Fan Yi-Ling, Hsu Ching-Han, Wu Ju-Yu, Tsai Ying-Ying, Chen Wei J, Lee Min-Shi, Hsu Fang-Rong, Liao Lun-De
Institute 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, Taiwan.
Heliyon. 2024 Dec 16;11(1):e40855. doi: 10.1016/j.heliyon.2024.e40855. eCollection 2025 Jan 15.
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.
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.
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.
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.
新冠疫情引发了对研究的迫切需求，这促使生物安全3级（BSL-3）实验室加强生物安全协议的制定，以防范处理高传染性病原体所带来的风险。实验室管理失误可能会造成重大危害。
一个外部系统捕捉人员进入实验室的图像，然后由基于人工智能的系统进行分析，以核实他们是否遵守个人防护装备（PPE）规定，从而增加一层保护。训练了一个深度学习模型来检测基本PPE物品的存在，如防护服、口罩、头罩、双层手套、鞋套和呼吸器，确保符合世界卫生组织（WHO）标准。内部实验室管理系统使用深度学习模型来划定警戒区域，并监测对所实施安全协议的遵守情况。
外部检测系统在一个由4112张图像组成的数据集上进行训练，这些图像分为15个PPE合规类别。该模型的准确率达到97.52%，召回率为97.03%。识别结果通过视觉界面实时呈现，并同时存储在管理员的仪表板上以供日后参考。我们在3347张图像上训练了内部管理系统，准确率达到90%，召回率为85%。结果以JSON格式传输到内部监测系统，该系统会在违反安全操作或警戒区域时触发警报。当达到安全阈值时，会向管理员发送实时通知。
BSL-3实验室监测系统显著降低了实验室操作期间人员接触病原体的风险。通过确保正确使用PPE并加强对所实施安全协议的遵守，该系统有助于维护BSL-3设施的完整性，并降低人员成为感染源的风险。
