Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA.
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
Sensors (Basel). 2021 Dec 29;22(1):212. doi: 10.3390/s22010212.
Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person's spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.
颈椎间盘植入物是治疗退行性椎间盘疾病（如颈脊髓病和神经根病）患者的常规手术治疗方法。然而，外科医生仍然主要根据诊断影像学研究的结果来确定颈椎间盘植入物的适应证，而这些影像学研究有时会导致并发症和植入物失败。为了解决这些问题，开发了一种新方法，使外科医生能够在手术前以患者特定的方式预览人工椎间盘植入物的术后效果。为此，使用人的脊柱的机器人复制品进行 3D 打印，修改为包括人工椎间盘植入物，并配备软磁传感器阵列。本研究的目的有三个：首先，评估软磁传感器阵列检测施加的负载的位置和幅度的潜力；其次，使用 3D 打印的人类脊柱复制品中的软磁传感器阵列来区分五种不同的机器人驱动姿势；最后，比较四种不同的机器学习算法的功效，以对前两个目标获得的负载、幅度和姿势进行分类。台架实验表明，软磁传感器阵列能够精确地检测力的位置和幅度，这四个不同的机器学习算法能够成功地对其进行分类，这些算法的性能得到了比较：支持向量机（SVM）、K 最近邻（KNN）、随机森林（RF）和人工神经网络（ANN）。特别是，RF 和 ANN 算法能够以 98.39%±1.50%和 98.05%±1.56%的准确率分别分类施加的负载之间 3.25 毫米的位置。此外，ANN 对 10 g 负载施加位置的分类准确率为 94.46%±2.84%。人工椎间盘植入的脊柱复制品通过机械臂进行屈伸运动。使用软磁传感器阵列，ANN 以 100%±0.0%的准确率成功地对五个不同的脊柱姿势进行了分类。所有结果表明，磁传感器阵列具有在侵入性手术之前生成数据的巨大潜力，这些数据可用于术前评估特定患者特定干预措施的适宜性，并可能有助于颈椎间盘植入患者的术后护理。
