Cleanliness assessment in long-term care facilities using deep learning and multiwavelength fluorescence imaging

Kaylee Husarik; Hamed Taheri Gorji; Jianwei Qin; Diane E. Chan; Insuck Baek; Moon S. Kim; Mona Sohrabi Thompson; Nicholas MacKinnon; Stanislav Sokolov; Fartash Vasefi; Michael Johnson; Melanie Metz; Kouhyar Tavakolian

doi:10.1117/12.2663393

13 June 2023 Cleanliness assessment in long-term care facilities using deep learning and multiwavelength fluorescence imaging

Kaylee Husarik, Hamed Taheri Gorji, Jianwei Qin, Diane E. Chan, Insuck Baek, Moon S. Kim, Mona Sohrabi Thompson, Nicholas MacKinnon, Stanislav Sokolov, Fartash Vasefi, Michael Johnson, Melanie Metz, Kouhyar Tavakolian

Author Affiliations +

Proceedings Volume 12545, Sensing for Agriculture and Food Quality and Safety XV; 1254507 (2023) https://doi.org/10.1117/12.2663393
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Protecting elders in long-term care facilities (LTCFs) from foodborne illnesses, such as norovirus, listeria, salmonella, and E. coli, is critical. Sanitation inspection is an ongoing concern for LTCF kitchens and dining facilities and staff who handle and serve food. LTCFs must prevent food contamination but must also deal with the potential spread of infection among workers and customers. By 2050 the number of Americans needing LTCFs is expected to double. The Centers for Disease Control and Prevention (CDC) reports that 1 to 3 million serious infections occur annually in nursing homes and assisted living. LTCF sanitization can benefit from standardized tools such as checklists and frequent staff education, including specific product use training. Visual inspection is the most common evaluation method for cleanliness after cleaning but is non-objective and less accurate. Swab-based adenosine triphosphate (ATP) bioluminescence assays are objective for evaluating the quality of cleaning in LTCFs. While more accurate than visual assessment, it requires additional swab and analysis time. We present a fast and easy-to-use handheld fluorescence imaging system for infection prevention in LTCFs. It detects invisible contamination, provides immediate UVC deactivation of potential threats (i.e., bacteria, viruses), and documentation for traceable evidence of cleanliness. We have developed an algorithm to detect organic residue contamination found in images of high-touch surfaces. We provide fluorescence imaging optimization of camera parameters to improve the machine-learning results of different surfaces in LTCFs that were measured, analyzed, and recorded. This information can improve cleaning procedures and educate and train staff.

Conference Presentation

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Kaylee Husarik, Hamed Taheri Gorji, Jianwei Qin, Diane E. Chan, Insuck Baek, Moon S. Kim, Mona Sohrabi Thompson, Nicholas MacKinnon, Stanislav Sokolov, Fartash Vasefi, Michael Johnson, Melanie Metz, and Kouhyar Tavakolian "Cleanliness assessment in long-term care facilities using deep learning and multiwavelength fluorescence imaging", Proc. SPIE 12545, Sensing for Agriculture and Food Quality and Safety XV, 1254507 (13 June 2023); https://doi.org/10.1117/12.2663393

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