Macquarie Medical School, Macquarie University, Sydney Australia.
Australian Institute of Health Innovation Macquarie University, Sydney Australia.
Commun Dis Intell (2018). 2022 Aug 18;46. doi: 10.33321/cdi.2022.46.51.
Current procedures for effective personal protective equipment (PPE) usage rely on the availability of trained observers or 'buddies' who, during the COVID-19 pandemic, are not always available. The application of artificial intelligence (AI) has the potential to overcome this limitation by assisting in complex task analysis. To date, AI use for PPE protocols has not been studied. In this paper we validate the performance of an AI PPE system in a hospital setting.
A clinical cohort study of 74 healthcare workers (HCW) at a 144-bed University teaching hospital. Participants were recruited to use the AI system for PPE donning and doffing. Performance was validated by the current gold standard double-buddy system across seven donning and ten doffing steps based on local infection control guidelines.
The AI-PPE platform was 98.9% sensitive on doffing and 85.3% sensitive on donning, when compared to remediated double buddy. On average, buddy correction of PPE was required 3.8 ± 1.5% of the time. The average time taken to don was 240 ± 51.5 seconds and doff was 241 ± 35.3 seconds.
This study demonstrates the ability of an AI model to analyse PPE donning and doffing with real-time feedback for remediation. The AI platform can identify complex multi-task PPE donning and doffing in a single validated system. This AI system can be employed to train, audit, and thereby improve compliance whilst reducing reliance on limited HCW resources. Further studies may permit the development of this educational tool into a medical device with other industry uses for safety.
目前有效的个人防护设备（PPE）使用程序依赖于受过培训的观察者或“伙伴”的可用性，而在 COVID-19 大流行期间，他们并非总是可用的。人工智能（AI）的应用有可能通过协助进行复杂的任务分析来克服这一限制。迄今为止，尚未研究过 AI 在 PPE 协议中的应用。在本文中，我们在医院环境中验证了 AI PPE 系统的性能。
对一家拥有 144 张床位的大学教学医院的 74 名医护人员（HCW）进行了临床队列研究。招募参与者使用 AI 系统进行 PPE 穿戴和脱卸。根据当地感染控制指南，通过当前的黄金标准双伙伴系统对七个穿戴和十个脱卸步骤进行验证，以评估性能。
与纠正后的双伙伴相比，AI-PPE 平台在脱卸时的灵敏度为 98.9%，在穿戴时的灵敏度为 85.3%。平均而言，需要伙伴纠正 PPE 的时间占总时间的 3.8±1.5%。穿戴时间平均为 240±51.5 秒，脱卸时间平均为 241±35.3 秒。
本研究证明了 AI 模型分析 PPE 穿戴和脱卸的能力，具有实时反馈以进行纠正。AI 平台可以在单个经过验证的系统中识别复杂的多任务 PPE 穿戴和脱卸。该 AI 系统可用于培训、审核，从而提高合规性，同时减少对有限的 HCW 资源的依赖。进一步的研究可能会允许将这个教育工具开发成一种医疗器械，并在其他行业中用于安全目的。
