Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a “new software score” in Coronavirus Disease 2019 Pneumonia Suspected Patients

Bagnera, Silvia; Bisanti, Francesca; Tibaldi, Claudia; Pasquino, Massimo; Berrino, Giulia; Ferraro, Roberta; Patania, Sebastiano

OBJECTIVES: The purpose of this study is to assess the performance of radiologists using a new software called “COVID-19 score” when performing chest radiography on patients potentially infected by coronavirus disease 2019 (COVID-19) pneumonia. Chest radiography (or chest X-ray, CXR) and CT are important for the imaging diagnosis of the coronavirus pneumonia (COVID-19). CXR mobile devices are efficient during epidemies, because allow to reduce the risk of contagion and are easy to sanitize. MATERIAL AND METHODS: From February–April 2020, 14 radiologists retrospectively evaluated a pool of 312 chest X-ray exams to test a new software function for lung imaging analysis based on radiological features and graded on a three-point scale. This tool automatically generates a cumulative score (0–18). The intra- rater agreement (evaluated with Fleiss’s method) and the average time for the compilation of the banner were calculated. RESULTS: Fourteen radiologists evaluated 312 chest radiographs of COVID-19 pneumonia suspected patients (80 males and 38 females) with an average age of 64, 47 years. The inter-rater agreement showed a Fleiss’ kappa value of 0.53 and the intra-group agreement varied from Fleiss’ Kappa value between 0.49 and 0.59, indicating a moderate agreement (considering as “moderate” ranges 0.4–0.6). The years of work experience were irrelevant. The average time for obtaining the result with the automatic software was between 7 s (e.g., zero COVID-19 score) and 21 s (e.g., with COVID-19 score from 6 to 12). CONCLUSION: The use of automatic software for the generation of a CXR “COVID-19 score” has proven to be simple, fast, and replicable. Implementing this tool with scores weighed on the number of lung pathological areas, a useful parameter for clinical monitoring could be available.