Measurement of body surface area in children with liver disease by a novel three-dimensional body scanning device

System for Estimation of Human Anthropometric Parameters Based on Data from Kinect v2 Depth Camera

Anthropometric measurements of the human body are an important problem that affects many aspects of human life. However, anthropometric measurement often requires the application of an appropriate measurement procedure and the use of specialized, sometimes expensive measurement tools. Sometimes the measurement procedure is complicated, time-consuming, and requires properly trained personnel. This study aimed to develop a system for estimating human anthropometric parameters based on a three-dimensional scan of the complete body made with an inexpensive depth camera in the form of the Kinect v2 sensor. The research included 129 men aged 18 to 28. The developed system consists of a rotating platform, a depth sensor (Kinect v2), and a PC computer that was used to record 3D data, and to estimate individual anthropometric parameters. Experimental studies have shown that the precision of the proposed system for a significant part of the parameters is satisfactory. The largest error was found in the waist circumference parameter. The results obtained confirm that this method can be used in anthropometric measurements.

Estimation of body surface area in neonates, infants, and children using body weight alone

Background

The aim of this study was to use Body Surface Area (BSA) data calculated with the Mosteller equation to test potential new equations that estimate BSA using Body Weight (BW) alone in children aged 0–18 years.

Mosteller’s equation, the golden standard at our hospital, was used to calculate the BSA in infants and children aged 0–18 years using BW and height data from 27,440 hospital visits by 20,635 patients over one year.

Methods

The best fit of three nonlinear regression equations (third-order polynomial, Meeh-type, and modified Boyd self-adjusting-type) to a plot of the calculated Mosteller BSA values versus BW was then investigated. The correlation between the BSA values estimated by these equations and the Mosteller BSA values was established by the Spearman rank correlation test. Bias and precision were evaluated as outlined by Sheiner and Beal. Measured and estimated BSA values were compared using the Eksborg plot.

Results

The estimated BSA values from all three equations and the BSA values from the Mosteller equation were closely correlated (P < .0001). The third-order polynomial and Meeh-type equations overestimated BSA by 0.13% and 0.40%, respectively, while the Boyd self-adjusted-type equation underestimated BSA by 0.060%. For the entire pediatric population, the best fit was obtained with the Meeh-type equation: 99.2% of the Meeh/Mosteller BSA ratios were within the range of 0.9–1.1 when compared with 98.3% and 97.2% for the polynomial and Boyd-type equations, respectively.

Conclusion

A single Meeh-type equation can be used to predict the results of Mosteller equation when H is not available with high precision and accuracy in children aged 0–18 years, including term neonates. We now plan to include the results of this study in CPOE systems in Sweden to improve drug dosage in all children.

The Mystery of the Z-Score

Reliable methods for measuring the thoracic aorta are critical for determining treatment strategies in aneurysmal disease. Z-scores are a pragmatic alternative to raw diameter sizes commonly used in adult medicine. They are particularly valuable in the pediatric population, who undergo rapid changes in physical development. The advantage of the Z-score is its inclusion of body surface area (BSA) in determining whether an aorta is within normal size limits. Therefore, Z-scores allow us to determine whether true pathology exists, which can be challenging in growing children. In addition, Z-scores allow for thoughtful interpretation of aortic size in different genders, ethnicities, and geographical regions. Despite the advantages of using Z-scores, there are limitations. These include intra- and inter-observer bias, measurement error, and variations between alternative Z-score nomograms and BSA equations. Furthermore, it is unclear how Z-scores change in the normal population over time, which is essential when interpreting serial values. Guidelines for measuring aortic parameters have been developed by the American Society of Echocardiography Pediatric and Congenital Heart Disease Council, which may reduce measurement bias when calculating Z-scores for the aortic root. In addition, web-based Z-score calculators have been developed to aid in efficient Z-score calculations. Despite these advances, clinicians must be mindful of the limitations of Z-scores, especially when used to demonstrate beneficial treatment effect. This review looks to unravel the mystery of the Z-score, with a focus on the thoracic aorta. Here, we will discuss how Z-scores are calculated and the limitations of their use.

Evaluation of five formulae for estimating body surface area of nigerian children

BACKGROUND

Physiological functions are often assessed by standardizing for body surface area (BSA) to avoid excessive variation in calculations in pediatric practice.

AIM

To explore the suitability of existing formulae for estimating the BSA of Nigerian children.

