![]() ![]() For example, bioelectrical impedance analysis (BIA), calipers, and anthropometric measurements are commonly used due to time and ease of measurement at the expense of accuracy 17, 27, 28, 29. Alternative body composition technologies focus on measuring body fat. Due to these limitations, BMI is an imperfect obesity screening tool despite its widespread clinical application 23, 24, 25, 26. As studies have become more inclusive 18, it has also become apparent that body composition, specifically percent body fat (%BF), varies across race and ethnic groups even after controlling for age and BMI, which leaves placement of weight category thresholds questionable when applied to the general public 19, 20, 21, 22. As such, adiposity levels are often misclassified in those who deviate from normalized lean mass percentages, including older adults who have lost muscle with age and athletic individuals with more muscular builds 16, 17. However, BMI cannot discern the fat component of body mass from lean tissues. In clinical practice, thresholds for body weight classifications are determined using BMI, where adults with BMI ≥25 and ≥30 kg/m 2 are defined as overweight and obese, respectively 13, 14, 15. The increased risk of chronic diseases that accompany excessive fat accumulation is the leading cause of death globally and contributes to an estimated $210 billion in medical costs in the US annually 11, 12. ![]() Excess adiposity impairs functional performance, is a major risk factor for developing chronic diseases, and is often accompanied by poor self-esteem 8, 9, 10. In clinical practice, body composition assessment is often used to evaluate dietary habits 3, excess adiposity 4 and malnutrition 5, weight loss following bariatric surgery 6, and the sarcopenia that often evolves with aging 7. Trial registration: Identifier: NCT04854421.īody composition is associated with cardiorespiratory fitness and longitudinal health outcomes 1, 2. The wide availability of smartphones suggests that the VBC method for evaluating %BF could play an important role in quantifying adiposity levels in a wide range of settings. In this first validation study of a novel, accessible, and easy-to-use system, VBC body fat estimates were accurate and without significant bias compared to DXA as the reference VBC performance exceeded those of all other BIA and ADP methods evaluated. Bias in Bland-Altman analyses is defined as the discordance between the y = 0 axis and the regressed line computed from the data in the plot. Bland-Altman analysis of VBC revealed the tightest limits of agreement (LOA) and absence of significant bias relative to DXA (bias −0.42%, R 2 = 0.03 p = 0.062 LOA −5.5% to +4.7%), whereas all other evaluated methods had significant ( p < 0.01) bias and wider limits of agreement. %BF measured by VBC also had good concordance with DXA (Lin’s concordance correlation coefficient, CCC: all 0.96 women 0.93 men 0.94), whereas BMI had very poor concordance (CCC: all 0.45 women 0.40 men 0.74). Relative to DXA, VBC had the lowest mean absolute error and standard deviation (2.16 ± 1.54%) compared to all of the other evaluated methods ( p < 0.05 for all comparisons). To test our scientific hypothesis we run multiple, pair-wise Wilcoxon signed rank tests where we compare each competing measurement tool (VBC, BIA, …) with respect to the same ground-truth (DXA). %BF measured by dual-energy x-ray absorptiometry (DXA) was set as the reference against which all other %BF measurements were compared. The PBRC participants also had air displacement plethysmography (ADP) measured. Each participant had %BF measured with VBC, three consumer and two professional bioimpedance analysis (BIA) systems. 134 healthy adults ranging in age (21–76 years), sex (61.2% women), race (60.4% White 23.9% Black), and body mass index (BMI, 18.5–51.6 kg/m 2) were evaluated at two clinical sites ( N = 64 at MGH, N = 70 at PBRC). The hypothesis is that VBC yields better accuracy than other consumer-grade fat measurements devices. The VBC algorithm is based on a state-of-the-art convolutional neural network (CNN). The aim of the current study was to evaluate the performance of a novel automated computer vision method, visual body composition (VBC), that uses two-dimensional photographs captured via a conventional smartphone camera to estimate percentage total body fat (%BF). Body mass index (BMI) and other clinical or commercially available tools for quantifying body fat (BF) such as DXA, MRI, CT, and photonic scanners (3DPS) are often inaccurate, cost prohibitive, or cumbersome to use. Body composition is a key component of health in both individuals and populations, and excess adiposity is associated with an increased risk of developing chronic diseases. ![]()
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