Seresirikachorn K.Ruamviboonsuk P.Soonthornworasiri N.Singhanetr P.Prakayaphun T.Kaothanthong N.Somwangthanaroj S.Theeramunkong T.Mahidol University2023-05-192023-05-192023-04-01PLoS ONE Vol.18 No.4 April (2023)https://repository.li.mahidol.ac.th/handle/20.500.14594/82135Objectives Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public’s practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK. Methods After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis. Results AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, p<0.001). People were 1.15 times more likely to be unprotected during the holidays and in the evening, than on working days and in the morning (OR = 1.15, 95% CI 1.13–1.17, p<0.001). Conclusions AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people’s mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.MultidisciplinaryArticleSCOPUS10.1371/journal.pone.02818412-s2.0-851522443651932620337040359