3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology.

Body composition prediction from 3D optical imagery has previously been studied with linear algorithms. In this study, we present a novel application of deep 3D convolutional graph networks and nonlinear Gaussian process regression for human body shape parameterization and body composition estimation. We trained and tested linear and nonlinear models with ablation studies on a novel ensemble body shape dataset containing 4286 scans. Nonlinear GPR produced up to a 20% reduction in prediction error and up to a 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only, and a 4-14% reduction in precision error for both sexes. All coefficients of determination (R2) for all predicted variables were above 0.86 and achieved lower estimation RMSEs than all previous work on 10 metrics of body composition

Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations.

BACKGROUND: Excess adiposity in children is strongly correlated with obesity-related metabolic disease in adulthood, including diabetes, cardiovascular disease, and 13 types of cancer. Despite the many long-term health risks of childhood obesity, body mass index (BMI) Z-score is typically the only adiposity marker used in pediatric studies and clinical applications. The effects of regional adiposity are not captured in a single scalar measurement, and their effects on short- and long-term metabolic health are largely unknown. However, clinicians and researchers rarely deploy gold-standard methods for measuring compartmental fat such as magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA) on children and adolescents due to cost or radiation concerns. Three-dimensional optical (3DO) scans are relatively inexpensive to obtain and use non-invasive and radiation-free imaging techniques to capture the external surface geometry of a patients body. This 3D shape contains cues about the body composition that can be learned from a structured correlation between 3D body shape parameters and reference DXA scans obtained on a sample population. STUDY AIM: This study seeks to introduce a radiation-free, automated 3D optical imaging solution for monitoring body shape and composition in children aged 5-17. METHODS: We introduce an automated, linear learning method to predict total and regional body composition of children aged 5-17 from 3DO scans. We collected 145 male and 206 female 3DO scans on children between the ages of 5 and 17 with three scanners from independent manufacturers. We used an automated shape templating method first introduced on an adult population to fit a topologically consistent 60,000 vertex (60 k) mesh to 3DO scans of arbitrary scanning source and mesh topology. We constructed a parameterized body shape space using principal component analysis (PCA) and estimated a regression matrix between the shape parameters and their associated DXA measurements. We automatically fit scans of 30 male and 38 female participants from a held-out test set and predicted 12 body composition measurements. RESULTS: The coefficient of determination (R2) between 3DO predicted body composition and DXA measurements was at least 0.85 for all measurements with the exception of visceral fat on 3D scan predictions. Precision error was 1-4 times larger than that of DXA. No predicted variable was significantly different from DXA measurement except for male trunk lean mass. CONCLUSION: Optical imaging can quickly, safely, and inexpensively estimate regional body composition in children aged 5-17. Frequent repeat measurements can be taken to chart changes in body adiposity over time without risk of radiation overexposure

A device-agnostic shape model for automated body composition estimates from 3D optical scans.

BACKGROUND: Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods. PURPOSE: We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices. METHODS: A fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52 kg/m2 . Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model. RESULTS: We achieved coefficients of determination (R2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat), and all root-mean-squared errors were below 3.0 kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x. CONCLUSIONS: Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes

Predicting 3D body shape and body composition from conventional 2D photography

PurposeTotal and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual-energy x-ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting three-dimensional (3D) body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera.MethodsDuplicate 3D optical scans, two-dimensional (2D) optical images, and DXA whole-body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA-derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test-retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t-tests were performed to detect significant differences between the sets.ResultsResults were reported on a held-out set. Body composition prediction achieved R2 s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held-out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t-tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests.ConclusionTotal and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible

Predicting 3D body shape and body composition from conventional 2D photography

PURPOSE: Total and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual-energy X-ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting 3-dimensional body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera. METHODS: Duplicate 3D optical scans, 2D optical images, and DXA whole body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA-derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test-retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t-tests were performed to detect significant differences between the sets. RESULTS: Results were reported on a held-out set. Body composition prediction achieved R(2)s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held-out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t-tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests. CONCLUSION: Total and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible

