Šušteršič Tijana, Milovanović Vladimir, Ranković Vesna, Filipović Nenad
Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000, Kragujevac, Serbia; Bioengineering Research and Development Center (BioIRC), Prvoslava Stojanovića 6, 34000, Kragujevac, Serbia.
Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000, Kragujevac, Serbia.
Comput Biol Med. 2020 Oct;125:103978. doi: 10.1016/j.compbiomed.2020.103978. Epub 2020 Aug 19.
The aim of this research was to investigate the best methodology for disc hernia diagnosis using foot force measurements from the designed platform. Based on the subjective neurological examination that examines muscle weakness on the nerve endings of the skin area on feet and concludes about origins of nerve roots between spine discs, a platform for objective recordings of the aforementioned muscle weakness has been designed. The dataset included 33 patients with pre-diagnosed L4/L5 and L5/S1 disc hernia on the left or the right side, confirmed with the MRI scanning and neurological exam. We have implemented 5 different classifiers that were found to be the most suitable for smaller dataset and investigated the accuracy of classification depending on the normalization method, linearity/non-linearity of the algorithm, and dataset splitting variation (32-1, 31-2, 30-3, 29-4 patients for training and testing, respectively). The classifier is able to distinguish between four different diagnoses L4/L5 on the left side, L4/L5 on the right side, L5/S1 on the left side and L5/S1 on the right side, as well as to recognize healthy subjects (without disc herniation). The results show that non-linear algorithms achieved better accuracy in comparison to tested linear classifiers, suggesting the expected non-linear connection between the foot force values and the level of disc herniation. Two algorithms with highest accuracy turned out to be Decision Tree and Naïve Bayes, depending on the normalization method. The system is also able to record and recognize improvements in muscle weakness after surgical operation and physical therapy.
本研究的目的是通过使用来自设计平台的足部力量测量来探究椎间盘突出症诊断的最佳方法。基于主观神经学检查，该检查通过检测足部皮肤区域神经末梢的肌肉无力情况，并推断脊柱椎间盘之间神经根的起源，设计了一个用于客观记录上述肌肉无力情况的平台。数据集包括33例经磁共振成像扫描和神经学检查确诊为左侧或右侧L4/L5和L5/S1椎间盘突出症的患者。我们实施了5种被发现最适合较小数据集的不同分类器，并根据归一化方法、算法的线性/非线性以及数据集划分变化（分别为32 - 1、31 - 2、30 - 3、29 - 4例患者用于训练和测试）来研究分类的准确性。该分类器能够区分左侧L4/L5、右侧L4/L5、左侧L5/S1和右侧L5/S1这四种不同诊断，以及识别健康受试者（无椎间盘突出症）。结果表明，与测试的线性分类器相比，非线性算法实现了更高的准确性，这表明足部力量值与椎间盘突出程度之间存在预期的非线性联系。根据归一化方法，准确率最高的两种算法是决策树和朴素贝叶斯。该系统还能够记录和识别手术和物理治疗后肌肉无力情况的改善。
