Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases

Abstract

Abstract Total-body CT scans are useful in saving trauma patients; however, interpreting numerous images with varied window settings slows injury detection. We developed an algorithm for “unified total-body CT image with multiple organ-specific windowings (Uni-CT)”, and assessing its impact on physician accuracy and speed in trauma CT interpretation. From November 7, 2008, to June 19, 2020, 40 cases of total-body CT images for blunt trauma with multiple injuries, were collected from the emergency department of Osaka General Medical Center and randomly divided into two groups. In half of the cases, the Uni-CT algorithm using semantic segmentation assigned visibility-friendly window settings to each organ. Four physicians with varying levels of experience interpreted 20 cases using the algorithm and 20 cases in conventional settings. The performance was analyzed based on the accuracy, sensitivity, specificity of the target findings, and diagnosis speed. In the proposal and conventional groups, patients had an average of 2.6 and 2.5 targeting findings, mean ages of 51.8 and 57.7 years, and male proportions of 60% and 45%, respectively. The agreement rate for physicians’ diagnoses was κ = 0.70. Average accuracy, sensitivity, and specificity of target findings were 84.8%, 74.3%, 96.9% and 85.5%, 81.2%, 91.5%, respectively, with no significant differences. Diagnostic speed per case averaged 71.9 and 110.4 s in each group (p < 0.05). The Uni-CT algorithm improved the diagnostic speed of total-body CT for trauma, maintaining accuracy comparable to that of conventional methods.

Keywords

• Diagnostic imaging
• Deep learning model
• Automated windowing
• Trauma