assessment of oral manifestations and oral health in hospitalized patients with covid-19: machine learning and statistical analysis

Background: this study aimed to investigate the oral health presentations of coronavirus disease 2019 (covid-19) inpatients using statistical analysis and machine learning methods before infection, during hospitalization, and after discharge from the hospital. methods: this cross-sectional study was conducted on 140 hospitalized covid-19 patients with reverse transcription-polymerase chain reaction diagnosis and severe symptoms. demographic data, clinical characteristics, oral health habits, and oral manifestations in three periods (i.e., before infection, during hospitalization, and after discharge from the hospital) were recorded through a questionnaire and oral examination. statistical analysis and machine learning methods were used for the analysis of patients’ data. results: xerostomia, dysgeusia, hypogeusia, halitosis, and a metallic taste were the most frequent oral symptoms during hospitalization, with the incidence of 68.6%, 51.4%, 49.3%, 31.4%, and 29.3% in patients, respectively. using tobacco significantly increased the incidence of xerostomia, dysgeusia, hypogeusia, halitosis, and a metallic taste during hospitalization (p = 0.011, p = 0.001, p = 0.002, p = 0.0001, and p = 0.0001, respectively). smoking led to increasing dysgeusia, hypogeusia, halitosis, and a metallic taste during hospitalization (p = 0.019, p = 0.014, p = 0.013, and p = 0.006, respectively). the micro-average receiver operating characteristic (roc) curve analysis revealed that the machine learning logistic regression model achieved the highest area under the roc curve with a value of 0.83. conclusions: xerostomia and dysgeusia are the most common oral symptoms of covid-19 patients and could be used to predict covid-19 infection. dysgeusia correlates with xerostomia, and it is hypothesized that xerostomia is an etiologic factor for dysgeusia. the early detection of covid-19 can help reduce the enormous burden on healthcare systems, and machine learning is advantageous for this purpose.