze a principled rejection strategy to abstain from predictions whose estimated epistemic uncertainty exceeds a specified threshold. We evaluate the approach using standard synthetic benchmarks and real-world medical device report data. Results: The theoretical results establish the validity of the method characterizing the regimes under which it is expected to be effective. Empirically, the rejection strategy enables explicit control of coverage, that is, the proportion of samples for which the model issues predictions, while improving performance on the retained samples. On 266,170 real-world FDA MAUDE device reports, a 10% abstention rate reduces classification error by 48%, and more aggressive rejection (approximately 70% coverage) yields near-perfect accuracy on the retained samples. On standard synthetic manufacturing benchmarks, abstaining on 9% of the decisions, our approach reduces the risk up to 63% compared with the standard no-abstention approach. Conclusions: Abstaining from predictions with high epistemic uncertainty can provide a practical tool for controlling the reliability of machine learning-based defect detection, especially in high-stakes medical device manufacturing applications. Significance: Uncertainty-aware defect detection may support safer and more reliable quality assurance in medical device manufacturing by identifying cases that require additional inspection rather than issuing potentially harmful predictions.
