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PurposeThe field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.MethodsA narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.ResultsThis review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.ConclusionImprovements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

PurposeAnterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF.MethodsRetrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance.ResultsIn total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features.ConclusionsThrough an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.

PurposeIt’s a long-held belief that Modic changes (MC) occur only in adults, with advanced age, and are highly associated with pain and adverse outcomes. The following study addressed the epidemiology, risk factors and clinical relevance of MC in young paediatric patients.MethodsTwo hundred and seven consecutive patients with no history of deformities, neoplasms, trauma, or infections were included in this ambispective study. MRIs were utilized to assess MCs and types, and other degenerative disc/endplate abnormalities. Subject demographics, duration of symptoms, clinic visits, conservative management (physical therapy, NSAIDs, opioids, injections) and surgery were noted.ResultsThe mean age was 16.5 years old (46.9% males), 14% had MCs and they occurred throughout the spine. Subject baseline demographics were similar between MCs and non-MCs patients (p > 0.05). Modic type 2 (50%) was the most common type (type 1:27.1%; type 3:18.8%; mixed:4.7%). Multivariate analyses noted that endplate damage (OR: 11.36), disc degeneration (OR: 5.81), disc space narrowing (OR: 5.77), Schmorl’s nodes (OR: 4.30) and spondylolisthesis (OR: 3.55) to be significantly associated with MCs (p < 0.05). No significant differences in conservative management were noted between Modic and non-MCs patients (p > 0.05). Among surgery patients (n = 44), 21% also had MCs (p = 0.134). Symptom-duration was significantly greater in MC patients (p = 0.049).ConclusionContrary to traditional dogma, robust evidence now exists noting that MCs and their types can develop in children. Our findings give credence to the “Juvenile” variant of MCs, whereby its implications throughout the lifespan need to be assessed. Juvenile MCs have prolonged symptoms and related to specific structural spine phenotypes.

Purpose We performed a prospective one-year multi-imaging study to assess the clinical outcomes and rate of disc resorption in acute lumbar disc herniation (LDH) patients undergoing inflammation-preserving treatment (i.e. no NSAIDS, steroids). Methods All patients received gabapentin to relieve leg pain, 12 sessions of acupuncture. Repeat MRI was performed, every 3 months, after 12 sessions of treatment continued for those without 40% reduction in herniated disc sagittal area. Disc herniations sizes were measured on sagittal T2W MRI sequences, pre-treatment and at post-treatment intervals. Patients were stratified to fast, medium, slow, and prolonged recovery groups in relation to symptom resolution and disc resorption. Results Ninety patients (51% females; mean age: 48.6 years) were assessed. Mean size of disc herniation was 119.54 ± 54.34 mm 2 , and the mean VAS-Leg score was 6.12 ± 1.13 at initial presentation. A total of 19 patients (21.1%) improved at the time of the repeat MRI (i.e. within first 3 months post-treatment). 100% of all patient had LDH resorption within one year (mean: 4.4. months). There was no significant difference at baseline LDH between fast, medium, slow, and prolonged resorption groups. Initial LDH size was weakly associated with degree of leg pain at baseline and initial gabapentin levels. Surgery was avoided in all cases. Conclusion This is the first study to note inflammation-preserving treatment, without conventional anti-inflammatory and steroid medications, as safe and effective for patients with an acute LDH. Rate of disc resorption (100%) was higher than comparative recent meta-analysis findings (66.7%) and no patient underwent surgery.

BackgroundAs big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered. PurposeThe following narrative review presents the current status of AI, in particular, ML, with special regard to imaging studies, in the field of spinal research.MethodsA multi-database assessment of the literature was conducted up to September 1, 2021, that addressed AI as it related to imaging of the spine. Articles written in English were selected and critically assessed. ResultsOverall, the review discussed the limitations, data quality and applications of ML models in the context of spine imaging. In particular, we addressed the data quality and ML algorithms in spine imaging research by describing preliminary results from a widely accessible imaging algorithm that is currently available for spine specialists to reference for information on severity of spine disease and degeneration which ultimately may alter clinical decision-making. In addition, awareness of the current, under-recognized regulation surrounding the execution of ML for spine imaging was raised. ConclusionsRecommendations were provided for conducting high-quality, standardized AI applications for spine imaging.

