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ABSTRACT IntroductionThe cervical spine, pivotal for mobility and overall body function, can be affected by cervical spondylosis, a major contributor to neural disorders. Prevalent in both general and military populations, especially among pilots, cervical spondylosis induces pain and limits spinal capabilities. Anterior Cervical Discectomy and Fusion (ACDF) surgery, proposed by Cloward in the 1950s, is a promising solution for restoring natural cervical curvature. The study objective was to investigate the impacts of ACDF implant design on postsurgical cervical biomechanics and neurorehabilitation outcomes by utilizing a biofield head-neck finite element (FE) platform that can facilitate scenario-specific perturbations of neck muscle activations. This study addresses the critical need to enhance computational models, specifically FE modeling, for ACDF implant design. Materials and MethodsWe utilized a validated head-neck FE model to investigate spine–implant biomechanical interactions. An S-shaped dynamic cage incorporating titanium (Ti) and polyetheretherketone (PEEK) materials was modeled at the C4/C5 level. The loading conditions were carefully designed to mimic helmet-to-helmet impact in American football, providing a realistic and challenging scenario. The analysis included intervertebral joint motion, disk pressure, and implant von Mises stress. ResultsThe PEEK implant demonstrated an increased motion in flexion and lateral bending at the contiguous spinal (C4/C5) level. In flexion, the Ti implant showed a modest 5% difference under 0% activation conditions, while PEEK exhibited a more substantial 14% difference. In bending, PEEK showed a 24% difference under 0% activation conditions, contrasting with Ti’s 17%. The inclusion of the head resulted in an average increase of 18% in neck angle and 14% in C4/C5 angle. Disk pressure was influenced by implant material, muscle activation level, and the presence of the head. Polyetheretherketone exhibited lower stress values at all intervertebral disc levels, with a significant effect at the C6/C7 levels. Muscle activation level significantly influenced disk stress at all levels, with higher activation yielding higher stress. Titanium implant consistently showed higher disk stress values than PEEK, with an orders-of-magnitude difference in von Mises stress. Excluding the head significantly affected disk and implant stress, emphasizing its importance in accurate implant performance simulation. ConclusionsThis study emphasized the use of a biofidelic head-neck model to assess ACDF implant designs. Our results indicated that including neck muscles and head structures improves biomechanical outcome measures. Furthermore, unlike Ti implants, our findings showed that PEEK implants maintain neck motion at the affected level and reduce disk stresses. Practitioners can use this information to enhance postsurgery outcomes and reduce the likelihood of secondary surgeries. Therefore, this study makes an important contribution to computational biomechanics and implant design domains by advancing computational modeling and theoretical knowledge on ACDF–spine interaction dynamics.
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Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery
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.
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- Award ID(s):
- 1950400
- PAR ID:
- 10314507
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 22
- Issue:
- 1
- ISSN:
- 1424-8220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s22010212