SUBJECTS AND METHODS

This cross-sectional study involved healthy children in a Local Government Area, Oyo State, Nigeria. The BSAs of 2745 children were calculated using the formulae by Boyd, Mosteller, Gehan and George, Haycock, and Dubois-DuBois, and the sixth arithmetic mean of these five formulae (mean-BSA) was performed. The outcome of interest was agreement between estimated BSA and mean-BSA for each method. The performance of each BSA estimation method was compared using bias, root mean square error and Bland-Altman plots of agreement.

RESULTS

The study participants comprised of 1229 males and 1246 females with mean (standard deviation) ages of 6.3 (3.0) years and 6.6 (3.1) years respectively (P = 0.01). Reference values for BSA estimates by gender were proposed each age group. Furthermore, BSA estimates from Boyd's and Mosteller's formulae were most similar to the mean-BSA with mathematically perfect correlations. The degree of deviation of BSA estimates from DuBois was largest with a remarkable increase at ages <6 years.

CONCLUSION

Formulae by Boyd and Mosteller are the best BSA estimate for Nigerian children among the existing formulae.

Estimation of body surface area in various childhood ages--validation of the Mosteller formula

AIM

The aim of the present study was to validate the Mosteller formula for the estimation of body surface area (BSA) in various childhood ages. Many physiological processes including drug metabolism correlate with values for BSA. In addition, dosing of many drugs, especially drugs with low therapeutic index, for example, anti-neoplastics, are based on estimated values of BSA.

METHODS

Published data from measured BSA in 268 children and infants (median age: 8 month; range: 0-18 years) were compared with BSA values estimated by the Mosteller formula. Correlation between estimated and measured BSA values was performed by the Spearman rank correlation. Bias and precision were evaluated as outlined by Sheiner and Beal. Measured and estimated BSA values were compared by the Eksborg's plot.

RESULTS

Measured values of BSA and BSA values estimated by the Mosteller formula were closely correlated (r(s) = 0.973; p < 0.0001). The formula of Mosteller had with a precision of 9.38% and underestimated BSA by 4.06%. The quotients Estimated/Measured BSA were within the range 0.9-1.1 in 71.3% of the observations, but deviation up to 35% occurred.

CONCLUSION

The Mosteller formula underestimates BSA in the paediatric population and must be used with precautions because of low precision, most pronounced in neonates and infants.

Technical note: Criterion validity of whole body surface area equations: a comparison using 3D laser scanning

Measurements of whole body surface area (WBSA) have important applications in numerous fields including biological anthropology, clinical medicine, biomechanics, and sports science. Currently, WBSA is most often estimated using predictive equations due to the complex and time consuming methods required for direct measurement. The main aim of this study was to identify whether there were significant and meaningful differences between WBSA measurements taken using a whole body three-dimensional (3D) scanner (criterion measure) and the estimates derived from each WBSA equation identified from a systematic review. The study also aimed to determine whether differences varied according to body mass index (BMI), sex, or athletic status. Fifteen WBSA equations were compared with direct measurements taken on 1,714 young adult subjects, aged 18-30 years, using the Vitus Smart 3D whole body scanner, including 1,452 subjects (753 males, 699 females) from the general Australian population and 262 rowers (148 males, 114 females). Mixed-design analysis of variances determined significant differences and accuracy was quantified using Bland-Altman analysis and effect sizes. Thirteen of the 15 equations overestimated WBSA. With a few exceptions, equations were accurate with a low-systematic error (bias ≤2%) and low-random error (standard deviation of the differences 1.5-3.0%). However, BMI did have a substantial impact with the accuracy of some WBSA equations varying between the four BMI categories. The Shuter and Aslani: Eur J Appl Physiol 82 (2000) 250-254 equation was identified as the most accurate equation and should be used for Western populations 18-30 years of age. Care must be taken when deciding which equation to use when estimating WBSA.

Three-dimensional body scanning: a new method to estimate body surface area in neonates

BACKGROUND

Body surface area (BSA) is usually estimated by calculation with mathematical formulae. Three-dimensional body scanning (3D scan) offers a suitable alternative.

OBJECTIVES

We determined the BSA in healthy term and near-term neonates by 3D scanning. This system should be useful in the setting of intensive care medicine.

METHODS

The measuring system consisted of a projector, two cameras, mirrors and a computer, and used the fringe projection technique with visible light. The infants were examined in a supine position; the hidden parts of the bodies were corrected for using a mathematical factor developed with a baby doll model. Results of the 3D scans were compared with those from five mathematical formulae for each subject.

RESULTS

A total of 209 infants were studied by 3D scanning, of whom 53 had acceptable images and were selected for further analysis. The mean BSA was 2,139 cm(2) (SD 223.72). The minimal BSA was 1,587 cm(2), the maximal 2,670 cm(2), with a good correlation to body weight and length. One mathematical formula (Du Bois and Du Bois) showed a distinct underestimation of BSA compared to 3D scanning, the others an overestimation. Mean percentage similarity was from 96.8 to 100.9%.

