{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:19:57Z","timestamp":1785291597234,"version":"3.55.0"},"reference-count":20,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2022,10,5]]},"abstract":"<jats:p>Objective. There are more and more basketball competitions, to propose a classification method of thoracolumbar fractures to assist in the diagnosis of basketball injuries, to analyze the feasibility of its clinical application, and to improve the recovery rate. Methods. From February 2015 to May 2022, 1130 CT images of thoracolumbar fractures admitted to our hospital and affiliated hospital units due to basketball injuries were collected, and the image labeling system uniformly labeled them. All CT images were classified according to the AO spine classification of thoracolumbar injuries. In the ABC-type classification, 935 CT images were used for training and validation to optimize the deep learning system, including 815 training sets and 120 validation sets; the remaining 198 CT images were used as test sets for comparing the deep learning system and clinician\u2019s diagnosis. In the classification of subtype A, a total of 523 CT scans can be performed for training and validation to optimize the deep learning system, including 500 training sets and 23 validation sets; the remaining 94 CT images are used as test sets for comparing depth learning systems and clinicians\u2019 diagnostic results. Results. The deep learning system had a correct rate of ABC classification of fractures in 86.4%, with a kappa coefficient of 0.850 (<jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>P<\/a:mi>\n                        <a:mo>&lt;<\/a:mo>\n                        <a:mn>0.001<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula>); the correct rate of subtype A was 85.3%, with a kappa coefficient of 0.815 (<jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>P<\/c:mi>\n                        <c:mo>&lt;<\/c:mo>\n                        <c:mn>0.001<\/c:mn>\n                     <\/c:math>\n                  <\/jats:inline-formula>). Conclusion. The classification accuracy of thoracolumbar fractures based on deep learning is high. The method can assist in diagnosing CT images of thoracolumbar fractures and improve the current manual and complex diagnosis process.<\/jats:p>","DOI":"10.1155\/2022\/8747487","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T20:35:07Z","timestamp":1665002107000},"page":"1-8","source":"Crossref","is-referenced-by-count":13,"title":["A Classification Method for Thoracolumbar Vertebral Fractures due to Basketball Sports Injury Based on Deep Learning"],"prefix":"10.1155","volume":"2022","author":[{"given":"XiaoGan","family":"Chen","sequence":"first","affiliation":[{"name":"Wuxi Taihu University, Wuxi Jiangsu 214000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1050-6334","authenticated-orcid":true,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Wuxi 9th People\u2019s Hospital Affiliated to Soochow University, Wuxi Jiangsu 214000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.05.004"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2538465"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"4","first-page":"415","article-title":"Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields","author":"P. 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