Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities
Introduction: The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024168867 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841526291080151040 |
|---|---|
| author | Yi-Ling Fan Ching-Han Hsu Ju-Yu Wu Ying-Ying Tsai Wei J. Chen Min-Shi Lee Fang-Rong Hsu Lun-De Liao |
| author_facet | Yi-Ling Fan Ching-Han Hsu Ju-Yu Wu Ying-Ying Tsai Wei J. Chen Min-Shi Lee Fang-Rong Hsu Lun-De Liao |
| author_sort | Yi-Ling Fan |
| collection | DOAJ |
| description | Introduction: The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures can pose significant hazards. Methods: An external system captured images of personnel entering a laboratory, which were then analyzed by an AI-based system to verify their compliance with personal protective equipment (PPE) regulations, thereby introducing an additional layer of protection. A deep learning model was trained to detect the presence of essential PPE items, such as clothing, masks, hoods, double-layer gloves, shoe covers, and respirators, ensuring adherence to World Health Organization (WHO) standards. The internal laboratory management system used a deep learning model to delineate alert zones and monitor compliance with the imposed safety protocols. Results: The external detection system was trained on a dataset consisting of 4112 images divided into 15 PPE compliance classes. The model achieved an accuracy of 97.52 % and a recall of 97.03 %. The identification results were presented in real time via a visual interface and simultaneously stored on the administrator's dashboard for future reference. We trained the internal management system on 3347 images, achieving 90 % accuracy and 85 % recall. The results were transmitted in JSON format to the internal monitoring system, which triggered alerts in response to violations of safe practices or alert zones. Real-time notifications were sent to the administrators when the safety thresholds were met. Conclusion: The BSL-3 laboratory monitoring system significantly reduces the risk of exposure to pathogens for personnel during laboratory operations. By ensuring the correct use of PPE and enhancing adherence to the imposed safety protocols, this system contributes to maintaining the integrity of BSL-3 facilities and mitigates the risk of personnel becoming infection vectors. |
| format | Article |
| id | doaj-art-a54b094ade4144d1826e575ab444d88f |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-a54b094ade4144d1826e575ab444d88f2025-01-17T04:49:48ZengElsevierHeliyon2405-84402025-01-01111e40855Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilitiesYi-Ling Fan0Ching-Han Hsu1Ju-Yu Wu2Ying-Ying Tsai3Wei J. Chen4Min-Shi Lee5Fang-Rong Hsu6Lun-De Liao7Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 350, Taiwan; Department of Biomedical Engineering & Environmental Sciences, National Tsing-Hua University, Hsinchu, TaiwanDepartment of Biomedical Engineering & Environmental Sciences, National Tsing-Hua University, Hsinchu, TaiwanInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 350, Taiwan; Doctoral Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, Taichung, TaiwanInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 350, Taiwan; Department of Biomedical Engineering & Environmental Sciences, National Tsing-Hua University, Hsinchu, TaiwanCenter for Neuropsychiatric Research, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 350, Taiwan; Corresponding author.National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 350, TaiwanDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, TaiwanInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 350, Taiwan; Corresponding author.Introduction: The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures can pose significant hazards. Methods: An external system captured images of personnel entering a laboratory, which were then analyzed by an AI-based system to verify their compliance with personal protective equipment (PPE) regulations, thereby introducing an additional layer of protection. A deep learning model was trained to detect the presence of essential PPE items, such as clothing, masks, hoods, double-layer gloves, shoe covers, and respirators, ensuring adherence to World Health Organization (WHO) standards. The internal laboratory management system used a deep learning model to delineate alert zones and monitor compliance with the imposed safety protocols. Results: The external detection system was trained on a dataset consisting of 4112 images divided into 15 PPE compliance classes. The model achieved an accuracy of 97.52 % and a recall of 97.03 %. The identification results were presented in real time via a visual interface and simultaneously stored on the administrator's dashboard for future reference. We trained the internal management system on 3347 images, achieving 90 % accuracy and 85 % recall. The results were transmitted in JSON format to the internal monitoring system, which triggered alerts in response to violations of safe practices or alert zones. Real-time notifications were sent to the administrators when the safety thresholds were met. Conclusion: The BSL-3 laboratory monitoring system significantly reduces the risk of exposure to pathogens for personnel during laboratory operations. By ensuring the correct use of PPE and enhancing adherence to the imposed safety protocols, this system contributes to maintaining the integrity of BSL-3 facilities and mitigates the risk of personnel becoming infection vectors.http://www.sciencedirect.com/science/article/pii/S2405844024168867BioengineeringBiomedical instrumentationBiomedical laboratory safetyBiosafety level-3 laboratory |
| spellingShingle | Yi-Ling Fan Ching-Han Hsu Ju-Yu Wu Ying-Ying Tsai Wei J. Chen Min-Shi Lee Fang-Rong Hsu Lun-De Liao Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities Heliyon Bioengineering Biomedical instrumentation Biomedical laboratory safety Biosafety level-3 laboratory |
| title | Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities |
| title_full | Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities |
| title_fullStr | Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities |
| title_full_unstemmed | Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities |
| title_short | Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities |
| title_sort | enhancing safety with an ai empowered assessment and monitoring system for bsl 3 facilities |
| topic | Bioengineering Biomedical instrumentation Biomedical laboratory safety Biosafety level-3 laboratory |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024168867 |
| work_keys_str_mv | AT yilingfan enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities AT chinghanhsu enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities AT juyuwu enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities AT yingyingtsai enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities AT weijchen enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities AT minshilee enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities AT fangronghsu enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities AT lundeliao enhancingsafetywithanaiempoweredassessmentandmonitoringsystemforbsl3facilities |