Liver Fat Assessment with Body Shape.
Liver Fat Assessment with Body Shape.
复制标题
肝脏脂肪评估与体形。
DOI：
10.1109/embc48229.2022.9871321
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Hahn,JamesK
中科院分区：
文献类型：
--
作者：
Zheng,Yijiang;Wang,Qiyue;Hahn,JamesK
肝脏脂肪变性已成为普通人群中严重的健康问题，但特别是对于那些肥胖的人。肝脏脂肪会增加肝硬化甚至肝癌的风险。目前评估肝脂肪变性的标准方法，如肝活检和CT/MR成像技术，是昂贵的和/或可能有相关的健康风险。在本文中，我们使用传统的线性回归模型和深度神经网络来评估肝脏脂肪变性。我们将我们的模型应用于医疗数据集，并评估回归和分类的方法。我们通过流行的评估指标比较几种模型的性能。实验结果表明，我们提出的神经网络优于香草线性回归模型的均方根误差22.37%，准确率18%。神经网络模型的R平方值大于0.72，准确率达到78%。因此，身体形状特征可以提供额外的准确和负担得起的选择来监测患者的肝脏脂肪的程度。临床相关性-本文提出了一种低成本和方便的方法来预测肝脏脂肪百分比使用身体形状。这种方法不会取代评估肝脏脂肪变性的金标准。然而，随着深度相机的广泛可用性（包括在智能手机上），该方法有望提供另一种可以在临床环境中广泛部署以及用于远程医疗的家庭使用的模式。
Hepatic steatosis has become a serious health concern among the general population, but especially for those who are obese. Liver fat can increase the risk of cirrhosis and even liver cancer. Current standard methods to assess hepatic steatosis, such as liver biopsy and CT/MR imaging techniques, are expensive and/or may have associated risks to health. In this paper, we use body shapes to assess hepatic steatosis using both traditional linear regression models and a deep neural network. We apply our models to a medical dataset and evaluate the approaches for both regression and classification. We compare the performance of several models via popular evaluation metrics. The experimental results indicate that our proposed neural network outperforms the vanilla linear regression model by 22.37% in RMSE and the accuracy by 18%. The R-squared value of the neural model is more than 0.72 and the accuracy reaches 78%. Hence, the body shape features can provide an additional accurate and affordable choice to monitor the degree of the patient's liver fat. Clinical relevance - This paper presents a low cost and convenient approach to predict liver fat percentage using body shapes. This approach will not replace the gold standard for assessing hepatic steatosis. However, with the wide availability for depth cameras (including on smartphones), the approach promises to provide another modality that can be deployed widely in clinical setting as well for home use for telehealth.