Which method for evaluating body composition is the most accurate to date?

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. 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. 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]. The VBC algorithm is based on a state-of-the-art convolutional neural network [CNN]. The hypothesis is that VBC yields better accuracy than other consumer-grade fat measurements devices. 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/m2] were evaluated at two clinical sites [N = 64 at MGH, N = 70 at PBRC]. Each participant had %BF measured with VBC, three consumer and two professional bioimpedance analysis [BIA] systems. The PBRC participants also had air displacement plethysmography [ADP] measured. %BF measured by dual-energy x-ray absorptiometry [DXA] was set as the reference against which all other %BF measurements were compared. 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]. 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