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Neural Network-based Visual Body Weight Estimation for Drug Dosage Finding

机译:基于神经网络的视觉体重估计,用于寻找药物剂量

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Body weight adapted drug dosages are important for emergency treatments: Inaccuracies in body weight, estimation may lead to inaccurate drug dosing. This paper describes an improved approach to estimating the body weight of emergency patients in a trauma room, based on images from an RGB-D and a thermal camera. The improvements are specific to several aspects: Fusion of RGB-D and thermal camera eases filtering and segmentation of the patient's body from the background. Robustness and accuracy is gained by an artificial neural network, which considers geometric features from the sensors as input, e.g. the patient's volume, and shape parameters. Preliminary experiments with 69 patients show an accuracy close to 90 percent, with less than 10 percent relative error and the results are compared with the physician's estimate, the patient's statement and an established anthropometric method.
机译:适应体重的药物剂量对于紧急治疗很重要:体重不准确,估计可能导致药物剂量不正确。本文基于RGB-D和热像仪的图像,描述了一种改进的方法来估计创伤室急诊患者的体重。这些改进特定于以下几个方面:RGB-D和热像仪的融合简化了从背景对患者身体的过滤和分割。通过人工神经网络获得鲁棒性和准确性,该人工神经网络将来自传感器的几何特征视为输入,例如患者的体型和形状参数。对69位患者进行的初步实验显示,其准确率接近90%,相对误差小于10%,并且将结果与医生的估计,患者的陈述和已建立的人体测量学方法进行了比较。

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