Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases
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 physi...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83346-y |
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| author | Naoki Okada Shusuke Inoue Chang Liu Sho Mitarai Shinichi Nakagawa Yohsuke Matsuzawa Satoshi Fujimi Goshiro Yamamoto Tomohiro Kuroda |
| author_facet | Naoki Okada Shusuke Inoue Chang Liu Sho Mitarai Shinichi Nakagawa Yohsuke Matsuzawa Satoshi Fujimi Goshiro Yamamoto Tomohiro Kuroda |
| author_sort | Naoki Okada |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-309482cc6e5a4d80bc1c4789bcf36edf |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-309482cc6e5a4d80bc1c4789bcf36edf2025-08-20T02:13:19ZengNature PortfolioScientific Reports2045-23222025-02-011511910.1038/s41598-024-83346-yUnified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma casesNaoki Okada0Shusuke Inoue1Chang Liu2Sho Mitarai3Shinichi Nakagawa4Yohsuke Matsuzawa5Satoshi Fujimi6Goshiro Yamamoto7Tomohiro Kuroda8Graduate School of Informatics, Kyoto Universityfcuro incGraduate School of Informatics, Kyoto UniversityGraduate School of Informatics, Kyoto UniversityUji Tokushukai HospitalOsaka Metropolitan UniversityOsaka General Medical CenterGraduate School of Informatics, Kyoto UniversityGraduate School of Informatics, Kyoto UniversityAbstract 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.https://doi.org/10.1038/s41598-024-83346-yDiagnostic imagingDeep learning modelAutomated windowingTrauma |
| spellingShingle | Naoki Okada Shusuke Inoue Chang Liu Sho Mitarai Shinichi Nakagawa Yohsuke Matsuzawa Satoshi Fujimi Goshiro Yamamoto Tomohiro Kuroda Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases Scientific Reports Diagnostic imaging Deep learning model Automated windowing Trauma |
| title | Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases |
| title_full | Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases |
| title_fullStr | Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases |
| title_full_unstemmed | Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases |
| title_short | Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases |
| title_sort | unified total body ct image with multiple organ specific windowings validating improved diagnostic accuracy and speed in trauma cases |
| topic | Diagnostic imaging Deep learning model Automated windowing Trauma |
| url | https://doi.org/10.1038/s41598-024-83346-y |
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