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

Abstract

Background: Radiology 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.

Methods: This 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.

Results: We 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.

Conclusions: We 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.

Keywords: Artificial intelligence (AI); computed tomography (CT); emergency; polytrauma.

Figure 1

Graphically illustrated and quantified AI algorithm results. Different parts of the figure do not belong to the same patient. Left: aortic analysis with graphic illustration of nine landmark positions and an exemplarily illustrated measurement plane. Top center: lung lesion detection. Bottom center: vertebral body segmentation used for height measurements at anterior/posterior edge and in the vertebral center. Top right: cardiac segmentation allowing for the calculation of total cardiac volume and coronary plaque volumetry (illustrated in green). Bottom right: summary of algorithm results with corresponding quantifiable metrics. **, vertebra height reduced by more than 25% (compared to neighbour); ***, vertebra height reduced by more than 40% (compared to neighbour). AI, artificial intelligence.

Figure 2

Algorithm analysis of cardiac volume and coronary artery plaques. A1/B1: subgroups have been built according to the cardiac assessment in the original reports; originally not evaluated cardiac sizes have been re-assessed (on the right, illustrated by the arrow). AI-detected total cardiac volume (A1) as well as AI-detected relative cardiac/lung volume (B1) significantly correlate with radiologists’ visual assessment. Lung volumetry was not functional for three patients with extensive consolidations or pneumothorax (compare 105 cases in A1 with 102 cases in A2). Optimized thresholds are graphically illustrated, based on the calculations in A2/B2. A2/B2: ROC analysis was performed based on total cardiac volume (A2) and relative cardiac/lung volume (B2) detection with radiologic assessment serving as reference standard (“cardiomegaly” and “borderline” was pooled and considered to be pathologic). ROC operating points were approximated to the maximum sum of sensitivity and specificity, corresponding metrics are illustrated. C1: Patients with AI-detected coronary plaques (n=99 out of 105) have been divided in subgroups according to primarily coronary plaque reporting by radiologists. Originally non-reported cases have been radiologically re-assessed and AI detection could be confirmed for 17 cases. Detected plaque volume significantly correlates with radiologists’ visual assessment. C2: ROC analysis was performed based on AI plaque volumetry and radiologists’ assessment as reference standard. ROC operating point (threshold 69.35 mm³) was approximated to the maximum sum of sensitivity and specificity, corresponding metrics are illustrated. A1/B1/C1: significance levels were analyzed by non-paired Student’s t-test and illustrated as *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. AI, artificial intelligence; ROC, receiver operating curve; SD, standard deviation.

Figure 3

Algorithm-based identification of 35 patients with initially non-detected dilatations of the thoracic aorta, radiologically confirmed for 34 cases. Aorta was measured at nine landmark positions according to American Heart Association (AHA) guidelines; enlarged aortic diameters were identified between landmark position #2 (sinotubular junction) and #6 (proximal descending aorta). Enlargements are graphically divided according to grade of dilatation in relation to the general population’s standard variation. Measurements at different landmark positions connected by dashed lines belong to the same patient. AI measurements highlighted in red could not be confirmed by radiological revision.

Figure 4

Lung lesions and vertebral body fractures detected by AI algorithm and/or radiologists; lung lesions solely detected by the AI are radiologically classified. A1/A2: 108 lung lesions were detected for 66 out of 105 patients; hereby 81 lesions of 54 patients were solely detected by the algorithm. A3: radiologists’ review and classification of AI-detected lesions that have not been mentioned in the initial radiological report: 64 out of 81 lesions were visually confirmed, among them 3 lesions of two different patients that have been classified as “recommended to control”. B1/B2: 64 vertebral body fractures for 52 out of 105 patients. Thirty-seven fractures of 33 patients have been solely detected by the algorithm; among them 13 fractures of 12 patients have been radiologically confirmed. Two of these fractures have been classified as acutely traumatic but stable without any involvement of the vertebra’s posterior edge. AI, artificial intelligence.