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 < 0.05 for all comparisons). %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). Bland-Altman analysis of VBC revealed the tightest limits of agreement (LOA) and absence of significant bias relative to DXA (bias −0.42%, R² = 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. 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. 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. 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.

Trial registration: ClinicalTrials.gov Identifier: NCT04854421.

身体成分是个人和人群健康的关键组成部分，过度肥胖与患慢性病的风险增加有关。体重指数 (BMI) 和其他用于量化体脂 (BF) 的临床或商用工具，例如 DXA、MRI、CT 和光子扫描仪 (3DPS)，通常不准确、成本过高或使用繁琐。当前研究的目的是评估一种新型自动化计算机视觉方法视觉身体成分 (VBC) 的性能，该方法使用通过传统智能手机相机拍摄的二维照片来估计全身脂肪百分比 (%BF)。VBC 算法基于最先进的卷积神经网络 (CNN)。假设是 VBC 比其他消费级脂肪测量设备产生更好的准确性。²）在两个临床地点进行了评估（ MGH 的N  = 64， PBRC 的N  = 70）。每个参与者都使用 VBC、三个消费者和两个专业生物阻抗分析 (BIA) 系统测量了 %BF。PBRC 参与者还测量了空气置换体积描记法 (ADP)。通过双能 X 射线吸收法 (DXA) 测量的 %BF 被设置为所有其他 %BF 测量值的参考值。为了检验我们的科学假设，我们运行了多个成对的 Wilcoxon 符号秩检验，在其中我们比较了每个竞争测量工具（VBC、BIA、...）与相同的真实值 (DXA)。相对于 DXA，与所有其他评估方法相比，VBC 具有最低的平均绝对误差和标准偏差 (2.16 ± 1.54%) ( p < 0.05 对于所有比较）。VBC 测量的 %BF 也与 DXA 具有良好的一致性（Lin 的一致性相关系数，CCC：全部 0.96；女性 0.93；男性 0.94），而 BMI 的一致性非常差（CCC：全部 0.45；女性 0.40；男性 0.74）。VBC 的 Bland-Altman 分析揭示了最严格的一致性限制 (LOA) 并且相对于 DXA 没有显着偏差（偏差 -0.42%，R ²  = 0.03；p  = 0.062；LOA -5.5% 至 +4.7%），而所有其他评估方法具有显着性 ( p < 0.01) 偏差和更广泛的一致性限制。Bland-Altman 分析中的偏差定义为 y = 0 轴与根据图中数据计算的回归线之间的不一致。在对新颖、可访问且易于使用的系统的首次验证研究中，与作为参考的 DXA 相比，VBC 体脂估计准确且没有显着偏差；VBC 性能超过了所有其他评估的 BIA 和 ADP 方法。智能手机的广泛应用表明，用于评估 %BF 的 VBC 方法可以在量化各种环境中的肥胖水平方面发挥重要作用。

试验注册：ClinicalTrials.gov 标识符：NCT04854421。