OBJECTIVE:This retrospective, single-center, descriptive case series evaluates early adoption of a two-stage spine MRI protocol incorporating high-resolution, three-dimensional mesoscale constructive interference in steady state (meso-CISS) for targeted cerebrospinal fluid (CSF) leak localization. BACKGROUND:Spontaneous intracranial hypotension (SIH) results from CSF leaks at the spinal canal, yet precise localization of dural defects remains challenging. Although fat-saturated heavily T2-weighted magnetic resonance (MR) myelography (HT2-MRM) is sensitive to spinal longitudinal extradural CSF collections (SLECs), it does not reliably localize the leak site. METHODS:Thirty-six consecutive patients with suspected SIH underwent a standardized total spine MRI protocol, including three-dimensional HT2-MRM for SLEC screening at Barnes-Jewish Hospital/Washington University School of Medicine between December 2023 and November 2025. In 11 SLEC-positive cases, meso-CISS was performed for targeted high-resolution leak localization. RESULTS:Among the 10 patients with SIH with interpretable meso-CISS imaging, findings consistent with dural defects were visualized in six (0.5-8 mm in size), with additional supportive features suggestive of dural defects identified in three patients. Compared with HT2-MRM, meso-CISS provided higher spatial detail, allowing clearer delineation of dural defects. CONCLUSION:A two-stage spine MRI protocol incorporating HT2-MRM and meso-CISS may aid in the noninvasive localization of dural defects in SIH. Meso-CISS provides high spatial resolution for visualization of small dural defects and associated pathology. Larger studies are required to clarify how this approach may be incorporated into existing diagnostic workflows for spinal CSF leak localization.

Study Design Narrative review. Objectives Artificial intelligence (AI) is being increasingly applied to the domain of spine surgery. We present a review of AI in spine surgery, including its use across all stages of the perioperative process and applications for research. We also provide commentary regarding future ethical considerations of AI use and how it may affect surgeon-industry relations. Methods We conducted a comprehensive literature review of peer-reviewed articles that examined applications of AI during the pre-, intra-, or postoperative spine surgery process. We also discussed the relationship among AI, spine industry partners, and surgeons. Results Preoperatively, AI has been mainly applied to image analysis, patient diagnosis and stratification, decision-making. Intraoperatively, AI has been used to aid image guidance and navigation. Postoperatively, AI has been used for outcomes prediction and analysis. AI can enable curation and analysis of huge datasets that can enhance research efforts. Large amounts of data are being accrued by industry sources for use by their AI platforms, though the inner workings of these datasets or algorithms are not well known. Conclusions AI has found numerous uses in the pre-, intra-, or postoperative spine surgery process, and the applications of AI continue to grow. The clinical applications and benefits of AI will continue to be more fully realized, but so will certain ethical considerations. Making industry-sponsored databases open source, or at least somehow available to the public, will help alleviate potential biases and obscurities between surgeons and industry and will benefit patient care.

The patient is a 22-year-old male with a history of C1 avulsion fracture causing vertebral artery compression with pseudoaneurysm and symptomatic stroke. Cerebral angiography demonstrated dynamic compression of the V3 segment of the vertebral artery due to a chronic C1 avulsion fracture. The authors utilized a full endoscopic approach with intraoperative angiography for proximal control and Doppler ultrasound to confirm adequate decompression. The surgery duration was 3 hours with blood loss < 5 ml. The patient was discharged on postoperative day 1 with no complication and has been asymptomatic since surgery. This is the first documented use of endoscopic decompression to treat this condition. The video can be found here: https://stream.cadmore.media/r10.3171/2024.1.FOCVID23234

