Diagnostic evaluation of viral versus bacterial tonsillopharyngitis using an artificial intelligence mobile application and symptom questionnaire

Abstract

Objective: In this study, it was aimed to distinguish bacterial/viral tonsillopharyngitis (TP) by scoring the symptom and throat images of pediatric patients with artificial intelligence-based mobile application. Method: Fifty-one patients who applied to Sakarya University Training and Research Hospital, Department of Pediatrics and Diseases with acute tonsillopharyngitis were included. Samples were taken from patients and mouth/throat pictures were taken so that the tonsils and pharynx were clearly visible. In the microbiology laboratory, identification with culture/MALDI-TOF MS (Biomerieux, France) from the first samples, and nucleic acid isolation from the other for molecular tests were performed. Symptoms such as fatigue, sore throat, muscle pain, cough, sneezing, and runny nose were questioned from each patient on a scale of 1 to 5. By uploading the symptom results and throat pictures to the artificial intelligence application, it was aimed to distinguish bacterial/viral tonsillopharyngitis with the developed scoring system. Results: Of the 51 samples included in the study, 21 were culture positive and 30 were negative. The artificial intelligence application was defined as 20 out of 21 culture-positive samples, 3 out of 30 culture-negative samples as bacterial tonsillopharyngitis (Sensitivity: 95.2%, specificity: 90%). Conclusion: This study is one of the first to bring together the artificial intelligence application and microbiology. AI/scoring system may have a role to play in the diagnosis of bacterial vs viral TP, and in doing so may enable more appropriate antibiotic usage targeted to only bacterial TP infections. It is important to distinguish between bacterial and viral tonsillopharyngitis in the COVID-19 pandemic.