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

<b>Background</b> 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. <b>Methods</b> 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 <i>pdf2image</i> 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. <b>Results</b> 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. <b>Conclusions</b> 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.

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Publication Details

Journal
International Journal of Environmental Research and Public Health
Published
2025-09-19
DOI
https://doi.org/10.3390/ijerph22091456
Primary Topic
Musculoskeletal pain and rehabilitation
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Thomas A. Peterson, Jeffrey C. Lotz, Jeannie F. Bailey, Abel Torres‐Espín et al.
International Journal of Environmental Research and Public Health
Musculoskeletal pain and rehabilitation
article

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

Thomas A. Peterson, Jeffrey C. Lotz, Jeannie F. Bailey, Abel Torres‐Espín, Trisha F. Hue, Andrew Bishara, Jennifer E. Cummings, Elisabetta de Rinaldis
article en

Abstract

<b>Background</b> 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. <b>Methods</b> 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 <i>pdf2image</i> 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. <b>Results</b> 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. <b>Conclusions</b> 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.

International Journal of Environmental Research and Public HealthVol. 22(9)
University of Waterloo (CA), Università Campus Bio-Medico (IT), University of California, San Francisco (US)
National Institutes of Health, University of California, San Francisco
Openalex Percentile: Top 71%
Musculoskeletal pain and rehabilitation
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