Intervertebral disk (IVD) degeneration is a natural progression of the aging process. Degenerative disk disease (DDD) is a pathologic condition associated with IVD that has been associated with chronic back pain. There are a variety of different mechanisms of DDD (genetic, mechanical, exposure). Each of these pathways leads to a final common result of unbalancing the anabolic and catabolic environment of the extracellular matrix in favor of catabolism. Attempts have been made to gain an understanding of the process of IVD degeneration with in Vitro studies. These models help our understanding of the disease process, but are limited as they do not come close to replicating the complexities that exist with an in Vivo model. Animal models have been developed to help us gain further understanding of the degenerative cascade of IVD degeneration In Vivo and test experimental treatment modalities to either prevent or reverse the process of DDD. Many modalities for treatment of DDD have been developed including therapeutic protein injections, stem cell injections, gene therapy, and tissue engineering. These interventions have had promising outcomes in animal models. Several of these modalities have been attempted in human trials, with early outcomes having promising results. Further, increasing our understanding of the degenerative process is essential to the development of new therapeutic interventions and the optimization of existing treatment protocols. Despite limited data, biological therapies are a promising treatment modality for DDD that could impact our future management of low back pain.

Pedicle screw instrumentation offers a significantly better major curve correction and postoperative pulmonary function values without neurologic problems compared with hybrid constructs. Both instrumentation methods offer similar junctional change, lowest instrumented vertebra, operative time, and postoperative SRS-24 outcome scores in the operative treatment of AIS.

Pedicle screw instrumentation, although more expensive, offers a significantly better major and minor curve correction without neurologic problems and improved pulmonary function values in the operative treatment of AIS and enables a slightly shorter fusion length than segmental hook instrumentation.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes. Substantial clinical work has focused on computer-assisted diagnosis (CAD), whereas technical work in volumetric image analysis has focused primarily on segmentation. 3D convolutional neural networks (3D-CNNs) have primarily been used for supervised classification on 3D modeling and light detection and ranging (LiDAR) data. Here, we demonstrate a 3D-CNN architecture that performs weakly supervised classification to screen head CT images for acute neurologic events. Features were automatically learned from a clinical radiology dataset comprising 37,236 head CTs and were annotated with a semisupervised natural-language processing (NLP) framework. We demonstrate the effectiveness of our approach to triage radiology workflow and accelerate the time to diagnosis from minutes to seconds through a randomized, double-blinded, prospective trial in a simulated clinical environment.