模拟未来的骨矿物质密度:简单还是复杂?
E Erjiang1, John J Carey2, Tingyan Wang3
1School of Management, Guangxi Minzu Univeristy, Nanning, China.
Bone
|July 7, 2024
概括
预测骨质疏松性骨折是一个挑战. 新的深度学习模型在预测骨矿物质密度 (BMD) 变化方面表现有希望,可能改善骨质疏松症管理和患者的治疗结果.
科学领域:
- 老年学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 骨质疏松性骨折对全球健康造成重大负担,影响患者的福祉和医疗保健成本.
- 骨矿物质密度 (BMD) 评估对于识别骨质疏松症和骨折风险至关重要.
- 目前的临床算法缺乏有效的方法来将随时间的BMD变化纳入骨折风险预测中.
研究的目的:
- 将统计方法 (ZBM) 与基于深度学习 (DL) 的方法进行比较,以预测未来的骨矿物质密度 (BMD).
- 通过使用纵向DXA数据,评估这些模型在预测BMD方面的性能.
主要方法:
- 分析了来自2948名成年人 (40-90岁) 的纵向DXA数据,其中至少有两次部扫描.
- 一个ZBM模型使用参考组数据和最新的扫描预测了未来的BMD.
- 一种基于DL的方法结合了历史DXA数据,ZBM特征和多维纵向变量.
主要成果:
- 深度学习模型,特别是混合DL,在女性中显著优于ZBM模型.
- 在男性中,基于ZBM的模型的表现与基于DL的模型相比或更好.
- 这项研究包括2652名女性 (90%) 和296名男性 (10%).
结论:
- 基于DL和统计模型都可以使用纵向临床数据预测未来的BMD.
- 这些预测模型可以提高骨质疏松症评估的临床决策,包括重复BMD测试的时间.
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