Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.

Nontraumatic atlantoaxial rotatory subluxation in adults: Report of two cases.

Background: Nontraumatic infectious atlantoaxial rotatory subluxation (AARS) is rare and less frequently encountered in adults versus children. We utilized a stepwise approach to treat two adults with nontraumatic infectious AARS and summarized the relevant literature. Case Description: Two patients, ages 35 and 66, presented with classic clinical and imaging findings for infectious nontraumatic AARS. Here, we summarized the management for these two patients along with the literature. Conclusion: Nontraumatic infectious AARS in adults requires prompt X-ray diagnosis and timely application of traction to minimize neurological deficits. MR/CT imaging next offers critical information regarding whether operative stabilization is warranted.

Exacerbation of autoimmune uveitis by obesity occurs through the melanocortin 5 receptor.

Autoimmune uveitis is a leading cause of blindness with a complex etiology. Obesity is considered a chronic disease with a connection with autoimmune diseases through systemic inflammation. However, an obesity and autoimmune disease connection is not consistently true in rodent models of autoimmune disease. A mouse model of human autoimmune uveitis, experimental autoimmune uveitis (EAU) has been used to better understand the immunobiology of uveitis. In this study, we assessed EAU in a high-fat diet (HFD) obesity model and found that the EAU severity is significantly higher in wild-type mice, but not in HFD melanocortin 5 receptor deficient mice. We find a decrease in CD11b+ F4/80+ Ly-6Clo Ly-6G+ Mϕs, previously shown to be suppressive, and an enhancement of a Th1 response at the onset of EAU in obese mice. We further demonstrate that at recovery of EAU, obese mice lack regulatory immunity that provides protection from EAU. This report demonstrates that obesity exacerbates autoimmune uveitis and inhibits the promotion of post-EAU regulatory immunity through the melanocortin 5 receptor. The implication of this work is that obesity may contribute to the prevalence of autoimmune uveitis.