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Proper personal protective equipment (PPE) use is critical to prevent disease transmission to healthcare providers, especially those treating patients with a high infection risk. To address the challenge of monitoring PPE usage in healthcare, computer vision has been evaluated for tracking adherence. Existing datasets for this purpose, however, lack a diversity of PPE and nonadherence classes, represent single not multiple providers, and do not depict dynamic provider movement during patient care. We introduce the Resuscitation Room Personal Protective Equipment (R2PPE) dataset that bridges this gap by providing a realistic portrayal of diverse PPE use by multiple interacting individuals in a healthcare setting. This dataset contains 26 videos, 10,034 images and 123,751 bounding box annotations for 17 classes of PPE adherence and nonadherence for eyewear, masks, gowns, and gloves, and one additional head class. Evaluations using newly proposed metrics confirm R2PPE exhibits higher annotation density than three established general-purpose and medical PPE datasets. The R2PPE dataset provides a resource for developing computer vision algorithms for monitoring PPE use in healthcare. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]