{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T23:59:05Z","timestamp":1786665545601,"version":"build-2736575974"},"reference-count":23,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of X-Ray Science and Technology"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Detecting and accurately diagnosing rib fractures in chest radiographs is a challenging and time-consuming task for radiologists. This study presents a novel deep learning system designed to automate the detection and segmentation of rib fractures in chest radiographs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed method combines CenterNet with HRNet v2 for precise fracture region identification and HRNet-W48 with contextual representation to enhance rib segmentation. A dataset consisting of 1006 chest radiographs from a tertiary hospital in Korea was used, with a split of 7:2:1 for training, validation, and testing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The rib fracture detection component achieved a sensitivity of 0.7171, indicating its effectiveness in identifying fractures. Additionally, the rib segmentation performance was measured by a dice score of 0.86, demonstrating its accuracy in delineating rib structures. Visual assessment results further highlight the model's capability to pinpoint fractures and segment ribs accurately.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This innovative approach holds promise for improving rib fracture detection and rib segmentation, offering potential benefits in clinical practice for more efficient and accurate diagnosis in the field of medical image analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/08953996251361041","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T08:52:43Z","timestamp":1753692763000},"page":"1027-1038","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Self-Assessment of acute rib fracture detection system from chest X-ray: Preliminary study for early radiological diagnosis"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9087-7783","authenticated-orcid":false,"given":"Hong Kyu","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Thoracic and Cardiovascular Surgery, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang-si, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyoung Soo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Thoracic and Cardiovascular Surgery, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang-si, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Gyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Internal medicine, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang-si, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2988-9785","authenticated-orcid":false,"given":"Jae Yong","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Hallym University Sacred Heart Hospital, Hallym University Medical Center, Anyang-si, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"1173","article-title":"Do we really need routine computed tomographic scanning in the primary evaluation of blunt chest trauma in patients with \u201cnormal\u201d chest radiograph?","volume":"51","author":"Exadaktylos AK","year":"2001","unstructured":"Exadaktylos AK, Sclabas G, Schmid SW, et al. 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