CONCLUSIONS

3D scanning is an accurate and practical method to estimate BSA in newborns. Individual and repeated measurements from day to day are possible. Further studies are warranted in preterm and sick neonates.

Mining Three-Dimensional Anthropometric Body Surface Scanning Data for Hypertension Detection

Hypertension is a major disease, being one of the top ten causes of death in Taiwan. The exploration of three-dimensional (3-D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a prediction model for hypertension using anthropometric body surface scanning data. This research adopts classification trees to reveal the relationship between a subject's 3-D scanning data and hypertension disease using the hybrid of the association rule algorithm (ARA) and genetic algorithms (GAs) approach. The ARA is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. The proposed approach was experimented and compared with a regular genetic algorithm in predicting a subject's hypertension disease. Better computational efficiency and more accurate prediction results from the proposed approach are demonstrated.

Association of hematological factors with components of the metabolic syndrome in older and younger adults

BACKGROUND AND AIMS

This study retrospectively examined the characteristics of metabolic syndrome in an aged population and assessed the risk factors for these subjects.

METHODS

A total of 1332 aged subjects (> or =65 years; mean age 71.0+/-5.0 years) were enrolled from 6903 subjects recruited from the Department of Health Management at Chang Gung Medical Center. Of these 6903 subjects, 1665 (814 females and 851 males) were diagnosed with metabolic syndrome. Whole body three-dimensional (3- D) laser scanning was employed for anthropometric measurements. Furthermore, health index (HI) was derived by the following equation: HI = (body weight x 2 x waist area) / [body height2 x (breast area + hip area)].

RESULTS

Among the 6903 subjects, no significant difference in gender was noted between groups with and without metabolic syndrome (p=0.142). For subjects >64 years, the incidence of metabolic syndrome in females is higher than in males. Subjects are categorized into four groups based on age and whether they had metabolic syndrome. Group A (4402 cases) consists of subjects <65 years old without metabolic syndrome. Group B (836 cases) comprises subjects >64 years old and without metabolic syndrome. Group C (1169 cases) contains subjects <65 years old with metabolic syndrome and group D (496 cases) is composed of subjects >64 years old with metabolic syndrome. Of the aged 1332 subjects, 595 were females (mean age, 70.6+/-4.6 years) and 737 were males (mean age, 71.3+/-5.3years), 37.2% (496/1332) had metabolic syndrome, 19.9% had DM and 21.8% had hypertension. These subjects had decreased BMI with age. Additionally, WHR peaked at an age range of 75-79 years. Of the aged subjects, also overweight, 42.8% and 33.6% were diagnosed with hypertension and DM, respectively; both ratios higher than those for non-overweight subjects (25.3% and 26.2%, respectively). Of the four groups in this study, the ratios for DM, hypertension, and WHR, HI, and LDL levels progressively increased through groups A to D. WBC count differs statistically significantly between these groups. Statistical analysis of WBC count, RBC and hemoglobin (Hb) with different parameters demonstrates significant elevation of WBC counts with the components of metabolic syndrome in aged subjects.

CONCLUSIONS

WBC count, RBC count and Hb are associated with metabolic syndrome components in younger and old adults of both genders. The incidence of metabolic syndrome marker increased after menopause onset in the female population in this study.

Correlation of the dysmetabolic risk factors with different anthropometric measurements

Metabolic syndrome is a common disorder in Taiwan. For this study 431 subjects were randomly selected from visitors to the Department of Health Management. Blood pressure, blood glucose, lipid, uric acid levels and anthropometric measurements with immunoreactive insulin (IRI) and leptin levels were all correlated. We randomly selected 431 subjects who visited the Department of Health Management. Whole body three-dimensional (3-D) laser scanner scans were employed for the anthropometric measurements. The metabolic index (MI) was designed using anthropometric parameters. Of the 431 subjects, 50% had displayed a body mass index (BMI) equal to or exceeding 25 kg/m2. Pearson correlation coefficient and multiple regression analysis revealed that MI constituted another index for correlating metabolic parameters by comparing MI with BMI and waist circumference to hip circumference ratio (WHR). Most data related to metabolic syndrome showed statistically significant differences between high and low IRI groups, comprising uric acid, total cholesterol, fasting plasma glucose, triglyceride, LDL, Chol/HDL ratio, and LDL/HDL ratio. Both IRI and leptin revealed statistical association with BMI, WHR, waist cross section area to hip cross section area ratio (WHAR), and MI in the study. Hypercholesterolemia appeared in 14.6% of the subjects. Elevated low-density lipoprotein (> or = 130 mg/dL) affected 36.9% of the subjects. In conclusion, MI calculated from 3-D body scanner correlated with many important metabolic risk factors and associated with clinical disorders like DM, hyperlipidemia, hyperuricemia and hypertension.

