Comparison of the Lund and Browder table to computed tomography scan three-dimensional surface area measurement for a pediatric cohort

Rumpf, R. Wolfgang; Stewart, William C.; Martinez, Stephen K.; Gerrard, Chandra Y.; Adolphi, Natalie L.; Thakkar, Rajan K.; Coleman, Alan; Rajab, Adrian; Ray, William C.; Fabia, Renata

doi:10.1016/j.jss.2017.08.019

Cited by 14 publications

(14 citation statements)

References 20 publications

(17 reference statements)

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“…Although this approach seems straightforward, there are more than 25 formulae to estimate BSA based on studies of different populations [ 44 ]. When it comes to child BSA, we need completely different formulae for calculation, again with various degrees of accuracy [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study

Che¹,

Lai²,

Christian³

et al. 2021

JMIR Med Inform

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Background Accurate assessment of the percentage total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancies in estimating %TBSA by different doctors. Objective We propose a method, based on deep learning, for burn wound detection, segmentation, and calculation of %TBSA on a pixel-to-pixel basis. Methods A 2-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected from medical records and labeled by burn surgeons, and the data set was then input into 2 deep learning architectures, U-Net and Mask R-CNN, each configured with 2 different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another data set, which was also input into U-Net and Mask R-CNN to segment the hands. The %TBSA of burn wounds was then calculated by comparing the pixels of mask areas on images of the burn wound and hand of the same patient according to the rule of hand, which states that one’s hand accounts for 0.8% of TBSA. Results A total of 2591 images of burn wounds were collected and labeled to form the burn wound data set. The data set was randomly split into training, validation, and testing sets in a ratio of 8:1:1. Four hundred images of volar hands were collected and labeled to form the hand data set, which was also split into 3 sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with DC values of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons. Conclusions This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately, thereby helping to provide precise care to burn victims.

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Section: Discussionmentioning

confidence: 99%

Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study

Che¹,

Lai²,

Christian³

et al. 2021

JMIR Med Inform

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“…Morphological differences between the actual regional distribution of an individual’s surface area and the standard table values can best be represented as generic random error. These will affect any estimate that operates from population averages, but are unlikely to be large, for reasons identified by Rumpf et al 20 …”

Section: Methodsmentioning

confidence: 99%

“…Previous work demonstrates that the persistence of large errors in %TBSA estimates is unlikely to result from a systematic deviation of body morphology (eg, increasing obesity) from that of the population used to create the de facto-standard tables of regional percentages for different areas of the body. 20 The L&B table, originally created in the 1940s, appears to remain acceptably faithful to pediatric body regional areas. Both extremely obese and extremely thin individuals have regional body percentage areas that are close to those predicted by L&B, and errors induced by individual-to-individual variation (eg, having proportionally longer or shorter legs) cause much more significant deviations from the L&B predicted values than does obesity or emaciation.…”

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confidence: 99%

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A 1% TBSA Chart Reduces Math Errors While Retaining Acceptable First-Estimate Accuracy

Ray

Rajab

Alexander³

et al. 2021

Journal of Burn Care &Amp; Research

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Life-threatening and treatment-altering errors occur in estimates of the percentage of total body surface area burned (%TBSA burned) with unacceptable frequency. In response, numerous attempts have been made to improve the charts commonly used for %TBSA-burned estimation. Recent research shows that the largest errors in %TBSA-burned estimates probably come from sources other than inaccurate values in the charts. Here, we develop a taxonomy of the possible sources of error and their impact on %TBSA-burned estimates. Also, we observe that different caregivers have different estimation needs: First-responders require a rapid estimate with sufficient accuracy to enable them to begin care and determine patient transport options, while burn surgeons ordering skin grafts desire accuracy to the square centimeter, and can afford considerable time to attain that accuracy. These competing needs suggest that a one-tool-fits-all-caregivers approach is suboptimal. We therefore present a validated, simplified burn chart that minimizes one of the largest sources of random errors in %TBSA-burned estimates—simple calculation errors—while also being quick and requiring little training. NCHart-1 also enables simple consensus estimates, as well as separation of estimation subtasks across caregivers, leading to several potential improvements in mass casualty situations. Our results demonstrate that NCHart-1 possesses the accuracy necessary for first responders, while reliably producing results in less than 2 minutes. Of 76 healthcare professionals surveyed, a large majority indicated a preference for NCHart-1 over their previous methods for ease of both use and training. For clinical or commercial use of NCHart-1, please contact: tech.commercialization@nationwidechildrens.org

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“…Even though the LB chart (Figure 1) [10] is considered the most accurate of these three methods, it is still prone to errors [11]. Using the LB chart itself is not easy, especially in emergency situations [10].…”

Section: Introductionmentioning

confidence: 99%

Lund and Browder chart—modified versus original: a comparative study

Murari¹,

Singh

2019

Acute Crit Care

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BackgroundThe Lund and Browder (LB) chart is currently the most accurate and widely used chart to calculate total body surface area affected by a burn injury. However, it is not easy to use charts to calculate burn percentages because of the difficulty in performing mathematical calculations with the percentages attributed to various body regions that are only partially burned. It is also cumbersome to have to perform mental calculations, especially in emergency situations.MethodsWe compared results from the LB chart with a modified Lund and Browder (MLB) chart using 10 assessors on five different burn wounds each drawn on both charts. ResultsVariability of results was significantly reduced using the MLB chart compared to the LB chart.ConclusionsAssessments performed using the MLB chart are less variable than those using the LB chart. Using this chart will help burn care providers rapidly, accurately, and reliably estimate burn extent.

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Comparison of the Lund and Browder table to computed tomography scan three-dimensional surface area measurement for a pediatric cohort

Abstract: While today's population is more obese than those studied by L&B, their body region proportions scale surprisingly well. The primary error in %TBSA estimation is not due to changing physical proportions of today's children and may instead lie in the application of the L&B table.

Cited by 14 publications

References 20 publications

Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study

Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study

A 1% TBSA Chart Reduces Math Errors While Retaining Acceptable First-Estimate Accuracy

Lund and Browder chart—modified versus original: a comparative study

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