通过先进的脚下压力分析预测糖尿病外围神经病变:一种机器学习方法
Mehewish Musheer Sheikh1, Mamatha Balachandra2, Narendra V G1
1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
Scientific reports
|July 2, 2025
概括
这项研究开发了一种使用足底压力分析来预测糖尿病外围神经病变的方法,这是糖尿病足的关键因素. 静态压力测量在识别神经病变方面达到100%的准确性,有助于早期发现和预防.
科学领域:
- 生物医学工程 生物医学工程
- 糖尿病学 糖尿病学
- 计算医学是一种计算医学.
背景情况:
- 糖尿病足 (DFU) 是糖尿病的严重并发症,通常导致截肢.
- 糖尿病患者的神经病变会导致足部无感,增加脚部组织压力和风险.
- 早期发现糖尿病外围神经病变对于预防DFU至关重要.
研究的目的:
- 提出一种新的脚部压力分布分析方法,用于预测糖尿病外围神经病变.
- 评估静态与动态脚部压力评估的有效性.
- 开发一个人工智能驱动的系统来检测神经病变和足类别.
主要方法:
- 从使用Win-Track平台的86名糖尿病患者收集了临床和脚部压力数据.
- 采用自动图像处理算法将脚底压力图像细分为前脚和后脚区域.
- 使用了机器学习模型 (渐变增强,随机森林,决策树) 和可解释的AI技术 (SHAP,Eli5,解释).
主要成果:
- 静态脚下压力分析显示出优于动态方法的性能,在预测神经病变方面达到100%的准确性.
- 渐变增强模型显示了最高的准确性 (88%动态,100%静态).
- 根据前脚和后脚的压力比率开发了一种脚分类系统,将脚分为平,正规或弧形.
结论:
- 开发的脚部压力分析方法显示了早期糖尿病外围神经病变检测的巨大潜力.
- 可解释的人工智能增强了模型的解释性,为特征的重要性提供了洞察力.
- 这种方法可以帮助风险分层,DFU预防,并减少糖尿病患者的截肢率.
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