Artificial intelligence for the triage of COVID-19 patients at the emergency department: a systematic review

Abstract

The aim of this article is to systematically analyze the available literature on the efficacy and validity of artificial intelligence (AI) applied to medical imaging techniques in the triage of patients with suspected or confirmed coronavirus disease 2019 (COVID-19) in Emergency Departments (EDs). A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. Medline, Web of Science, and Scopus were searched to identify observational studies evaluating the efficacy of AI methods in the diagnosis and prognosis of COVID-19 using medical imaging. The main characteristics of the selected studies were extracted by two independent researchers and were formally assessed in terms of methodological quality using the Newcastle-Ottawa scale. A total of 11 studies, including 14,499 patients, met inclusion criteria. The quality of the studies was medium to high. Overall, the diagnostic yield of the AI techniques compared to a gold standard was high, with sensitivity and specificity values ranging from 79% to 98% and from 70%to 93%, respectively. The methodological approaches and imaging datasets were highly heterogeneous among studies. In conclusion, AI methods significantly boost the diagnostic yield of medical imaging in the triage of COVID-19 patients in the ED. However, there are significant limitations that should be overcome in future studies, particularly regarding the heterogeneity and limited amount of available data to train AI models.