基于几何和材料参数的双能x线吸收仪对骨质疏松性骨折的综合数据增强分类。

IF 3.9 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung
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引用次数: 0

摘要

背景:骨质疏松患者骨折风险评估是制定早期对策、预防不适和住院治疗的必要条件。目前的方法,如骨折风险评估工具(FRAX),提供的是5- 10年的风险评估,而不是评估骨骼当前的健康状况。方法:数据库由亚洲大学医学中心于2017 - 2021年收集。纳入9260例患者,年龄55 - 99岁,包括242例股骨骨折(FX)病例和9018例非骨折(NFX)病例。为了模拟骨骼当前健康状况与常见FXs的关联,使用二维双能x射线吸收仪(2D-DXA)分析结果训练了三种预测算法——极端梯度增强(XGB)、支持向量机和多层感知器,并随后对其进行基准测试。XGB分类器被证明是最有效的,然后使用自适应合成过采样器生成的合成数据进一步改进,以平衡FX和NFX类,并增强边界清晰度,以获得更好的分类精度。结果:在原始数据上训练的XGB模型具有良好的预测能力,在测试用例上的曲线下面积(AUC)为0.78,F1得分为0.71。合成数据的加入提高了分类的特异性和敏感性,AUC为0.99,F1评分为0.98。结论:提出的方法表明,当前的骨骼健康可以通过2D-DXA分析的后处理结果进行评估。此外,合成数据还可以通过平衡多数类和少数类来稳定不均匀的数据库,从而显着提高分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters.

Background: Bone fracture risk assessment for osteoporotic patients is essential for implementing early countermeasures and preventing discomfort and hospitalization. Current methodologies, such as Fracture Risk Assessment Tool (FRAX), provide a risk assessment over a 5- to 10-year period rather than evaluating the bone's current health status.

Methods: The database was collected by Ajou University Medical Center from 2017 to 2021. It included 9,260 patients, aged 55 to 99, comprising 242 femur fracture (FX) cases and 9,018 non-fracture (NFX) cases. To model the association of the bone's current health status with prevalent FXs, three prediction algorithms-extreme gradient boosting (XGB), support vector machine, and multilayer perceptron-were trained using two-dimensional dual-energy X-ray absorptiometry (2D-DXA) analysis results and subsequently benchmarked. The XGB classifier, which proved most effective, was then further refined using synthetic data generated by the adaptive synthetic oversampler to balance the FX and NFX classes and enhance boundary sharpness for better classification accuracy.

Results: The XGB model trained on raw data demonstrated good prediction capabilities, with an area under the curve (AUC) of 0.78 and an F1 score of 0.71 on test cases. The inclusion of synthetic data improved classification accuracy in terms of both specificity and sensitivity, resulting in an AUC of 0.99 and an F1 score of 0.98.

Conclusion: The proposed methodology demonstrates that current bone health can be assessed through post-processed results from 2D-DXA analysis. Moreover, it was also shown that synthetic data can help stabilize uneven databases by balancing majority and minority classes, thereby significantly improving classification performance.

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来源期刊
Endocrinology and Metabolism
Endocrinology and Metabolism Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
6.60
自引率
5.90%
发文量
145
审稿时长
24 weeks
期刊介绍: The aim of this journal is to set high standards of medical care by providing a forum for discussion for basic, clinical, and translational researchers and clinicians on new findings in the fields of endocrinology and metabolism. Endocrinology and Metabolism reports new findings and developments in all aspects of endocrinology and metabolism. The topics covered by this journal include bone and mineral metabolism, cytokines, developmental endocrinology, diagnostic endocrinology, endocrine research, dyslipidemia, endocrine regulation, genetic endocrinology, growth factors, hormone receptors, hormone action and regulation, management of endocrine diseases, clinical trials, epidemiology, molecular endocrinology, neuroendocrinology, neuropeptides, neurotransmitters, obesity, pediatric endocrinology, reproductive endocrinology, signal transduction, the anatomy and physiology of endocrine organs (i.e., the pituitary, thyroid, parathyroid, and adrenal glands, and the gonads), and endocrine diseases (diabetes, nutrition, osteoporosis, etc.).
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