Efficacy of sanitization in healthcare using deep learning and multiwavelength fluorescence imaging

Luke Woods; Connor Propp; Mitch Sueker; Kaylee Husarik; Hamed Taheri Gorji; Jianwei Qin; Insuck Baek; Moon S. Kim; Diane E. Chan; Stanislav Sokolov; Nicholas MacKinnon; Fartash Vasefi; Kouhyar Tavakolian

doi:10.1117/12.2665702

13 June 2023 Efficacy of sanitization in healthcare using deep learning and multiwavelength fluorescence imaging

Luke Woods, Connor Propp, Mitch Sueker, Kaylee Husarik, Hamed Taheri Gorji, Jianwei Qin, Insuck Baek, Moon S. Kim, Diane E. Chan, Stanislav Sokolov, Nicholas MacKinnon, Fartash Vasefi, Kouhyar Tavakolian

Author Affiliations +

Proceedings Volume 12545, Sensing for Agriculture and Food Quality and Safety XV; 125450H (2023) https://doi.org/10.1117/12.2665702
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Protecting patients receiving care in healthcare facilities from contracting illnesses such as C. difficile, S. aureus, and Acinetobacter is critical. The bed or room incoming patients are pla ced in dramatically affects their chances of contracting an illness from a previous patient. The Centers for Disease Control and Prevention (CDC) reports that 1 in 31 hospital patients have at least one healthcare-associated infection (HAI). Surveillance for HAI is a priority given that the patients receiving healthcare often have compromised immune status. Currently , no real-time tool to monitor cleanliness efficacy provides information for immediate mitigation in large-scale institutional environments. According to the CDC, the transmission of many healthcare-acquired pathogens is related to the contamination of near-patient surfaces and equipment. Therefore, hospitals are encouraged to optimize and improve high-touch surface cleaning at the time of discharge or transfer of pa tients. We have developed a fast and easy-to-use scanner that objectively assesses cleanliness and cleaning product efficacy in healthcare facilities. The scanner detects invisible contamination, provides UVC disinfection of any contamination identified that may harbor bacteria and viruses, and an audit trail of image data for evidence of cleanliness. In addition, we have developed an image segmentation algorithm that provides live identification and labeling of organic residue contamination in video images of high-touch surfaces. Finally, we present fluorescence imaging results of different surfaces in healthcare that were measured, analyzed, and recorded. This information can be used to improve cleaning procedures and for staff education and training.

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Luke Woods, Connor Propp, Mitch Sueker, Kaylee Husarik, Hamed Taheri Gorji, Jianwei Qin, Insuck Baek, Moon S. Kim, Diane E. Chan, Stanislav Sokolov, Nicholas MacKinnon, Fartash Vasefi, and Kouhyar Tavakolian "Efficacy of sanitization in healthcare using deep learning and multiwavelength fluorescence imaging", Proc. SPIE 12545, Sensing for Agriculture and Food Quality and Safety XV, 125450H (13 June 2023); https://doi.org/10.1117/12.2665702

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KEYWORDS

Contamination

Medicine

Cameras

Imaging systems

Fluorescence

Fluorescence imaging

Inspection

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