Journal article
An image dataset for surveillance of personal protective equipment adherence in healthcare
Scientific data, v 12(1), pp 96-10
17 Jan 2025
PMID: 39824881
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- An image dataset for surveillance of personal protective equipment adherence in healthcare
- Creators
- Wanzhao Yang - Rutgers, The State University of New JerseyMary S Kim - Children's NationalGenevieve J Sippel - Children's NationalAaron H Mun - Children's NationalKathleen H McCarthy - Children's NationalBeomseok Park - Rutgers, The State University of New JerseyAleksandra Sarcevic - Drexel UniversityMarius George Linguraru - Children's NationalIvan Marsic - Rutgers, The State University of New JerseyRandall S Burd - Children's National
- Publication Details
- Scientific data, v 12(1), pp 96-10
- Publisher
- NATURE PORTFOLIO; BERLIN
- Number of pages
- 10
- Grant note
- R01LM011834 / U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R56EB032819 / U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:001398291700005
- Scopus ID
- 2-s2.0-85216228242
- Other Identifier
- 991022020437404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence