Is it sufficient to treat adult lumbar spinal deformity using anterior lumbar interbody fusion with percutaneous pedicle screw fixation?

Anterior lumbar interbody fusion (ALIF) has been performed for lumbar spinal restoration and stabilization without extensive paraspinal muscle damage or massive bleeding. The authors retrospectively investigated surgical results of multilevel ALIF followed by percutaneous pedicle screw fixation (PPSF) in adult lumbar spinal deformity (ALSD). This study included 28 patients with degenerative lumbar spinal deformity, who underwent selective multilevel ALIF and PPSF between January 2013 and August 2016 at our hospital. Standing X-rays were performed and coronal Cobb angle (CCA) of scoliosis, sagittal vertical axis (SVA), lumbar lordosis (LL), thoracic kyphosis (TK), pelvic tilt (PT), and sacral slope (SS) were measured. Pain and functional assessment were performed using visual analogue scale (VAS) scores for low back pain and leg pain, and Oswestry Disability Index (ODI) scores. CCA, SVA and LL were significantly improved immediately after surgery and relatively well maintained until the last follow-up. After surgery, PT was significantly decreased and SS was increased, respectively. However, cases with SVA > 95 mm or PT > 30° showed a loss of correction in sagittal balance parameters to a greater extent at the last follow-up compared to the group of patients with minor sagittal imbalance. VAS scores for back and radicular pain, and ODI score were significantly decreased at the final follow-up (p < 0.05). Multilevel ALIF with PPSF yielded favorable clinical and radiological outcomes in coronal and sagittal balance without severe surgical mortality or morbidity in patients with ALSD. However, correction loss in sagittal balance was observed in cases with SVA > 95 mm or PT > 30˚.     

C1 lateral mass screw insertion from the caudal–dorsal to the cranial–ventral direction as an alternate method for C1 fixation: A quantitative anatomical and morphometric evaluation

ObjectC1 lateral mass screw has been widely used for fixation of the upper cervical spine. However, traditional fixation methods are not without complication. Morphometric measurement of an alternative approach is conducted.MethodsThree-dimensional CT scans of the cervical spine obtained in 100 adults were evaluated, and key measurements were determined for screw entry points, trajectories, and screw lengths for placement of a C1 screw via this alternate approach. Additional measures were included to account for relevant anatomic variation, including the size of the dangerous lateral zone of the C1 entry point and depth of the atlantooccipital joint surface. Twenty dried atlantal specimens were evaluated to determine corresponding ex vivo measurements.ResultsThe mean maximum angle of medialization was 20.8° ± 2.8° (right) and 21.1° ± 2.8° (left), as measured in the axial CT images. Sagittal CT images show the mean maximum superior angulation was 24.7° ± 4.3° (right) and 24° ± 4.0° (left), and the mean minimum superior angulation was 13.6° ± 4.4° (right) and 13.6° ± 3.9° (left). The mean screw length within the lateral mass was 21.2 ± 1.9 mm (right) and 21.3 ± 2.0 mm (left). Given an additional 10–15 mm needed for rod adaptation, an ideal screw length of 30–35 mm was determined.ConclusionThe C1 insertion caudally from the C2 nerve root may become an alternate method. Preoperative consideration of the ideal screw insertion point, trajectory, and length are vital for safe and effective surgical intervention.     

Partially pre-calculated weights for the backpropagation learning regime and high accuracy function mapping using continuous input RAM-based sigma–pi nets

In this article we present a methodology that partially pre-calculates the weight updates of the backpropagation learning regime and obtains high accuracy function mapping. The paper shows how to implement neural units in a digital formulation which enables the weights to be quantised to 8-bits and the activations to 9-bits. A novel methodology is introduced to enable the accuracy of sigma–pi units to be increased by expanding their internal state space. We, also, introduce a novel means of implementing bit-streams in ring memories instead of utilising shift registers. The investigation utilises digital “Higher Order” sigma–pi nodes and studies continuous input RAM-based sigma–pi units. The units are trained with the backpropagation learning regime to learn functions to a high accuracy. The neural model is the sigma–pi units which can be implemented in digital microelectronic technology.The ability to perform tasks that require the input of real-valued information, is one of the central requirements of any cognitive system that utilises artificial neural network methodologies. In this article we present recent research which investigates a technique that can be used for mapping accurate real-valued functions to RAM-nets. One of our goals was to achieve accuracies of better than 1% for target output functions in the range Y∈[0,1], this is equivalent to an average Mean Square Error (MSE) over all training vectors of 0.0001 or an error modulus of 0.01. We present a development of the sigma–pi node which enables the provision of high accuracy outputs. The sigma–pi neural model was initially developed by Gurney (Learning in nets of structured hypercubes. PhD Thesis, Department of Electrical Engineering, Brunel University, Middlessex, UK, 1989; available as Technical Memo CN/R/144). Gurney's neuron models, the Time Integration Node (TIN), utilises an activation that was derived from a bit-stream. In this article we present a new methodology for storing sigma–pi node's activations as single values which are averages.In the course of the article we state what we define as a real number; how we represent real numbers and input of continuous values in our neural system. We show how to utilise the bounded quantised site-values (weights) of sigma–pi nodes to make training of these neurocomputing systems simple, using pre-calculated look-up tables to train the nets. In order to meet our accuracy goal, we introduce a means of increasing the bandwidth capability of sigma–pi units by expanding their internal state-space. In our implementation we utilise bit-streams when we calculate the real-valued outputs of the net. To simplify the hardware implementation of bit-streams we present a method of mapping them to RAM-based hardware using ‘ring memories’. Finally, we study the sigma–pi units’ ability to generalise once they are trained to map real-valued, high accuracy, continuous functions. We use sigma–pi units as they have been shown to have shorter training times than their analogue counterparts and can also overcome some of the drawbacks of semi-linear units (Gurney, 1992. Neural Networks, 5, 289–303).