疼痛位置和神经病变模式的总结,自动从患者自我报告的感觉图纸中提取
Andrew Bishara1,2, Elisabetta de Rinaldis3,4, Trisha F Hue5
1Department of Anesthesia and Perioperative Care, University of California, San Francisco, CA 94143, USA.
International journal of environmental research and public health
|September 27, 2025
概括
这项研究开发了一种自动化的方法来数字化疼痛图纸,创建慢性腰部疼痛 (LBP) 的热图. 这项技术有助于了解疼痛分布,以更好地管理LBP.
科学领域:
- 生物医学工程 生物医学工程
- 疼痛医学 医学 疼痛医学
- 数字健康数字健康
背景情况:
- 慢性腰痛 (LBP) 是全球残疾的主要原因之一.
- 当前的疼痛评估方法往往过于简化了LBP的复杂性质.
- 手动数字化疼痛图是耗时的,对于大规模研究来说是不一致的.
研究的目的:
- 开发和验证一个自动化的Python管道,用于数字化纸张疼痛图纸.
- 将患者绘制的疼痛图转换为机器可读的数据和热图.
- 为了使空间知情分析和慢性LBP的精确管理.
主要方法:
- 一个Python管道处理了332名成年人的扫描纸张疼痛图纸.
- 使用图像处理技术,包括PDF拉斯特化,图像注册 (ORB,RANSAC,OpenCV) 和边缘检测.
- 对疼痛,麻木和针针的二进制面具被生成,规范化并视觉化为热图.
主要成果:
- 自动化工作流成功将332名参与者的疼痛图纸数字化.
- 生成的热图确定了已知的LBP区域 (腰部,部) 和较少识别的区域 (侧部,下腹部).
- 分析显示,神经病倾斜疼痛模式中的腿部疼痛分布更广泛.
结论:
- 一个自动化工作流程有效地将手动疼痛图纸转换为规模化的数字热图.
- 这种方法为空间详细,精确的慢性LBP管理提供了实际基础.
- 数字化数据有助于直观地可视化症状重叠和未来大规模数据分析.
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