ArticleJAMA surgery2022
Practical Computer Vision Application to Compute Total Body Surface Area Burn: Reappraising a Fundamental Burn Injury Formula in the Modern Era.
Article in JAMA surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Toward Better Response Assessment of Cutaneous Chronic Graft-Versus-Host Disease: A Report from the National Institutes of Health Consensus Project Task Force.Transplantation and cellular therapy · 2026Guideline
- Transfusion requirements as a surrogate marker of mortality and morbidity in adults with severe burns: A retrospective cohort study.Turkish journal of surgery · 2025Article
- Enhanced Trauma Video Review With Computer Vision: Trauma Resuscitation Phase Segmentation and Procedure Detection.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Automated computer-assisted surface area estimation in large surface burns: Is Fiji taking over burn care?JPRAS open · 2025Article
- Variability in Microbial Communities Driven by Particulate Matter on Human Facial Skin.Toxics · 2024Article
- Updates in SJS/TEN: collaboration, innovation, and community.Frontiers in medicine · 2023Review
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7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Critical burn management decisions rely on accurate percent total body surface area (%TBSA) burn estimation. Existing %TBSA burn estimation models (eg, Lund-Browder chart and rule of nines) were derived from a linear formula and a limited number of individuals a century ago and do not reflect the range of body habitus of the modern population. Objective: To develop a practical %TBSA burn estimation tool that accounts for exact burn injury pattern, sex, and body habitus. Design, Setting, and Participants: This population-based cohort study evaluated the efficacy of a computer vision algorithm application in processing an adult laser body scan data set. High-resolution surface anthropometry laser body scans of 3047 North American and European adults aged 18 to 65 years from the Civilian American and European Surface Anthropometry Resource data set (1998-2001) were included. Of these, 1517 participants (49.8%) were male. Race and ethnicity data were not available for analysis. Analyses were conducted in 2020. Main Outcomes and Measures: The contributory %TBSA for 18 body regions in each individual. Mobile application for real-time %TBSA burn computation based on sex, habitus, and exact burn injury pattern. Results: Of the 3047 individuals aged 18 to 65 years for whom body scans were available, 1517 (49.8%) were male. Wide individual variability was found in the extent to which major body regions contributed to %TBSA, especially in the torso and legs. Anterior torso %TBSA increased with increasing body habitus (mean [SD], 15.1 [0.9] to 19.1 [2.0] for male individuals; 15.1 [0.8] to 18.0 [1.7] for female individuals). This increase was attributable to increase in abdomen %TBSA (mean [SD], 5.3 [0.7] to 8.7 [1.8]) among male individuals and increase in abdomen (mean [SD], 4.6 [0.6] to 6.8 [1.7]) and pelvis (mean [SD], 1.5 [0.2] to 2.9 [0.9]) %TBSAs among female individuals. For most body regions, Lund-Browder chart and rule of nines estimates fell outside the population's measured interquartile ranges. The mobile application tested in this study, Burn Area, facilitated accurate %TBSA burn computation based on exact burn injury pattern for 10 sex and body habitus-specific models. Conclusions and Relevance: Computer vision algorithm application to a large laser body scan data set may provide a practical tool that facilitates accurate %TBSA burn computation in the modern era.
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