A pose‐independent method for accurate and precise body composition from 3D optical scans

International audienceObjective: To investigate if digitally reposing three-dimensional optical (3DO) whole-body scans to a standardized pose would improve body composition accuracy and precision regardless of initial pose.Methods: Healthy adults (n=540), stratified by sex, BMI, and age, completed whole-body 3DO and dual-energy X-ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low-dimensional space by principal component analysis (stratified by sex). Total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs).Results: The analysis included 472 participants after exclusions. After reposing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared to DXA was: fat mass R2 = 0.91 male, 0.94 female; fat-free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 0.77 male, 0.79 female.Conclusions: Reposed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted

Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies

BackgroundThree-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status.ObjectivesThe objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling.MethodsHealthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA.ResultsThis analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female).Conclusions3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.This trial was registered at clinicaltrials.gov as NCT03637855

Examination of Adverse Reactions After COVID-19 Vaccination Among Patients With a History of Multisystem Inflammatory Syndrome in Children

ImportanceData are limited regarding adverse reactions after COVID-19 vaccination in patients with a history of multisystem inflammatory syndrome in children (MIS-C). The lack of vaccine safety data in this unique population may cause hesitancy and concern for many families and health care professionals.ObjectiveTo describe adverse reactions following COVID-19 vaccination in patients with a history of MIS-C.Design, Setting, and ParticipantsIn this multicenter cross-sectional study including 22 North American centers participating in a National Heart, Lung, and Blood Institute, National Institutes of Health–sponsored study, Long-Term Outcomes After the Multisystem Inflammatory Syndrome in Children (MUSIC), patients with a prior diagnosis of MIS-C who were eligible for COVID-19 vaccination (age ≥5 years; ≥90 days after MIS-C diagnosis) were surveyed between December 13, 2021, and February 18, 2022, regarding COVID-19 vaccination status and adverse reactions.ExposuresCOVID-19 vaccination after MIS-C diagnosis.Main Outcomes and MeasuresThe main outcome was adverse reactions following COVID-19 vaccination. Comparisons were made using the Wilcoxon rank sum test for continuous variables and the χ2 or Fisher exact test for categorical variables.ResultsOf 385 vaccine-eligible patients who were surveyed, 185 (48.1%) received at least 1 vaccine dose; 136 of the vaccinated patients (73.5%) were male, and the median age was 12.2 years (IQR, 9.5-14.7 years). Among vaccinated patients, 1 (0.5%) identified as American Indian/Alaska Native, non-Hispanic; 9 (4.9%) as Asian, non-Hispanic; 45 (24.3%) as Black, non-Hispanic; 59 (31.9%) as Hispanic or Latino; 53 (28.6%) as White, non-Hispanic; 2 (1.1%) as multiracial, non-Hispanic; and 2 (1.1%) as other, non-Hispanic; 14 (7.6%) had unknown or undeclared race and ethnicity. The median time from MIS-C diagnosis to first vaccine dose was 9.0 months (IQR, 5.1-11.9 months); 31 patients (16.8%) received 1 dose, 142 (76.8%) received 2 doses, and 12 (6.5%) received 3 doses. Almost all patients received the BNT162b2 vaccine (347 of 351 vaccine doses [98.9%]). Minor adverse reactions were observed in 90 patients (48.6%) and were most often arm soreness (62 patients [33.5%]) and/or fatigue (32 [17.3%]). In 32 patients (17.3%), adverse reactions were treated with medications, most commonly acetaminophen (21 patients [11.4%]) or ibuprofen (11 [5.9%]). Four patients (2.2%) sought medical evaluation, but none required testing or hospitalization. There were no patients with any serious adverse events, including myocarditis or recurrence of MIS-C.Conclusions and RelevanceIn this cross-sectional study of patients with a history of MIS-C, no serious adverse events were reported after COVID-19 vaccination. These findings suggest that the safety profile of COVID-19 vaccination administered at least 90 days following MIS-C diagnosis appears to be similar to that in the general population.</jats:sec