Background:Hand hygiene can be a simple, inexpensive, and effective method for preventing the spread of infectious diseases. However, a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is challenging. The aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and communities.Methods:We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks (3D CNNs) and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal validation. An assessment scheme with a probability smoothing method was also proposed to optimize the neural network’s output to identify the handwashing steps, measure the exact duration, and grade the standard level of recognized steps. Twenty-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external validation.Results:Using a deep learning algorithm and an assessment scheme, combined with a probability smoothing method, each handwashing step was recognized (ACC ranged from 90.64% to 98.87% in the hospital and from 87.39% to 96.71% in the community). An assessment scheme measured each step’s exact duration, and the intraclass correlation coefficients were 0.98 (95% CI: 0.97-0.98) and 0.91 (95% CI: 0.88-0.93) for the total video duration in the hospital and community, respectively. Furthermore, the system assessed the quality of handwashing, similar to the expert panel (kappa = 0.79 in the hospital; kappa = 0.65 in the community).Conclusions:This work developed an algorithm to directly assess handwashing compliance and quality from videos, which is promising for application in healthcare settings and communities to reduce pathogen transmission.

Handwashing;Infectious control;Deep learning;Action recognition;Standard level grading

Wang Ting;Xia Jun;Wu Tianyi;Ni Huanqi;Long Erping;Li Ji-Peng Olivia;Zhao Lanqin;Chen Ruoxi;Wang Ruixin;Xu Yanwu;Huang Kai;Lin Haotian

State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, Guangdong 510275, China;School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510275, China;Key Laboratory of Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China;Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom;Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315300, China;Center for Precision Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510275, China

智慧医学（英文）

[1]Wang Ting;Xia Jun;Wu Tianyi;Ni Huanqi;Long Erping;Li Ji-Peng Olivia;Zhao Lanqin;Chen Ruoxi;Wang Ruixin;Xu Yanwu;Huang Kai;Lin Haotian-.Handwashing quality assessment via deep learning: a modelling study for monitoring compliance and standards in hospitals and communities)[J].智慧医学（英文）,2022(03):152-160

Handwashing,handwashing

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