Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

» Although spine surgery is effective in reducing pain and improving functional status, it is associated with unacceptably high rates of complications, thus necessitating comprehensive preoperative patient optimization. » Numerous risk factors that can impact long-term surgical outcomes have been identified, including malnutrition, cardiovascular disease, osteoporosis, substance use, and more. » Preoperative screening and personalized, evidence-based interventions to manage medical comorbidities and optimize medications can enhance clinical outcomes and improve patient satisfaction following spine surgery. » Multidisciplinary team-based approaches, such as enhanced recovery after surgery protocols and multidisciplinary conferences, can further facilitate coordinated care from across specialties and reduce overall hospital length of stay.

Sacral insufficiency fractures commonly affect elderly women with osteoporosis and can cause debilitating lower back pain. First line management is often with conservative measures such as early mobilization, multimodal pain management, and osteoporosis management. If non-operative management fails, sacroplasty is a minimally invasive intervention that may be pursued. Candidates for sacroplasty are patients with persistent pain, inability to tolerate immobilization, or patients with low bone mineral density. Before undergoing sacroplasty, patients’ bone health should be optimized with pharmacotherapy. Anabolic agents prior to or in conjunction with sacroplasty have been shown to improve patient outcomes. Sacroplasty can be safely performed through a number of techniques: short-axis, long-axis, coaxial, transiliac, interpedicular, and balloon-assisted. The procedure has been demonstrated to rapidly and durably reduce pain and improve mobility, with little risk of complications. This article aims to provide a narrative literature review of sacroplasty including, patient selection and optimization, the various technical approaches, and short and long-term outcomes.

Background: Sarcopenia increases postoperative complication and mortality rates in elderly patients. Although measurement of the psoas muscle area on computed tomography (CT) scans is traditionally used to diagnose sarcopenia, CT is not routine in orthopaedic practice and causes unnecessary radiation exposure. EOS, a low-dose full-body imaging modality, captures musculoskeletal structures in an upright position, offering an alternative for sarcopenia diagnosis. Methods: Patients ≥18 years of age were included in this retrospective study if they had undergone non-contrast CT spine and EOS imaging between May 2022 and May 2024. Psoas muscle measurements at L3 and L4 were made using non-contrast CT scans, while thigh muscle measurements were obtained with EOS imaging. Inter- and intra-rater reliabilities were assessed using intraclass correlation coefficients (ICCs). Predicted probabilities for L4-psoas sarcopenia were determined through logistic regression, controlling for demographic covariates and validated with an 80% to 20% train-validate split. Sarcopenia cutoffs for anteroposterior (AP) thigh thickness and lateral (LAT) quadriceps thickness were determined with use of the Youden index. Results: Sarcopenia was identified in 23.1% of 134 patients (85 female and 49 male; 121 White, 7 Black, and 6 Hispanic) on the basis of L4-psoas muscle index thresholds. EOS and CT measurements showed excellent ICCs (≥0.90). Multivariable regressions identified AP thigh thickness and LAT quadriceps thickness as significant predictors of psoas area and L4-psoas sarcopenia. The area under the receiver operating characteristic curve for identifying L4-psoas sarcopenia was 0.85 for AP thigh thickness and 0.77 for LAT quadriceps thickness. Cutoffs were 12.47 cm (males) and 10.68 cm (females) for AP thigh thickness, and 3.23 cm (males) and 2.20 cm (females) for LAT quadriceps thickness. In the validation cohort of 27 patients, the AP thigh thickness model showed 0.94 sensitivity and 0.89 specificity, while the LAT quadriceps thickness model showed 0.70 sensitivity and 1.00 specificity. Applying these cutoffs to the entire data set showed that 66.7% of males and 75.0% of females with measurements below both cutoffs had sarcopenia. Conclusions: EOS is a reliable alternative to CT for muscle mass assessment and sarcopenia diagnosis. EOS may be a valuable tool for assessing sarcopenia without a CT scan, as thigh muscle measurements via EOS correlate well with CT-derived psoas measurements. This imaging modality aids in early sarcopenia diagnosis, potentially enhancing preoperative planning and reducing radiation exposure, unnecessary costs, and resource utilization. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

» The incidence of postoperative foot drop after spine surgery is estimated at 3.33%, with higher rates reported in complex procedures such as high-grade spondylolisthesis correction (up to 30%). Risk factors include disc-space distraction, deformity corrections, prolonged surgery, and advanced patient age. » The primary mechanisms of postoperative foot drop include direct nerve trauma, stretch injuries from retraction or distraction, compression from hematomas or implants, and ischemic damage because of disrupted blood flow. » Preoperative counseling, intraoperative precision, appropriate disc space distraction, and careful nerve retraction are key to mitigating the risk of foot drop, with ongoing research needed to standardize preventive and management guidelines. » Treatment strategies are tailored to the underlying cause, ranging from conservative options (physical therapy, ankle-foot orthoses, and functional electrical stimulation) to surgical interventions (hematoma evacuation, implant removal, neurolysis, nerve transfer, and tendon transfer), although outcomes are highly variable.