ArticleJournal of the American Medical Informatics Association : JAMIA2025
Comparative analysis of personal protective equipment nonadherence detection: computer vision versus human observers.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bounding Boxes in Practice: Design and Early Evaluation of a Video-Based PPE Compliance Monitoring Application for Time-Critical Care: A preliminary evaluation of a video-based explainable interface to improve the usability of AI model outputs.Extended abstracts on Human factors in computing systems. CHI Conference · 2026Article
- Image recognition-based detection system for preventing accidental dislodgement of head-and-neck medical supplies in ICU patients: A feasibility randomized controlled trial.International journal of nursing sciences · 2026Article
- Artificial intelligence-assisted personal protective equipment donning and doffing training for health professions students and healthcare workers: a scoping review.BMC medical education · 2025Article
- Timing and type of personal protective equipment adherence lapses in pediatric trauma resuscitation: A retrospective video study.American journal of infection control · 2025Article
- Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
objectivesHuman 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
methodsThe 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.
resultsHuman 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.
conclusionThe 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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