Namki Hong, Sang Wouk Cho, Young Han Lee, Chang Oh Kim, Hyeon Chang Kim, Yumie Rhee, William D Leslie, Steven R Cummings, Kyoung Min Kim
{"title":"Deep learning-based identification of vertebral fracture and osteoporosis in lateral spine radiographs and DXA VFA to predict incident fracture.","authors":"Namki Hong, Sang Wouk Cho, Young Han Lee, Chang Oh Kim, Hyeon Chang Kim, Yumie Rhee, William D Leslie, Steven R Cummings, Kyoung Min Kim","doi":"10.1093/jbmr/zjaf050","DOIUrl":null,"url":null,"abstract":"<p><p>Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) images may improve fracture risk assessment in older adults. In 26 299 lateral spine radiographs from 9276 individuals attending a tertiary-level institution (60% train set; 20% validation set; 20% test set; VERTE-X cohort), DL models were developed to detect prevalent vertebral fracture and osteoporosis. The pre-trained DL models from lateral spine radiographs were then fine-tuned in 30% of a DXA VFA dataset (KURE cohort), with performance evaluated in the remaining 70% test set. The area under the receiver operating characteristics curve (AUROC) for DL models to detect prevalent vertebral fracture and osteoporosis was 0.926 (95% CI 0.908-0.955) and 0.848 (95% CI 0.827-0.869) from VERTE-X spine radiographs, respectively, and 0.924 (95% CI 0.905-0.942) and 0.867 (95% CI 0.853-0.881) from KURE DXA VFA images, respectively. A total of 13.3% and 13.6% of individuals sustained an incident fracture during a median follow-up of 5.4 years and 6.4 years in the VERTE-X test set (n = 1852) and KURE test set (n = 2456), respectively. Incident fracture risk was significantly greater among individuals with DL-detected vertebral fracture (hazard ratios [HR] 3.23 [95% CI 2.51-5.17] and 2.11 [95% CI 1.62-2.74] for the VERTE-X and KURE test sets) or DL-detected osteoporosis (HR 2.62 [95% CI 1.90-3.63] and 2.14 [95% CI 1.72-2.66]), which remained significant after adjustment for clinical risk factors and femoral neck BMD. DL scores improved incident fracture discrimination and net benefit when combined with clinical risk factors. In summary, DL-detected prevalent vertebral fracture and osteoporosis in lateral spine radiographs and DXA VFA images enhanced fracture risk prediction in older adults.</p>","PeriodicalId":185,"journal":{"name":"Journal of Bone and Mineral Research","volume":" ","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bone and Mineral Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/jbmr/zjaf050","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) images may improve fracture risk assessment in older adults. In 26 299 lateral spine radiographs from 9276 individuals attending a tertiary-level institution (60% train set; 20% validation set; 20% test set; VERTE-X cohort), DL models were developed to detect prevalent vertebral fracture and osteoporosis. The pre-trained DL models from lateral spine radiographs were then fine-tuned in 30% of a DXA VFA dataset (KURE cohort), with performance evaluated in the remaining 70% test set. The area under the receiver operating characteristics curve (AUROC) for DL models to detect prevalent vertebral fracture and osteoporosis was 0.926 (95% CI 0.908-0.955) and 0.848 (95% CI 0.827-0.869) from VERTE-X spine radiographs, respectively, and 0.924 (95% CI 0.905-0.942) and 0.867 (95% CI 0.853-0.881) from KURE DXA VFA images, respectively. A total of 13.3% and 13.6% of individuals sustained an incident fracture during a median follow-up of 5.4 years and 6.4 years in the VERTE-X test set (n = 1852) and KURE test set (n = 2456), respectively. Incident fracture risk was significantly greater among individuals with DL-detected vertebral fracture (hazard ratios [HR] 3.23 [95% CI 2.51-5.17] and 2.11 [95% CI 1.62-2.74] for the VERTE-X and KURE test sets) or DL-detected osteoporosis (HR 2.62 [95% CI 1.90-3.63] and 2.14 [95% CI 1.72-2.66]), which remained significant after adjustment for clinical risk factors and femoral neck BMD. DL scores improved incident fracture discrimination and net benefit when combined with clinical risk factors. In summary, DL-detected prevalent vertebral fracture and osteoporosis in lateral spine radiographs and DXA VFA images enhanced fracture risk prediction in older adults.
深度学习(DL)识别侧位脊柱x线片和DXA椎体骨折评估(VFA)图像中的椎体骨折和骨质疏松症可能改善老年人骨折风险评估。来自9276名在三级医疗机构接受治疗的患者的26 299张侧位脊柱x线片(60%为培训组;20%验证集;20%测试集;VERTE-X队列),开发DL模型以检测常见的椎体骨折和骨质疏松症。然后在30%的DXA VFA数据集(KURE队列)中对来自侧位脊柱x线片的预训练DL模型进行微调,并在其余70%的测试集中评估其性能。DL模型检测椎骨骨折和骨质疏松的受者工作特征曲线下面积(AUROC)分别为VERTE-X脊柱x线片的0.926 (95% CI 0.908-0.955)和0.848 (95% CI 0.827-0.869), KURE DXA VFA图像的0.924 (95% CI 0.905-0.942)和0.867 (95% CI 0.853-0.881)。在VERTE-X测试组(n = 1852)和KURE测试组(n = 2456)中位随访期间,分别有13.3%和13.6%的个体发生偶发性骨折,随访时间分别为5.4年和6.4年。dl检测到椎体骨折(风险比[HR] 3.23 [95% CI 2.51-5.17]和2.11 [95% CI 1.62-2.74],对于vert - x和KURE测试集)或dl检测到骨质疏松症(风险比[HR] 2.62 [95% CI 1.90-3.63]和2.14 [95% CI 1.72-2.66])的个体,发生骨折的风险显著增加,在调整临床危险因素和股骨颈骨密度后仍然显著。当结合临床危险因素时,DL评分提高了事故骨折的鉴别和净收益。总之,在侧位脊柱x线片和DXA VFA图像中dl检测到的椎骨骨折和骨质疏松症增强了老年人骨折风险预测。
期刊介绍:
The Journal of Bone and Mineral Research (JBMR) publishes highly impactful original manuscripts, reviews, and special articles on basic, translational and clinical investigations relevant to the musculoskeletal system and mineral metabolism. Specifically, the journal is interested in original research on the biology and physiology of skeletal tissues, interdisciplinary research spanning the musculoskeletal and other systems, including but not limited to immunology, hematology, energy metabolism, cancer biology, and neurology, and systems biology topics using large scale “-omics” approaches. The journal welcomes clinical research on the pathophysiology, treatment and prevention of osteoporosis and fractures, as well as sarcopenia, disorders of bone and mineral metabolism, and rare or genetically determined bone diseases.