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Spine pathology afflicts people across the globe and is responsible for a large portion of physician visits and healthcare costs. Imaging such as plain radiographs, CT, MRI, and ultrasound is vital to assess structure, function, and stability of the spine and also provide guidance in therapeutic interventions. Ultrasound utilization in spine conditions is less ubiquitous, but provides benefits in low costs, portability, and dynamic imaging. This study assesses ultrasound efficacy in diagnosis and therapeutic interventions for spine pathology. A systematic review conducted via PubMed, MEDLINE, and Google Scholar identified 3,630 papers with eventual inclusion of 73 papers with an additional 21 papers supplemental papers subsequently added. Findings highlighted ultrasound utilization for different structural elements of the spine such as muscle, bone, disc, ligament, canal, and joints are presented and compared with radiographs, CT, and MRI imaging where relevant. Spinal curvature and mobility are similarly presented. Ultrasound efficacy for guided therapeutics about the spine is presented and assessed against other modalities. Ultrasound is a widely used and efficacious modality to guide injections about the spine. Diagnostic utility is less well studied, but shows promise in assessing fractures, posterior ligamentous stability, and intra-operative hardware placement. The low cost, portability, and dynamic imaging ability make it an attractive modality particularly for developing health systems and resource limited environments such as combat settings and the International Space Station. Further study is recommended before broad adoption in diagnostics.

Background: The ability to accurately predict postoperative outcomes is of considerable interest in the field of orthopaedic surgery. Machine learning has been used as a form of predictive modeling in multiple health-care settings. The purpose of the current study was to determine whether machine learning algorithms using preoperative data can predict improvement in American Shoulder and Elbow Surgeons (ASES) scores for patients with glenohumeral osteoarthritis (OA) at a minimum of 2 years after shoulder arthroplasty. Methods: This was a retrospective cohort study that included 472 patients (472 shoulders) diagnosed with primary glenohumeral OA (mean age, 68 years; 56% male) treated with shoulder arthroplasty (431 anatomic total shoulder arthroplasty and 41 reverse total shoulder arthroplasty). Preoperative computed tomography (CT) scans were used to classify patients on the basis of glenoid and rotator cuff morphology. Preoperative and final postoperative ASES scores were used to assess the level of improvement. Patients were separated into 3 improvement ranges of approximately equal size. Machine learning methods that related patterns of these variables to outcome ranges were employed. Three modeling approaches were compared: a model with the use of all baseline variables (Model 1), a model omitting morphological variables (Model 2), and a model omitting ASES variables (Model 3). Results: Improvement ranges of ≤28 points (class A), 29 to 55 points (class B), and >55 points (class C) were established. Using all follow-up time intervals, Model 1 gave the most accurate predictions, with probability values of 0.94, 0.95, and 0.94 for classes A, B, and C, respectively. This was followed by Model 2 (0.93, 0.80, and 0.73) and Model 3 (0.77, 0.72, and 0.71). Conclusions: Machine learning can accurately predict the level of improvement after shoulder arthroplasty for glenohumeral OA. This may allow physicians to improve patient satisfaction by better managing expectations. These predictions were most accurate when latent variables were combined with morphological variables, suggesting that both patients’ perceptions and structural pathology are critical to optimizing outcomes in shoulder arthroplasty. Level of Evidence: Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.

The authors have no financial or personal relationships to people or organisations that could potentially and inappropriately influence their work and conclusions.