REACH-BMAS (Body-Map Auto-Segmentation): screening body-map drawings for chronic widespread pain

This is the authors' abstract. We don't add key points for this paper.

Dear Editor, Chronic widespread pain (CWP) is formally recognized as a distinct clinical diagnosis in the International Classification of Diseases, 11th Revision (ICD-11)(MG30.01).1,2 Despite this recognition, classification remains challenging, as current methods rely heavily on subjective patient reporting. Pen-on-paper pain drawings provide a cost-effective tool for documenting pain distribution3,4 but lack stardardized and objective interpretation. To address this gap, we developed an automated segmentation algorithm—REACH Body-Map Auto-Segmentation (REACH-BMAS)—to systematically quantify pain distribution and evaluate its performance against ICD-11 criteria for CWP. By automating the classification of pain drawings, REACH-BMAS enables consistent, scalable, and objective identification of affected individuals. We applied this method to data from the COMEBACK longitudinal cohort,5 demonstrating its potential to enhance chronic pain phenotyping in clinical and research contexts. We analyzed baseline data from 331 adults with chronic low back pain (cLBP) enrolled in the COMEBACK study, a NIH-funded longitudinal cohort (2021-2023). All participants reported pain persisting for more than 3 months and on >50% of days during the past 6 months. Standardized pen-on-paper body maps and validated patient-reported outcome measures (PROMs) were collected at baseline.