BACKGROUND AND OBJECTIVES:Neurosurgeons and hospitals devote tremendous resources to improving recovery from lumbar spine surgery. Current efforts to predict surgical recovery rely on one-time patient report and health record information. However, longitudinal mobile health (mHealth) assessments integrating symptom dynamics from ecological momentary assessment (EMA) and wearable biometric data may capture important influences on recovery. Our objective was to evaluate whether a preoperative mHealth assessment integrating EMA with Fitbit monitoring improved predictions of spine surgery recovery. METHODS:Patients age 21-85 years undergoing lumbar surgery for degenerative disease between 2021 and 2023 were recruited. For up to 3 weeks preoperatively, participants completed EMAs up to 5 times daily asking about momentary pain, disability, depression, and catastrophizing. At the same time, they were passively monitored using Fitbit trackers. Study outcomes were good/excellent recovery on the Quality of Recovery-15 (QOR-15) and a clinically important change in Patient-Reported Outcomes Measurement Information System Pain Interference 1 month postoperatively. After feature engineering, several machine learning prediction models were tested. Prediction performance was measured using the c-statistic. RESULTS:A total of 133 participants were included, with a median (IQR) age of 62 (53, 68) years, and 56% were female. The median (IQR) number of preoperative EMAs completed was 78 (61, 95), and the median (IQR) number of days with usable Fitbit data was 17 (12, 21). 63 patients (48%) achieved a clinically meaningful improvement in Patient-Reported Outcomes Measurement Information System pain interference. Compared with traditional evaluations alone, mHealth evaluations led to a 34% improvement in predictions for pain interference (c = 0.82 vs c = 0.61). 49 patients (40%) had a good or excellent recovery based on the QOR-15. Including preoperative mHealth data led to a 30% improvement in predictions of QOR-15 (c = 0.70 vs c = 0.54). CONCLUSION:Multimodal mHealth evaluations improve predictions of lumbar surgery outcomes. These methods may be useful for informing patient selection and perioperative recovery strategies.

Surgical management of far lateral disc herniations remains challenging. Current transforaminal full-endoscopic approaches require non-visualized docking in the Kambin’s triangle and have been associated with significant risk of inadvertent nerve injury. We develop a full-endoscopic approach based on reliable bony landmarks allowing for visualization of the exiting nerve root prior to the far lateral discectomy. The surgical details of a full-endoscopic trans-pars interarticularis approach for far lateral discectomy are described. These descriptions include high quality intraoperative images and important surgical pearls. A small patient cohort is presented to demonstrate feasibility and safety of the procedure. We demonstrate the feasibility of this approach in 14 patients with a mean age of 59.5 ± 14.7 years. At a mean follow up of 21.9 ± 6.8 months, improvement of the visual analogue scale (VAS) for leg pain was 4.3 ± 1.0 resulting in minimally clinically important difference in 78.6

Spatial computing (SC) in a surgical context offers reconstructed interactive four-dimensional models of radiological imaging. Preoperative and postoperative assessment with SC can offer more insight into personalized surgical approaches. Spine surgery has benefitted from the use of perioperative SC assessment. Herein, we describe the use of SC to perform a perioperative assessment of a revision spinal deformity surgery. A 79-year-old wheelchair-bound male presented to the neurosurgery clinic with a history of chronic lumbar pain associated with bilateral lower extremity weakness. His surgical history is significant for an L2-L5 lumbar decompression with posterior fixation 1 year prior. On examination, there were signs of thoracic myelopathy. Imaging revealed his previous instrumentation, pseudoarthrosis, and cord compression. We perform a two-staged operation to address the thoracic spinal cord compression and myelopathy, pseudoarthrosis, and malalignment with a lack of global spinal harmony. His imaging is driven by a spatial computing and SC environment and offers support for the diagnosis of his L2-3 and L4-5 pseudoarthrosis on the reconstructed SC-based computed tomography scan. SC enabled the assessment of the configuration of the psoas muscle and course of critical neurovascular structures in addition to graft sizing, trajectory and approach, evaluation of the configuration and durability of the anterior longitudinal ligament, and the overlying abdominal viscera. SC increases the familiarity of the patient's specific anatomy and enhances perioperative assessment. As such, SC can be used to preoperatively plan for spinal revision surgery.