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\"Richard Feynman is one of, if not the, most famous physicists of the latter half of the 20th century. In 1964, at Cornell University, he delivered the famous Messenger Lectures. This book, The Character of Physical Law, sprung from these lectures. In this classic work, Feynman explores the relationship between math and physics, describes the great conservation principles, the puzzle of symmetry in physical law, how to reconcile physical problems that yield infinite results with their manifestations in the natural world, and quantum mechanical views of nature. Feynman's accessible speech and conversational style comes through well in each essay; his simple pencil and paper drawings communicate complex ideas as if one were viewing them on a chalk board. This reissue features a foreword by Nobel Laureate Frank Wilczek, which situates the work within modern scholarship and describes why the book is still relevant today. Although he mentions areas where Feynman's theories need \"updating,\" he points out that Feynman's unorthodox and brilliant way of thinking helped develop the general quantum electrodynamics theory, one of the most precise and accurate theories in physical science. Wilczek concludes with the assertion that this book represents Feynman at \"the height of his powers, and that this \"is the single best introduction to modern physics, altogether.\"\"-- Provided by publisher.

Abstract Here, we systematically review clinical studies that report morbidity and outcomes data for cervical and lumbar surgeries performed in ambulatory surgery centers (ASCs). We focus on anterior cervical discectomy and fusion (ACDF), posterior cervical foraminotomy, cervical arthroplasty, lumbar microdiscectomy, lumbar laminectomy, and minimally invasive transforaminal interbody fusion (TLIF) and lateral lumbar interbody fusion, as these are prevalent and surgical spine procedures that are becoming more commonly performed in ASC settings. A systematic search of PubMed was conducted, using combinations of the following phrases: “outpatient,” “ambulatory,” or “ASC” with “anterior cervical discectomy fusion,” “ACDF,” “cervical arthroplasty,” “lumbar,” “microdiscectomy,” “laminectomy,” “transforaminal lumbar interbody fusion,” “spine surgery,” or “TLIF.” In reviewing the available literature to date, there is ample level 3 (retrospective comparisons) and level 4 (case series) evidence to support both the safety and effectiveness of outpatient cervical and lumbar surgery. While no level 1 or 2 (randomized clinical trials) evidence currently exists, the plethora of real-world clinical data creates a formidable argument for serious investments in ASCs for multiple spine procedures.

Purpose Integrating machine learning models into electronic medical record systems can greatly enhance decision-making, patient outcomes, and value-based care in healthcare systems. Challenges related to data accessibility, privacy, and sharing can impede the development and deployment of effective predictive models in spine surgery. Federated learning (FL) offers a decentralized approach to machine learning that allows local model training while preserving data privacy, making it well-suited for healthcare settings. Our objective was to describe federated learning solutions for enhanced predictive modeling in spine surgery. Methods The authors reviewed the literature. Results FL has promising applications in spine surgery, including telesurgery, AI-based prediction models, and medical image segmentation. Implementing FL requires careful consideration of infrastructure, data quality, and standardization, but it holds the potential to revolutionize orthopedic surgery while ensuring patient privacy and data control. Conclusions Federated learning shows great promise in revolutionizing predictive modeling in spine surgery by addressing the challenges of data privacy, accessibility, and sharing. The applications of FL in telesurgery, AI-based predictive models, and medical image segmentation have demonstrated their potential to enhance patient outcomes and value-based care.

Purpose To determine if differences in safety or efficacy exist between balloon kyphoplasty (BKP), vertebroplasty (VP) and non-surgical management (NSM) for the treatment of osteoporotic vertebral compression fractures (VCFs). Methods As of February 1, 2011, a PubMed search (key words: kyphoplasty, vertebroplasty) resulted in 1,587 articles out of which 27 met basic selection criteria (prospective multiple-arm studies with cohorts of ≥20 patients). This systematic review adheres to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Results Pain reduction in both BKP (−5.07/10 points, P  < 0.01) and VP (−4.55/10, P  < 0.01) was superior to that for NSM (−2.17/10), while no difference was found between BKP/VP ( P  = 0.35). Subsequent fractures occurred more frequently in the NSM group (22 %) compared with VP (11 %, P  = 0.04) and BKP (11 %, P  = 0.01). BKP resulted in greater kyphosis reduction than VP (4.8º vs. 1.7°, P  < 0.01). Quality of life (QOL) improvement showed superiority of BKP over VP ( P  = 0.04), along with a trend for disability improvement ( P  = 0.08). Cement extravasation was less frequent in the BKP ( P  = 0.01). Surgical intervention within the first 7 weeks yielded greater pain reduction than VCFs treated later. Conclusions BKP/VP provided greater pain relief and fewer subsequent fractures than NSM in osteoporotic VCFs. BKP is marginally favored over VP in disability improvement, and significantly favored in QOL improvement. BKP had a lower risk of cement extravasation and resulted in greater kyphosis correction. Despite this analysis being restricted to Level I and II studies, significant heterogeneity suggests that the current literature is delivering inconsistent messages and further trials are needed to delineate confounding variables.

Background Lumbar discectomy has been shown to be clinically beneficial in numerous studies for appropriately selected patients. Some patients, however, undergo revision discectomy, with previously reported estimates of revisions ranging from 5.1% to 7.9%. No study to date has been able to precisely quantify the rate of revision surgery over numerous years on a national scale. Questions/Purpose We performed a survival analysis for lumbar discectomy on a national scale using a life-table analysis to answer the following questions: (1) What is the rate of revision discectomy on a national scale over 5 to 7 years for patients undergoing primary discectomy alone? (2) Are there differences in revision discectomy rates based on age of patient, region of the country, or the payer type? Methods The Medicare 5% National Sample Administrative Database (SAF5) and a large national database from Humana Inc (HORTHO) were used to catalog the number of patients undergoing a lumbar discectomy. Both of these databases have been cited in numerous peer-reviewed publications during the previous 5 years and routinely are audited by PearlDiver Inc. We identified patients using relevant ICD-9 codes and Current Procedural Terminology (CPT) codes, including ICD-9 72210 (lumbar disc displacement) for disc herniation. We used appropriate CPT codes to identify patients who had a lumbar discectomy. We analyzed patients undergoing additional surgery including those who had repeat discectomy (CPT-63042: laminotomy, reexploration single interspace, lumbar) and patients who had additional more-extensive decompressive procedures with or without fusion after their primary procedure. Revision surgery rates were calculated for patients 65 years and older and those younger than 65 years and for each database (Humana Inc and Medicare). Patients from the two databases also were analyzed based on four distinct geographic regions in the United States where their surgery occurred. There were a total of 7520 patients who underwent a lumbar discectomy for an intervertebral disc displacement with at least 5 years of followup in the HORTHO and SAF5 databases. We used cumulative incidence of revision surgery to estimate the survivorship of these patients. Results In the HORTHO (2613 patients) and SAF5 (4907 patients) databases, 147 patients (5.6%; 95% CI, 1.8%–9.2%) and 305 patients (6.2%; 95% CI, 3.5%–8.9%) had revision surgery at 7 years after the index discectomy respectively. Survival analysis showed survival rates greater than 93% (95% CI, 91%–98%) for all of the cohorts for a primary discectomy up to 7 years after the surgery. The survivorship was lower for patients younger than 65 years (93% [95% CI, 87%–99%, 1016 of 1091] versus 95% [95% CI, 90%–100%, 1450 of 1522], p = 0.02). When nondiscectomy lumbar surgeries were included, the survivorship of patients younger than 65 years remained lower (83% [95% CI, 76%–89%, 902 of 1091] versus 87% [95% CI, 82%–92%, 1324 of 1522], p = 0.02). There was no difference in revision discectomy rates across geographic regions (p = 0.41) at 7 years. Similarly, there was no difference in additional nondiscectomy lumbar surgery rates (p = 0.68) across geographic regions at 7 years. There was no difference in survivorship rates between patients covered by Medicare (94% [95% CI, 91%–97%], 4602 of 4907) versus Humana Inc (94% [95% CI, 90%–98%], 2466 of 2613) (p = 0.31). Conclusions Our study shows rates of cumulative survival after an index lumbar discectomy with revision discectomy as the endpoint. We hope these data allow physicians to offer accurate advice to patients regarding the risk of revision surgery for patients of all ages during 5 to 7 years after their index procedure to enhance shared decision making in spinal surgery. These data also will help public policymakers and accountable care organizations accurately allocate scarce resources to patients with symptomatic lumbar disc herniation. Level of Evidence Level III, therapeutic study.

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.

PurposeRadiological degenerative phenotypes provide insight into a patient’s overall extent of disease and can be predictive for future pathological developments as well as surgical outcomes and complications. The objective of this study was to develop a reliable method for automatically classifying sagittal MRI image stacks of cervical spinal segments with respect to these degenerative phenotypes.MethodsWe manually evaluated sagittal image data of the cervical spine of 873 patients (5182 motion segments) with respect to 5 radiological phenotypes. We then used this data set as ground truth for training a range of multi-class multi-label deep learning-based models to classify each motion segment automatically, on which we then performed hyper-parameter optimization.ResultsThe ground truth evaluations turned out to be relatively balanced for the labels disc displacement posterior, osteophyte anterior superior, osteophyte posterior superior, and osteophyte posterior inferior. Although we could not identify a single model that worked equally well across all the labels, the 3D-convolutional approach turned out to be preferable for classifying all labels.ConclusionsClass imbalance in the training data and label noise made it difficult to achieve high predictive power for underrepresented classes. This shortcoming will be mitigated in the future versions by extending the training data set accordingly. Nevertheless, the classification performance rivals and in some cases surpasses that of human raters, while speeding up the evaluation process to only require a few seconds.