Waist-to-hip circumference ratio (WHR) is now recognized as among the strongest shape biometrics linked with health outcomes, although use of this phenotypic marker remains limited due to the inaccuracies in and inconvenient nature of flexible tape measurements when made in clinical and home settings. Here we report that accurate and reliable WHR estimation in adults is possible with a smartphone application based on novel computer vision algorithms. The developed application runs a convolutional neural network model referred to as MeasureNet that predicts a person’s body circumferences and WHR using front, side, and back color images. MeasureNet bridges the gap between measurements conducted by trained professionals in clinical environments, which can be inconvenient, and self-measurements performed by users at home, which can be unreliable. MeasureNet’s accuracy and reliability is evaluated using 1200 participants, measured by a trained staff member. The developed smartphone application, which is a part of Amazon Halo, is a major advance in digital anthropometry, filling a long-existing gap in convenient, accurate WHR measurement capabilities.

腰臀围比 （WHR） 现在被认为是与健康结果相关的最强的形状生物识别技术之一，尽管由于在临床和家庭环境中进行柔性卷尺测量的不准确和不便性，这种表型标记的使用仍然受到限制。在这里，我们报告了使用基于新型计算机视觉算法的智能手机应用程序可以准确可靠地估计成人的 WHR。开发的应用程序运行一个称为 MeasureNet 的卷积神经网络模型，该模型使用正面、侧面和背面的彩色图像预测一个人的体围和 WHR。MeasureNet 弥合了由训练有素的专业人员在临床环境中进行的测量（可能不方便）与用户在家中进行的自我测量（可能不可靠）之间的差距。MeasureNet 的准确性和可靠性由 1200 名参与者进行评估，并由训练有素的工作人员进行测量。开发的智能手机应用程序是 Amazon Halo 的一部分，是数字人体测量学的重大进步，填补了方便、准确的 WHR 测量能力长期存在的空白。