在使用深森林模型与基因优化的可穿戴传感器布局后,mRUST估计骨骨折愈合后的内钉
Wenxuan Chen1, Mingxia Gong1, Fang Pu1
1Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Annals of biomedical engineering
|October 7, 2025
概括
一种新的深森林回归模型使用便携式内的足部压力数据估计了骨骨折愈合. 这种具有成本效益的系统为基于辐射的评估提供了一个客观的替代方案,以改善患者监测.
科学领域:
- 整形外科 整形外科 整形外科
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 轴骨折愈合评估传统上依赖于放射学方法 (mRUST评分),这涉及辐射暴露和主观解释.
- 需要客观,非侵入性和具有成本效益的方法来监测骨折愈合.
- 便携式内系统为持续的步态数据收集提供了一个有希望的途径.
研究的目的:
- 开发一种使用脚部压力数据估计mRUST分数的定量模型.
- 为便携式内系统确定一个最佳的,具有成本效益的传感器布局.
- 为评估骨轴骨折愈合提供一个客观和无辐射的方法.
主要方法:
- 开发了一个深森林回归 (DFR) 模型,通过从23名骨骨折患者收集的连续脚下压力数据来估计mRUST得分.
- 利用了103次后续访问中的515个步态分析段的数据.
- 一个遗传算法 (GA) 优化了传感器布局,并使用Shapley添加式解释 (SHAP) 评估了模型的可解释性.
主要成果:
- 确定了一个最佳的6传感器布局,实现了高的确定系数 (R2 = 0.902),与完整的99传感器阵列相比较.
- DFR模型在估计愈合阶段 (早期,中期,晚期) 中表现出高准确度.
- SHAP分析证实了临床相关性,显示了随着愈合的进展,传感器贡献从脚跟到前脚的转移.
结论:
- 使用GA优化的足部压力内的DFR模型提供了准确而客观的评估,用于在内膜钉后骨折愈合.
- 这种便携式,数据驱动的方法是放射学方法的可行替代方案.
- 该系统可及时且方便地对骨折恢复进行临床监测.
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