{"doi":"10.3390/ijerph22091456","title":"A Summary of Pain Locations and Neuropathic Patterns Extracted Automatically from Patient Self-Reported Sensation Drawings","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.","journal":"International Journal of Environmental Research and Public Health","year":2025,"id":547008,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":22.178386323199504,"corpus_rank":9377,"citation_count":1,"citer_count":1,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6029,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":66.6667,"fair_percentile":86.48731274839498,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1226692,"name":"Elisabetta de Rinaldis","orcid":null,"position":1,"is_corresponding":false},{"id":19298,"name":"Trisha F. Hue","orcid":"0000-0001-5922-5755","position":2,"is_corresponding":false},{"id":16210,"name":"Thomas A. Peterson","orcid":"0000-0002-2562-6574","position":3,"is_corresponding":false},{"id":19287,"name":"Jennifer E. Cummings","orcid":null,"position":4,"is_corresponding":false},{"id":296155,"name":"Abel Torres‐Espín","orcid":"0000-0002-9787-8738","position":5,"is_corresponding":false},{"id":19280,"name":"Jeannie F. Bailey","orcid":"0000-0003-4618-7512","position":6,"is_corresponding":false},{"id":19255,"name":"Jeffrey C. Lotz","orcid":"0000-0002-9654-0647","position":7,"is_corresponding":false},{"id":19256,"name":"REACH Investigators","orcid":null,"position":8,"is_corresponding":false},{"id":19283,"name":"Andrew Bishara","orcid":"0000-0002-5427-482X","position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-19T02:53:36.567932Z","pmid":"41007599","pmcid":"PMC12469790","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":83.3333,"fair_a":75.0,"fair_i":0.0,"fair_r":41.6667,"fair_zscore":1.2752,"fair_rationale":{"fair_score":66.67,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":83.33,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"https://doi.org/10.25934/PR00010819","grounded":true,"rationale":"The data 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[majority verdict 'no' (2/4 passes agreed)]","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. 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