Department of Electrical and Electronic Engineering, Hanyang University, Ansan, 15588, South Korea.
Department of Orthopedic Surgery, Hanyang University College of Medicine, Seoul, 04763, South Korea.
Sci Rep. 2024 Oct 3;14(1):23022. doi: 10.1038/s41598-024-72891-1.
This paper proposes a new method for predicting the stage of osteoporosis by estimating bone-density parameters using an ultrasonic-bone densitometer. Using the developed ultrasonic bone densitometer, the ultrasonic signal passing through the phalanx of the finger was measured, and the measured signal was preprocessed to generate input data for parameter estimation. The Nakagami probability-density function was used for modeling this data, and optimal bone-density parameters were estimated using an optimization problem - to predict the stage of osteoporosis. The proposed method was verified through in vitro and in vivo experiments. In phantom experiments, cubes with different materials (ABS plastic, PLA plastic, Nylon) were used, and each cube had a different density through internal density so that it could mimic bones. The experiments were conducted using them and the results were compared. The experimental results confirmed that there was a correlation between the density and estimated parameters for all three materials. In the in vivo experiment, the osteoporosis stages were classified as normal, osteopenia, and osteoporosis, and the bone-density parameters were estimated for the participants of each group. Thus, a basis for predicting the stage of osteoporosis was created, and data from new participants were collected and verified as test data. As a result, normal and abnormal groups (osteopenia and osteoporosis) were predicted.
本文提出了一种新的方法，通过使用超声骨密度计来估计骨密度参数来预测骨质疏松症的阶段。使用开发的超声骨密度计，测量了通过手指指骨的超声信号，并对测量的信号进行预处理，以生成用于参数估计的输入数据。使用 Nakagami 概率密度函数对该数据进行建模，并通过优化问题来估计最佳骨密度参数 - 以预测骨质疏松症的阶段。通过体外和体内实验验证了该方法。在体模实验中，使用了具有不同材料（ABS 塑料、PLA 塑料、尼龙）的立方体，并且每个立方体通过内部密度具有不同的密度，从而可以模拟骨骼。使用它们进行了实验，并比较了结果。实验结果证实，对于所有三种材料，密度与估计参数之间都存在相关性。在体内实验中，将骨质疏松症阶段分为正常、骨量减少和骨质疏松症，并为每个组别的参与者估计了骨密度参数。因此，为预测骨质疏松症的阶段创造了依据，并收集了新参与者的数据并将其作为测试数据进行验证。结果，预测了正常和异常组（骨量减少和骨质疏松症）。
