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

Andrew Bishara (), Elisabetta de Rinaldis, Trisha F. Hue, Thomas Peterson, Jennifer Cummings, Abel Torres-Espin, Jeannie F. Bailey, Jeffrey C. Lotz and Investigators Reach
Additional contact information
Andrew Bishara: Department of Anesthesia and Perioperative Care, University of California, San Francisco, CA 94143, USA
Elisabetta de Rinaldis: Department of Orthopaedic Surgery, University of California, San Francisco, CA 94143, USA
Trisha F. Hue: Department of Epidemiology & Biostatistics, University of California, San Francisco, CA 94143, USA
Thomas Peterson: Department of Orthopaedic Surgery, University of California, San Francisco, CA 94143, USA
Jennifer Cummings: Department of Orthopaedic Surgery, University of California, San Francisco, CA 94143, USA
Abel Torres-Espin: Department of Neurosurgery, University of California, San Francisco, CA 94143, USA
Jeannie F. Bailey: Department of Orthopaedic Surgery, University of California, San Francisco, CA 94143, USA
Jeffrey C. Lotz: Department of Orthopaedic Surgery, University of California, San Francisco, CA 94143, USA
Investigators Reach: Membership of the REACH Investigators is provided in the Acknowledgments.

IJERPH, 2025, vol. 22, issue 9, 1-14

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.

Keywords: chronic low back pain; body-map digitization; spatial pain phenotyping; automated image processing; digital health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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