使用深度学习侧位脊柱x线片和DXA VFA来估计脊柱年龄,以预测意外骨折和死亡率。

IF 6 Q2 GERIATRICS & GERONTOLOGY
Sang Wouk Cho, Namki Hong, Kyoung Min Kim, Young Han Lee, Chang Oh Kim, Hyeon Chang Kim, Yumie Rhee, Brian H Chen, William D Leslie, Steven R Cummings
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引用次数: 0

摘要

脊柱侧位x线片和DXA VFAs估计的脊柱年龄可能与骨折和死亡风险相关。在VERTE-X队列(n = 10,341,衍生集)和KURE队列(n = 3517,外部测试集)中,脊柱年龄比实足年龄更能区分椎体骨折和骨质疏松症的发生率。预测年龄差异与总体(调整HR [aHR] 1.22 / 1 SD增量,p
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality.

Spine age estimated from lateral spine radiographs and DXA VFAs could be associated with fracture and mortality risk. In the VERTE-X cohort (n = 10,341, derivation set) and KURE cohort (n = 3517; external test set), spine age discriminated prevalent vertebral fractures and osteoporosis better than chronological age. Predicted age difference was associated with overall (adjusted HR [aHR] 1.22 per 1 SD increment, p < 0.001), vertebral, non-vertebral incident fractures, and mortality (aHR 1.31, p = 0.001) during a median 6.6 years follow-up in KURE, independent of chronological age and covariates. Spine age to estimate FRAX hip fracture probabilities, instead of chronological age, improved the discriminatory performance for incident hip fracture (AUROC 0.83 vs. 0.78, p = 0.027). Shorter height, lower femoral neck BMD, diabetes, vertebral fractures, and surgical prosthesis were associated with higher predicted age difference, explaining 40% of variance. Spine age estimated from lateral spine radiographs and DXA VFA enhanced fracture risk assessment and mortality prediction over chronological age.

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