Application of three-dimensional body scanner: observation of prevalence of metabolic syndrome

BACKGROUND AND AIMS

This retrospective cross-sectional study correlates blood pressure, blood glucose, lipid and uric acid levels with anthropometric measurements.

METHODS

A total of 3975 visitors to the Department of Health Management were randomly selected to participate in this cross-sectional study. Whole body three-dimensional (3-D) laser scans were used to obtain anthropometric measurements. A health index (HI) was also designed based on anthropometric parameters. Subjects were defined as having metabolic syndrome when three of the following criteria were met: obesity (BMI of at least 30 kg/m(2); or a WHR of over 0.9 for males and 0.85 for females); triglyceride of at least 150 mg/dl; high-density lipoprotein (HDL)-cholesterol below 35 mg/dl for males and 39 mg/dl for females; fasting sugar levels of at least 110 mg/dl and hypertension.

RESULTS

Of 3975 subjects, 341 (8.6%) met the criteria for diabetes mellitus (DM); of these, 32.8% were diagnosed with hypertension. This proportion exceeded 18% of the subjects had normal glucose levels. Of the 3975 subjects, 658 (16.6%) met the criteria for metabolic syndrome. Proportionally, more male subjects than female subjects were diagnosed with metabolic syndrome (18.5% vs 14.7%). Of these, central obesity, elevated triglyceride and low HDL-cholesterol were the main factors in men, while fasting glucose, hypertension and central obesity were the main factors in women. This investigation found that larger proportions of subjects with impaired glucose tolerance (41.1%) and DM (64.2%) than of subjects with normal glucose subjects, suffered from metabolic syndrome (9.5%).

CONCLUSIONS

3-D body scanning is useful in correlating pertinent factors with metabolic syndrome, these factors include central obesity, hyperglycemia, dyslipidemia, hyperuricemia and hypertension.

The 3D scanner for measuring body surface area: a simplified calculation in the Chinese adult

Three-dimensional (3D) surface anthropometry enables us to extend the study to 3D geometry and morphology of mainly external human body tissues. A model is presented for estimation of human body surface area (BSA), which is identical in form to the one proposed in 1916 by DuBois and DuBois is presented. The purpose of this study is to measure BSA, using 3D scanner, and to derive a simple BSA estimation formula for the Chinese adults. In as little as 12s, the Chang Gung Whole-Body Scanner (CGWBS) allows you to capture the shape of the entire human body. The total error in BSA measurement due to scanning measurement and software computational error is less than 1%. The 3D anthropometric measures in a healthy population (n=3951) were investigated, and the results were used to derive a BSA estimation formula. The results seem to be comparable to previous data that measured BSA using traditional methods. The BSA estimation model of this study also validated using 300 new measurements along with the formulae proposed in previous researches. The result suggests that our formula better fits our adults.

Comparison of three-dimensional anthropometric body surface scanning to waist-hip ratio and body mass index in correlation with metabolic risk factors

In this study, we used a three-dimensional (3D) body scanner to evaluate subjects with central obesity. These measurements were correlated with blood pressure, blood glucose, lipid, and uric acid levels. We randomly selected 1,204 subjects who visited the Department of Health Management at Chang Gung Medical Center in Linkou. The health index (HI) was calculated as HI = (body weight x 2 x waist profile area)/[body height(2) x (breast profile area + hip profile area)]. Among the 1,204 subjects, body mass index (BMI) equal to or greater than 30 kg/m(2) was found in 8.3% of the subjects. Pearson correlation coefficient studies revealed that HI was a better index for correlating metabolic parameters than BMI and waist-to-hips girth ratio. Of the 1,204 subjects, 9.5% (114) had diabetes mellitus, according to American Diabetes Association criteria. The percentages of men and women with total cholesterol greater than or equal to 245 mg/dL were almost the same (11.0% vs. 11.1%). Elevated low-density lipoprotein (> or =130 mg/dL) was found in 36.9% (444) of subjects. In conclusion, HI as determined by 3D scanning, is another method to predict these metabolic abnormalities. Further prospective long-term follow-up study is needed to determine the relationships of 3D body scanning data and HI to blood pressure, blood glucose, lipid, and uric acid levels.