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The dataset was divided into a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009306) and an internal testing set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200977). An external testing set of 50 patients from the public RibFrac dataset was included. Fractures were classified into severe and non-severe categories. A modified YOLO-based deep learning model was developed for detection and grading. Performance was compared with thoracic surgeons using precision, recall, mAP50, and F1 score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The deep learning model showed excellent performance in diagnosing fresh rib fractures. For all fractures types in internal test set, the precision, recall, mAP50, and F1 score were 0.963, 0.934, 0.972, and 0.948, respectively. The model outperformed thoracic surgeons of varying experience levels (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed deep learning model can automatically detect and grade fresh rib fractures with accuracy comparable to that of physicians. This model helps improve diagnostic accuracy, reduce physician workload, save medical resources, and strengthen health care in resource-limited areas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-025-01641-0","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T19:07:12Z","timestamp":1742843232000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning"],"prefix":"10.1186","volume":"25","author":[{"given":"Tongxin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyi","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanghong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luya","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junliang","family":"Che","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shulei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"1641_CR1","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.jcma.2016.01.006","volume":"79","author":"FC-F Lin","year":"2016","unstructured":"Lin FC-F, Li R-Y, Tung Y-W, Jeng K-C, Tsai SC-S. 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Ethical approval for this retrospective study was obtained from the Ethics Committee of the First Affiliated Hospital of Army Medical University (Ethics Committee Name: Ethics Committee of the First Affiliated Hospital of Army Medical University, Ethics ID: KY2023062) and the Ethics Committee of Chongqing Dianjiang People\u2019s Hospital (Ethics Committee of Chongqing Dianjiang People\u2019s Hospital, Ethics ID: DYLL-LW-2023-03).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Given the retrospective nature of the study, the requirement for informed consent was waived.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human ethics and consent to participate"}},{"value":"NA.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"98"}}