Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance.

Abstract

Background\nRadiology reporting of emergency whole-body computed tomography (CT) scans is time-critical and therefore involves a significant risk of pathology under-detection. We hypothesize a relevant number of initially missed secondary thoracic findings that would have been detected by an artificial intelligence (AI) software platform including several pathology-specific AI algorithms.\n\n\nMethods\nThis retrospective proof-of-concept-study consecutively included 105 shock-room whole-body CT scans. Image data was analyzed by platform-bundled AI-algorithms, findings were reviewed by radiology experts and compared with the original radiologist s reports. We focused on secondary thoracic findings, such as cardiomegaly, coronary artery plaques, lung lesions, aortic aneurysms and vertebral fractures.\n\n\nResults\nWe identified a relevant number of initially missed findings, with their quantification based on 105 analyzed CT scans as follows: up to 25 patients (23.8%) with cardiomegaly or borderline heart size, 17 patients (16.2%) with coronary plaques, 34 patients (32.4%) with aortic ectasia, 2 patients (1.9%) with lung lesions classified as recommended to control and 13 initially missed vertebral fractures (two with an acute traumatic origin). A high number of false positive or non-relevant AI-based findings remain problematic especially regarding lung lesions and vertebral fractures.\n\n\nConclusions\nWe consider AI to be a promising approach to reduce the number of missed findings in clinical settings with a necessary time-critical radiological reporting. Nevertheless, algorithm improvement is necessary focusing on a reduction of false positive findings and on algorithm features assessing the finding relevance, e.g., fracture age or lung lesion malignancy.