Cunha Pedro, Moura Daniel C, Guevara López Miguel Angel, Guerra Conceição, Pinto Daniela, Ramos Isabel
Laboratory of Experimental Mechanics and Optics (LOME) Institute of Mechanical Engineering and Industrial Management (INEGI) Campus da FEUP, Rua Dr. Roberto Frias, 400 4200-465, Porto, Portugal,
J Med Syst. 2014 Sep;38(9):87. doi: 10.1007/s10916-014-0087-0. Epub 2014 Jul 11.
The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children's bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose. This paper presents a study where the use of ensemble techniques for improving bone age assessment is evaluated. A new computer method was developed that extracts descriptors for each joint of each finger, which are then combined using different ensemble schemes for obtaining a final bone age value. Three popular ensemble schemes are explored in this study: bagging, stacking and voting. Best results were achieved by bagging with a rule-based regression (M5P), scoring a mean absolute error of 10.16 months. Results show that ensemble techniques improve the prediction performance of most of the evaluated regression algorithms, always achieving best or comparable to best results. Therefore, the success of the ensemble methods allow us to conclude that their use may improve computer-based bone age assessment, offering a scalable option for utilizing multiple regions of interest and combining their output.
骨龄评估，即骨骼成熟度评估，是儿科学中的一项重要任务，用于测量儿童骨骼的成熟程度。目前，尚无评估骨龄的标准临床程序，应用最为广泛的方法是格鲁利希和派尔法以及坦纳和怀特豪斯法。已有人提出采用计算机方法实现该过程的自动化；然而，对于如何结合手部不同部位的特征以及如何利用集成技术来实现这一目的，尚缺乏探索。本文介绍了一项关于评估使用集成技术改进骨龄评估的研究。开发了一种新的计算机方法，该方法为每根手指的每个关节提取描述符，然后使用不同的集成方案进行组合，以获得最终的骨龄值。本研究探索了三种常用的集成方案：装袋法、堆叠法和投票法。采用基于规则的回归算法（M5P）的装袋法取得了最佳结果，平均绝对误差为10.16个月。结果表明，集成技术提高了大多数评估回归算法的预测性能，并始终能取得最佳或与最佳结果相当的成绩。因此，集成方法的成功使我们得出结论，其应用可能会改进基于计算机的骨龄评估，为利用多个感兴趣区域并组合其输出提供了一种可扩展的选择。