Purpose This study aimed to explore associations between ABO blood type and postoperative adjacent segment degeneration/disease (ASD) following lumbar spine fusion, as well as evaluate differences in spinopelvic alignment, perioperative care, postoperative complications, and patient-reported outcome measures (PROMs). Methods An ambispective study was performed. Patients who underwent posterolateral or posterior lumbar interbody fusion were included. Demographic, perioperative and postoperative, clinical, and blood type information was recorded. Pre- and post-operative radiographic imaging was analyzed for alignment parameters and development of ASD. Results 445 patients were included, representing O+ (36.0%), O- (5.2%), A+ (36.2%), A- (6.3%), B+ (12.1%), B- (1.6%), and AB+ (2.7%) blood types. Most patients were female (59.1%), and had a mean age of 60.3 years and BMI of 31.1 kg/m 2 . Postoperatively, groups did not differ in duration of the hospital ( p  = 0.732) or intensive care unit ( p  = 0.830) stay, discharge disposition ( p  = 0.504), reoperation rate ( p  = 0.192), or in-hospital complication rate ( p  = 0.377). Postoperative epidural hematoma was most common amongst A + patients ( p  = 0.024). Over a mean of 11.0 months of follow-up, all patients exhibited similar improvement in PROMs, with 132 (29.7%) patients developing radiographic evidence of ASD. B + patients were significantly more likely than A + and O + patients to develop spondylolisthesis and ASD ( p  < 0.05). No significant differences in sagittal alignment parameters and number of levels of fusion were found ( p  > 0.05). Conclusions This is the first large-scale study to address and demonstrate proof-of-principle that ABO blood type, a non-modifiable risk factor, is associated with ASD following lumbar spine fusion.

Brugada syndrome (BrS) is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of SCN5A , encoding the cardiac sodium channel Na V 1.5, susceptibility genes remain largely unknown. Here we performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with BrS and 10,001 controls, and identified 21 association signals at 12 loci (10 new). Single nucleotide polymorphism (SNP)-heritability estimates indicate a strong polygenic influence. Polygenic risk score analyses based on the 21 susceptibility variants demonstrate varying cumulative contribution of common risk alleles among different patient subgroups, as well as genetic associations with cardiac electrical traits and disorders in the general population. The predominance of cardiac transcription factor loci indicates that transcriptional regulation is a key feature of BrS pathogenesis. Furthermore, functional studies conducted on MAPRE2 , encoding the microtubule plus-end binding protein EB2, point to microtubule-related trafficking effects on Na V 1.5 expression as a new underlying molecular mechanism. Taken together, these findings broaden our understanding of the genetic architecture of BrS and provide new insights into its molecular underpinnings. Genome-wide association analyses identify new susceptibility loci for Brugada syndrome. Functional studies implicate microtubule-related trafficking effects on sodium channel expression as an underlying molecular mechanism.

Musculoskeletal diseases (MSDs) are characterized as injuries and illnesses that affect the musculoskeletal system. MSDs affect every population worldwide and are associated with substantial global burden. Variations in the makeup of the gut microbiota may be related to chronic MSDs. There is growing interest in exploring potential connections between chronic MSDs and variations in the composition of gut microbiota. The human microbiota is a complex community consisting of viruses, archaea, bacteria, and eukaryotes, both inside and outside of the human body. These microorganisms play crucial roles in influencing human physiology, impacting metabolic and immunological systems in health and disease. Different body areas host specific types of microorganisms, with facultative anaerobes dominating the gastrointestinal tract (able to thrive with or without oxygen), while strict aerobes prevail in the nasal cavity, respiratory tract, and skin surfaces (requiring oxygen for development). Together with the immune system, these bacteria have coevolved throughout time, forming complex biological relationships. Changes in the microbial ecology of the gut may have a big impact on health and can help illnesses develop. These changes are frequently impacted by lifestyle choices and underlying medical disorders. The potential for safety, expenses, and efficacy of microbiota-based medicines, even with occasional delivery, has attracted interest. They are, therefore, a desirable candidate for treating MSDs that are chronic and that may have variable progression patterns. As such, the following is a narrative review to address the role of the human microbiome as it relates to MSDs.

Background : In all societies, the burden and cost of allergic and chronic respiratory diseases are increasing rapidly. Most economies are struggling to deliver modern health care effectively. There is a need to support the transformation of the health care system into integrated care with organizational health literacy.Main body : As an example for chronic disease care, MASK (Mobile Airways Sentinel NetworK), a new project of the ARIA (Allergic Rhinitis and its Impact on Asthma) initiative, and POLLAR (Impact of Air POLLution on Asthma and Rhinitis, EIT Health), in collaboration with professional and patient organizations in the field of allergy and airway diseases, are proposing real-life ICPs centred around the patient with rhinitis, and using mHealth to monitor environmental exposure. Three aspects of care pathways are being developed: (i) Patient participation, health literacy and self-care through technology-assisted “patient activation”, (ii) Implementation of care pathways by pharmacists and (iii) Next-generation guidelines assessing the recommendations of GRADE guidelines in rhinitis and asthma using real-world evidence (RWE) obtained through mobile technology. The EU and global political agendas are of great importance in supporting the digital transformation of health and care, and MASK has been recognized by DG Santé as a Good Practice in the field of digitally-enabled, integrated, person-centred care.Conclusion : In 20 years, ARIA has considerably evolved from the first multimorbidity guideline in respiratory diseases to the digital transformation of health and care with a strong political involvement.