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 manifesta-tions 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 hospital-ization, 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.