Load distribution on intervertebral cages with and without posterior instrumentation.

BACKGROUND CONTEXT: Posterior and transforaminal lumbar interbody fusion (PLIF, TLIF) are well-established procedures for spinal fusion. However, little is known about load sharing between cage, dorsal construct, and biological tissue within the instrumented lumbar spine. PURPOSE: The aim of this study was to quantify the forces acting on cages under axial compression force with and without posterior instrumentation. STUDY DESIGN: Biomechanical cadaveric study. METHODS: Ten lumbar spinal segments were tested under uniaxial compression using load cell instrumented intervertebral cages. The force was increased in 100N increments to 1000N or a force greater than 500N on one load cell. Each specimen was tested after unilateral PLIF (uPLIF), bilateral PLIF (bPLIF) and TLIF each with/without posterior instrumentation. Dorsal instrumentation was performed with 55N of compression per side. RESULTS: Cage insertion resulted in median cage preloads of 16N, 29N and 35N for uPLIF, bPLIF, and TLIF. The addition of compressed dorsal instrumentation increased the median preload to 224N, 328N, and 317N, respectively. With posterior instrumentation, the percentage of the external load acting on the intervertebral cage was less than 25% at 100N (uPLIF: 14.2%; bPLIF: 16%; TLIF: 11%), less than 45% at 500N (uPLIF: 31.8%; bPLIF: 41.1%; TLIF: 37.9%) and less than 50% at 1000N (uPLIF: 40.3%; bPLIF: 49.7%; TLIF: 43.4%). Without posterior instrumentation, the percentage of external load on the cages was significantly higher with values above 50% at 100N (uPLIF: 55.6%; bPLIF: 75.5%; TLIF: 66.8%), 500N (uPLIF: 71.7%; bPLIF: 79.2%; TLIF: 65.4%), and 1000N external load (uPLIF: 73%; bPLIF: 80.5%; TLIF: 66.1%). For absolute loads, preloads and external loads must be added together. CONCLUSIONS: Without posterior instrumentation, the intervertebral cages absorb more than 50% of the axial load and the load distribution is largely independent of the loading amplitude. With posterior instrumentation, the external load acting on the cages is significantly lower and the load distribution becomes load amplitude dependent, with a higher proportion of the load transferred by the cages at high loads. The bPLIF cages tend to absorb more force than the other two cage configurations. CLINICAL SIGNIFICANCE: Cage instrumentation allows some of the compression force to be transmitted through the cage to the screws below, better distributing and reducing the overall force on the pedicle screws at the end of the construct and on the rods.

Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study.

In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935-0.963) for the internal validation and 0.912 (95% CI 0.866-0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures.

Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices-A Feasibility Study.

Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas (VS) as a computationally inexpensive alternative that does not require the creation of segmentations. Methods: A total of 2992 T1-weighted contrast-enhanced axial slices containing VS from the MRIs of 633 patients were labeled according to tumor location, of which 2538 slices from 539 patients were used for training a CNN (ResNet-34) to classify them according to the side of the tumor as a surrogate for detection and 454 slices from 94 patients were used for internal validation. The model was then externally validated on contrast-enhanced and non-contrast-enhanced slices from a different institution. Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results: The model achieved an accuracy of 0.928 (95% CI: 0.869-0.987) on contrast-enhanced slices and 0.795 (95% CI: 0.702-0.888) on non-contrast-enhanced slices from the external validation cohorts. The implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the focus of the model was not limited to the contrast-enhancing tumor but to a larger area of the cerebellum and the cerebellopontine angle. Conclusions: Single-slice predictions might constitute a computationally inexpensive alternative to training 2.5/3D-CNNs for certain detection tasks in medical imaging even without the use of segmentations. Head-to-head comparisons between 2D and more sophisticated architectures could help to determine the difference in accuracy, especially for more difficult tasks.