个人防护装备不符合性检测的比较分析:计算机视觉与人类观察者。

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mary S Kim, Beomseok Park, Genevieve J Sippel, Aaron H Mun, Wanzhao Yang, Kathleen H McCarthy, Emely Fernandez, Marius George Linguraru, Aleksandra Sarcevic, Ivan Marsic, Randall S Burd
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引用次数: 0

摘要

目标:人工监控医疗保健提供者对个人防护设备(PPE)的遵守情况有几个局限性,包括在人员短缺时需要额外的人员,以及在长时间工作时警惕性降低。为了应对这些挑战,我们开发了一种自动计算机视觉系统,用于监控医疗机构中个人防护设备的使用情况。我们在视频监控实验中评估了该系统与人类观察员检测不遵守情况的性能:使用物体检测器和跟踪器对自动系统进行了训练,以检测 15 类眼镜、口罩、手套和防护服。为了评估该系统与人类观察者相比在检测不遵守规定方面的表现,我们设计了一个视频监控实验,实验有两个条件:视频持续时间(20、40 和 60 秒)和视频中的人数(3 对 6)。12 名护士作为人类观察员参与了实验。根据检测到的不遵医嘱行为的数量来评估绩效:结果:人工观察者发现的不遵医嘱情况少于系统(参数估计值-0.3,95% CI -0.4至-0.2,P 讨论):自动系统可同时追踪多个物体和个人。该系统的性能还不受观察时间长短的影响,这是对人工监控的一种改进:自动系统为可扩展的医院感染控制实践监控和改善医疗机构中个人防护设备的使用提供了一个潜在的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis of personal protective equipment nonadherence detection: computer vision versus human observers.

Objectives: Human monitoring of personal protective equipment (PPE) adherence among healthcare providers has several limitations, including the need for additional personnel during staff shortages and decreased vigilance during prolonged tasks. To address these challenges, we developed an automated computer vision system for monitoring PPE adherence in healthcare settings. We assessed the system performance against human observers detecting nonadherence in a video surveillance experiment.

Materials and methods: The automated system was trained to detect 15 classes of eyewear, masks, gloves, and gowns using an object detector and tracker. To assess how the system performs compared to human observers in detecting nonadherence, we designed a video surveillance experiment under 2 conditions: variations in video durations (20, 40, and 60 seconds) and the number of individuals in the videos (3 versus 6). Twelve nurses participated as human observers. Performance was assessed based on the number of detections of nonadherence.

Results: Human observers detected fewer instances of nonadherence than the system (parameter estimate -0.3, 95% CI -0.4 to -0.2, P < .001). Human observers detected more nonadherence during longer video durations (parameter estimate 0.7, 95% CI 0.4-1.0, P < .001). The system achieved a sensitivity of 0.86, specificity of 1, and Matthew's correlation coefficient of 0.82 for detecting PPE nonadherence.

Discussion: An automated system simultaneously tracks multiple objects and individuals. The system performance is also independent of observation duration, an improvement over human monitoring.

Conclusion: The automated system presents a potential solution for scalable monitoring of hospital-wide infection control practices and improving PPE usage in healthcare settings.

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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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