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实用计算机视觉在计算体表面积烧伤中的应用:在现代重新评估一项基本烧伤损伤公式。

Practical Computer Vision Application to Compute Total Body Surface Area Burn: Reappraising a Fundamental Burn Injury Formula in the Modern Era.

机构信息

Department of Surgery, Stanford University, Stanford, California.

Surgeons Writing About Trauma, Stanford University, Stanford, California.

出版信息

JAMA Surg. 2022 Feb 1;157(2):129-135. doi: 10.1001/jamasurg.2021.5848.

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.

摘要

重要性

关键的烧伤管理决策依赖于准确的体表面积百分比(%TBSA)烧伤估计。现有的%TBSA 烧伤估计模型(例如 Lund-Browder 图表和九分法)是由一个线性公式和 100 多年前的少数人推导出来的,它们并没有反映出现代人口的体型范围。

目的

开发一种实用的%TBSA 烧伤估计工具,该工具考虑了精确的烧伤损伤模式、性别和体型。

设计、设置和参与者:这项基于人群的队列研究评估了计算机视觉算法应用在处理成人激光体扫描数据集方面的功效。从民用美国和欧洲表面人体测量资源数据集(1998-2001)中纳入了 3047 名 18 至 65 岁的北美和欧洲成年人的高分辨率表面人体测量激光体扫描数据。其中,1517 名参与者(49.8%)为男性。种族和民族数据不适于分析。分析于 2020 年进行。

主要结果和措施

每个人的 18 个身体区域的贡献%TBSA。基于性别、体型和确切的烧伤损伤模式的实时%TBSA 烧伤计算移动应用程序。

结果

在有体扫描的 3047 名 18 至 65 岁的个体中,1517 名(49.8%)为男性。主要身体区域对%TBSA 的贡献程度存在广泛的个体差异,尤其是在躯干和腿部。躯干前区的%TBSA 随着体型的增加而增加(男性个体中,平均值[标准差]为 15.1[0.9]至 19.1[2.0];女性个体中,平均值[标准差]为 15.1[0.8]至 18.0[1.7])。这种增加归因于男性个体中腹部%TBSA 的增加(平均值[标准差]为 5.3[0.7]至 8.7[1.8])和腹部(平均值[标准差]为 4.6[0.6]至 6.8[1.7])和骨盆(平均值[标准差]为 1.5[0.2]至 2.9[0.9])%TBSA 的增加。对于大多数身体区域,Lund-Browder 图表和九分法的估计值落在人群测量的四分位间距之外。本研究中测试的移动应用程序 Burn Area 基于精确的烧伤损伤模式,为 10 个性别和体型特定的模型提供了准确的%TBSA 烧伤计算。

结论和相关性

计算机视觉算法应用于大型激光体扫描数据集,可能提供一种实用的工具,便于在现代时代进行准确的%TBSA 烧伤计算。

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