AI in the Identification of Lung Contusions Through Chest Radiological Examination in Blunt Thoracic Trauma

The observational study focuses on comparing the interpretation of chest radiological examinations performed using a computer-based system with the standard interpretation conducted by a radiologist. The "LUNIT" system serves as a tool designed to assist radiologists in detecting the 10 most common abnormalities visible on chest radiographs, with proven efficacy in large case series. The investigation addresses the need to evaluate lung injuries resulting from thoracic trauma, which are linked to a higher risk of complications requiring close monitoring to detect potential respiratory failure. The primary aim of the study is to assess the accuracy of the LUNIT system in interpreting chest radiographs for the identification of lung contusions compared to the standard radiologist-based interpretation.