Agreement of anthropometric and body composition measures predicted from 2D smartphone images and body impedance scales with criterion methods

Digital Single-Image Smartphone Assessment of Total Body Fat and Abdominal Fat Using Machine Learning

“…Similarly, FM values were highly correlated (R 2 = 0.93) with LOA from 2.96 to −2.76 kg. Nana et al [ 25 ] captured 2D smartphone digital images of Asian adults in two poses: a frontal position with arms extended from the body, feet together and legs separated at shoulder width, and a right lateral position with feet together and arms extended along the torso and thigh in front of a green photographic screen. Twenty-four replicate images were obtained for each pose (three sets of eight consecutive digital photographs).…”

“…Regardless of approach, 2D DP methods have relatively good reproducibility and similarities in group comparisons of estimated and predicted body fat variables. Although the investigators reported no significance between mean predicted and DXA-determined %fat values, proportional bias (e.g., tendency for greater errors from 2D DP with increasing %fat values) emerged and limits general use of these models [ 25 , 26 ]. In contrast, the results of the present study found no proportional bias between 2D DP-predicted and DXA-measured FM.…”

“…Use of proprietary prediction equations that fail to designate the physical characteristics and body shape variables used as independent variables confound the interpretation of findings of 2D DP measurement precision (reproducibility), trends in bias, and wide LOA [ 24 , 25 , 26 ]. A potential limitation is the failure to reveal the inclusion of body weight, a significant predictor of %fat and FM, hence affecting the independence of digitally acquired and derived body surface components in estimation of body shape and composition [ 24 , 25 , 26 ]. The present study, which is an evolution of the first use of 2D DP with a smartphone camera [ 19 ], uses machine learning to determine silhouette characteristics to derive a precise and accurate model to predict FM independently of body weight.…”

“…Initial, and some contemporary, digital imaging methods use three-dimensional (3D) techniques with general access constrained by size and cumbersome equipment, immobility, and cost [ 21 , 22 , 23 ]. The current widespread public availability of smartphone cameras enables acquisition of two-dimensional (2D) digital image photographs that overcome the practical limitations of 3D digital imaging systems [ 19 , 24 , 25 , 26 ]. Contemporary use of digital imaging methods focuses solely on estimation of total body adiposity but offers an opportunity to assess localized fat in body regions such as the abdomen.…”

“…Computational machine learning is a mathematical tool increasingly used in medical research to develop algorithms designed to make accurate predictions from large data sets [ 27 ]. The application of machine learning to digital imaging analysis to estimate body fat is limited, with reports of poor reproducibility of body composition estimates, weak concordance with reference values, and proportional bias [ 24 , 25 , 26 ].…”