A Summary of Pain Locations and Neuropathic Patterns Extracted Automatically from Patient Self-Reported Sensation Drawings.

3区 综合性期刊
Andrew Bishara, Elisabetta de Rinaldis, Trisha F Hue, Thomas Peterson, Jennifer Cummings, Abel Torres-Espin, Jeannie F Bailey, Jeffrey C Lotz, Reach Investigators
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引用次数: 0

Abstract

Background Chronic low-back pain (LBP) is the largest contributor to disability worldwide, yet many assessments still reduce a complex, spatially distributed condition to a single 0-10 score. Body-map drawings capture location and extent of pain, but manual digitization is too slow and inconsistent for large studies or real-time telehealth. Methods Paper pain drawings from 332 adults in the multicenter COMEBACK study (four University of California sites, March 2021-June 2023) were scanned to PDFs. A Python pipeline automatically (i) rasterized PDF pages with pdf2image v1.17.0; (ii) resized each scan and delineated anterior/posterior regions of interest; (iii) registered patient silhouettes to a canonical high-resolution template using ORB key-points, Brute-Force Hamming matching, RANSAC inlier selection, and 3 × 3 projective homography implemented in OpenCV; (iv) removed template outlines via adaptive Gaussian thresholding, Canny edge detection, and 3 × 3 dilation, leaving only patient-drawn strokes; (v) produced binary masks for pain, numbness, and pins-and-needles, then stacked these across subjects to create pixel-frequency matrices; and (vi) normalized matrices with min-max scaling and rendered heat maps. RGB composites assigned distinct channels to each sensation, enabling intuitive visualization of overlapping symptom distributions and for future data analyses. Results Cohort-level maps replicated classic low-back pain hotspots over lumbar paraspinals, gluteal fold, and posterior thighs, while exposing less-recognized clusters along the lateral hip and lower abdomen. Neuropathic-leaning drawings displayed broader leg involvement than purely nociceptive patterns. Conclusions Our automated workflow converts pen-on-paper pain drawings into machine-readable digitized images and heat maps at the population scale, laying practical groundwork for spatially informed, precision management of chronic LBP.

Abstract Image

Abstract Image

Abstract Image

从患者自我报告的感觉图中自动提取的疼痛位置和神经病变模式摘要。
慢性腰痛(LBP)是全球范围内导致残疾的最大因素,然而许多评估仍然将这一复杂的、空间分布的疾病降低到单一的0-10分。人体地图绘图可以捕捉到疼痛的位置和程度,但对于大型研究或实时远程医疗来说,手动数字化速度太慢,也不一致。方法将332名参与多中心康复研究的成人的纸质疼痛图扫描成pdf格式(四个加州大学站点,2021年3月- 2023年6月)。Python管道自动(i)栅格化PDF页面pdf2image v1.17.0;(ii)调整每次扫描的大小并划定感兴趣的前后区域;(iii)将患者轮廓注册到规范的高分辨率模板中,使用ORB关键点,Brute-Force Hamming匹配,RANSAC内链选择和OpenCV中实现的3 × 3投影单应性;(iv)通过自适应高斯阈值分割、Canny边缘检测和3 × 3扩张去除模板轮廓,只留下患者绘制的笔画;(v)产生的疼痛,麻木和针二进制掩模,然后堆叠这些跨科目创建像素频率矩阵;(vi)具有最小-最大缩放和渲染热图的归一化矩阵。RGB复合材料为每个感觉分配了不同的通道,使重叠症状分布的直观可视化和未来的数据分析成为可能。结果:队列水平的图谱复制了腰椎棘旁、臀襞和大腿后部的典型腰痛热点,同时暴露了沿髋外侧和下腹部的鲜为人知的集群。与纯粹的伤害模式相比,神经性学习图显示了更广泛的腿部参与。我们的自动化工作流程将纸上的疼痛图转换为机器可读的数字化图像和人群尺度的热图,为慢性腰痛的空间信息精确管理奠定了实践基础。
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来源期刊
自引率
0.00%
发文量
14422
期刊介绍: International Journal of Environmental Research and Public Health (IJERPH) (ISSN 1660-4601) is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes, and short communications in the interdisciplinary area of environmental health sciences and public health. It links several scientific disciplines including biology, biochemistry, biotechnology, cellular and molecular biology, chemistry, computer science, ecology, engineering, epidemiology, genetics, immunology, microbiology, oncology, pathology, pharmacology, and toxicology, in an integrated fashion, to address critical issues related to environmental quality and public health. Therefore, IJERPH focuses on the publication of scientific and technical information on the impacts of natural phenomena and anthropogenic factors on the quality of our environment, the interrelationships between environmental health and the quality of life, as well as the socio-cultural, political, economic, and legal considerations related to environmental stewardship and public health. The 2018 IJERPH Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJERPH. See full details at http://www.mdpi.com/journal/ijerph/awards.
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