A method for measuring human body composition using digital images

Longitudinal concordance of body composition and anthropometric assessment by a novel smartphone application across a 12-week self-managed weight loss intervention

Smartphone applications (SPA) now offer the ability to provide accessible in-home monitoring of relevant individual health biomarkers. Previous cross-sectional validations of similar technologies have reported acceptable accuracy with high-grade body composition assessments; this research assessed longitudinal agreement of a novel SPA across a self-managed weight loss intervention of thirty-eight participants (twenty-one males, seventeen females). Estimations of body mass (BM), body fat percentage (BF%), fat-free mass (FFM) and waist circumference (WC) from the SPA were compared with ground truth (GT) measures from a dual-energy X-ray absorptiometry scanner and expert technician measurement. Small mean differences (MD) and standard error of estimate (SEE) were observed between method deltas (ΔBM: MD = 0·12 kg, SEE = 2·82 kg; ΔBF%: MD = 0·06 %, SEE = 1·65 %; ΔFFM: MD = 0·17 kg, SEE = 1·65 kg; ΔWC: MD = 1·16 cm, SEE = 2·52 cm). Concordance correlation coefficient (CCC) assessed longitudinal agreement between the SPA and GT methods, with moderate concordance (CCC: 0·55-0·73) observed for all measures. The novel SPA may not be interchangeable with high-accuracy medical scanning methods yet offers significant benefits in cost, accessibility and user comfort, in conjunction with the ability to monitor body shape and composition estimates over time.

Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk

Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task. Here, we report a deep learning model trained on an individual’s body shape outline—or “silhouette” —that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes (R²: 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (ΔR² = 0.05–0.13). Next, we study VAT/ASAT ratio, a nearly body-mass index (BMI)—and waist circumference-independent marker of metabolically unhealthy fat distribution. While the comparator model poorly predicts VAT/ASAT ratio (R²: 0.17–0.26), a silhouette-based model enables significant improvement (R²: 0.50–0.55). Increased silhouette-predicted VAT/ASAT ratio is associated with increased risk of prevalent and incident type 2 diabetes and coronary artery disease independent of BMI and waist circumference. These results demonstrate that body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for scalable population-based assessment.

Accuracy of image analysis for linear zoometric measurements in dromedary camels

The present study was designed to verify the effectiveness of the image analysis method for body measurement in dromedary camel compared to manual measurements as a reference method. To achieve this aim, twenty-one linear body measurements were estimated on 59 adult Sahraoui dromedary camels (22 males and 37 females) with a normal clinical condition by using a measuring stick or vernier caliper (standard method). On the other hand, image analysis on profile, front, or behind photographs was processed using Axiovision Software. Overall mean comparison, relative error, variance, Pearson's correlation coefficient, and coefficient of variance showed that the image analysis method was accurate in relation to the manual measurement. Furthermore, image analysis results indicated relevant accuracy (bias correction factor, C_b ≈1) and precision (Pearson ρ ≈1) which were significantly correlated with the results of the reference method (Lin's concordance correlation coefficients r_ccc ≈ 1). According to Bland-Altman upper and lower limits of agreement, the concordance was estimated between 93.22 and 98.3%. Passing-Bablok regression showed a good relationship between the results of the two methods displaying no significant systematic and proportional bias. The image analysis method for linear body measurements in dromedary camel showed results that are in agreement with the manual measuring method. Therefore, the image analysis could be considered a valid tool for camel conformation trait studies.

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 three-dimensional (3D) body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera.

METHODS

Duplicate 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.

RESULTS

Results 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.

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.

Advancing Nutrition in the International Food Assistance Agenda: Progress and Future Directions Identified at the 2018 Food Assistance for Nutrition Evidence Summit

BACKGROUND

Global food insecurity persists despite continued international attention, necessitating evidence-based food assistance interventions that adequately address nutritional concerns. In June 2018, the US Agency for International Development's Office of Food for Peace through the Food Aid Quality Review (FAQR) project sponsored a "Food Assistance for Nutrition Evidence Summit" to share evidence relevant to policy and programmatic decision-making and to identify critical evidence gaps.

OBJECTIVE

This article presents 4 priority areas to advance nutrition in the international food assistance agenda generated through presentations and discussions with the food assistance community at the Evidence Summit.

METHODS

Priority areas were identified after the Evidence Summit using a combination of FAQR team discussions, review of presentations and official notes, and supporting literature.

RESULTS

Key priority areas to advance nutrition in the international food assistance agenda are as follows: (1) increase research funding for food assistance in all contexts, paying particular attention to emergency settings; (2) research and adopt innovative ingredients, technology, and delivery strategies in food assistance products and programs that encourage long-term well-being; (3) redefine and expand indicators of nutritional status to capture contextual information about the outcomes of food assistance interventions; and (4) augment communication and collaboration across the food assistance ecosystem.

CONCLUSIONS

These priorities are critical in a time of increased humanitarian need and will be key to fostering long-term resilience among vulnerable groups.

Illustration of Measurement Error Models for Reducing Bias in Nutrition and Obesity Research Using 2-D Body Composition Data

OBJECTIVE

This study aimed to illustrate the use and value of measurement error models for reducing bias when evaluating associations between body fat and having type 2 diabetes (T2D) or being physically active.

METHODS

Logistic regression models were used to evaluate T2D and physical activity among adults aged 19 to 80 years from the Photobody Study (n = 558). Self-reported T2D and physical activity were categorized as "yes" or "no." Body fat measured by two-dimensional photographs was adjusted for bias using dual-energy x-ray absorptiometry scans as a reference. Three approaches were applied: regression calibration (RC), simulation extrapolation (SIMEX), and multiple imputation (MI).

RESULTS

Unadjusted two-dimensional measures of body fat had upward biases of 30% and 233% for physical activity and T2D, respectively. For the physical activity model, RC-adjusted values had a 13% upward bias, whereas MI and SIMEX decreased the bias to 9% and 91%, respectively. For the T2D model, MI reduced the bias to 0%, whereas RC and SIMEX increased the upward bias to > 300%.

CONCLUSIONS

Of three statistical approaches to reducing bias due to measurement errors, MI performed best in comparison to RC and SIMEX. Measurement error methods can improve the reliability of analyses that look for relations between body fat measures and health outcomes.