关键词: bone bones deep learning develop development fracture fracture risk assessment imaging machine learning muscle muscles musculoskeletal predict prediction predictive prospective cohort scanning tomography validate validation

Mesh : Humans Deep Learning Female Male Tomography, X-Ray Computed / methods Aged Spinal Fractures / diagnostic imaging Retrospective Studies Middle Aged Longitudinal Studies Spine / diagnostic imaging Muscle, Skeletal / diagnostic imaging injuries

来  源:   DOI:10.2196/48535   PDF(Pubmed)

Abstract:
BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations.
OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles.
METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles.
RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001).
CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.
摘要:
背景:随着老龄化人口的逐步增加,机会性计算机断层扫描(CT)扫描的使用正在增加,这可能是一种有价值的方法来获取有关老年人群肌肉和骨骼的信息。
目的:本研究的目的是通过使用椎骨和椎旁肌肉的图像来开发和外部验证基于CT的机会性骨折预测模型。
方法:这些模型是基于2010年至2019年对1214例腹部CT图像患者的回顾性纵向队列研究而开发的。这些模型在495名患者中进行了外部验证。这项研究的主要结果定义为在5年随访中识别椎骨骨折事件的预测准确性。图像模型是使用注意力卷积神经网络-递归神经网络模型从椎骨和椎旁肌肉的图像开发的。
结果:开发和验证组中患者的平均年龄分别为73岁和68岁,其中69.1%(839/1214)和78.8%(390/495)是女性,分别。在外部验证队列中,用于预测椎骨骨折的受试者操作员曲线下面积(AUROC)在椎骨和椎旁肌肉图像中优于仅骨骼图像中的面积(分别为0.827,95%CI0.821-0.833和0.815,95%CI0.806-0.824;P<.001)。这些图像模型的AUROC高于骨折风险评估模型(主要骨质疏松风险为0.810,0.780为髋部骨折风险)。对于使用年龄的临床模型,性别,BMI,使用类固醇,吸烟,可能的继发性骨质疏松症,2型糖尿病,艾滋病毒,丙型肝炎,肾功能衰竭,外部验证队列的AUROC值为0.749(95%CI0.736-0.762),低于使用椎骨和肌肉的图像模型(P<0.001)。
结论:使用椎骨和椎旁肌肉图像的模型比使用仅骨或临床变量图像的模型表现更好。机会性CT筛查可能有助于识别未来骨折风险高的患者。
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