{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:07:51Z","timestamp":1790744871205,"version":"4.1.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study provides a Rapid Analysis and Processing of Image Data (RAPID) framework that combines deep learning-based CT topogram analysis with DICOM spatial geometry to enable reliable anatomical labelling of CT series independent of inconsistent textual metadata.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>In this single-centre retrospective study, three YOLOv8-based models comprising the RAPID framework were trained on CT topograms to perform global anatomical classification, body-region detection, and landmark detection. Classification used 83207 topograms (20,802 test), while landmark and\u00a0body region detection models were trained on 2000 (500 test) and 1926 (481 test) topograms, respectively, collected between 2003 and 2022. Model performance was evaluated using the F1 score and mAP50, with additional external validation on the external cohort. Furthermore, three radiologists independently reviewed 150 randomly selected predictions for detection models using a Likert-scale-based clinical assessment with inter-rater agreement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Results<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>Across a total of 65,250 patients (median age, 62 years; interquartile range, 23; 44% female) included in training and testing, inference performance achieved an overall internal F1 score of 0.920 and an external score of 0.970 for classification. Body region and landmark detection achieved internal mAP50 values of 0.993 and 0.958, with corresponding F1 scores of 0.996 and 0.957, respectively. The external mAP scores for detection tasks were 0.952 and 0.926, with corresponding F1 scores of 0.929 and 0.905, respectively. Experts\u2019 reviews were generally consistent with the technical evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Conclusion<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>RAPID enables accurate image-derived anatomical labelling of CT series using topograms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Reliable anatomy-based CT series labelling is essential for clinical workflows, but the traditional approach that relies on inconsistent DICOM metadata requires manual review and limits scalability<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The three proposed deep learning models achieved high performance in identifying anatomical regions and landmarks, with expert assessments in agreement with the quantitative evaluation<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Deep Learning-based analysis of CT topograms with DICOM-derived spatial geometry, enables reliable and reproducible image-based anatomical labelling of CT series, reducing reliance on inconsistent DICOM attributes and improving data consistency and scalability for clinical applications<\/jats:italic>\n                      .\n           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