Objective: This study addressed the problem of objectively detecting leaks in P2 respirators at point of use, an essential component for healthcare workers' protection. To achieve this, we explored the use of infra-red (IR) imaging combined with machine learning algorithms on the thermal gradient across the respirator during inhalation. Results: The study achieved high accuracy in predicting pass or fail outcomes of quantitative fit tests for flat-fold P2 FFRs. The IR imaging methods surpassed the limitations of self fit-checking. Conclusions: The integration of machine learning and IR imaging on the respirator itself demonstrates promise as a more reliable alternative for ensuring the proper fit of P2 respirators. This innovative approach opens new avenues for technology application in occupational hygiene and emphasizes the need for further validation across diverse respirator styles. Significance Statement: Our novel approach leveraging infra-red imaging and machine learning to detect P2 respirator leaks represents a critical advancement in occupational safety and healthcare workers' protection.

中文翻译：

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在适合性测试诊所期间使用红外成像检测医护人员的呼吸器泄漏

目的：本研究解决了在使用时客观检测 P2 呼吸器泄漏的问题，这是保护医护人员的重要组成部分。为了实现这一目标，我们探索了将红外 (IR) 成像与机器学习算法相结合来研究吸气过程中呼吸器上的热梯度。结果：该研究在预测平折 P2 FFR 定量拟合测试的通过或失败结果方面实现了高精度。红外成像方法超越了自拟合检查的局限性。结论：机器学习和红外成像在呼吸器本身上的集成表明有望成为确保 P2 呼吸器正确佩戴的更可靠的替代方案。这种创新方法为职业卫生领域的技术应用开辟了新途径，并强调需要对不同类型的呼吸器进行进一步验证。意义声明：我们利用红外成像和机器学习来检测 P2 呼吸器泄漏的新颖方法代表了职业安全和医护人员保护方面的重大进步。