Endovascular approaches for the treatment of dural carotid-cavernous fistulas: A systematic review.

INTRODUCTION: Dural carotid-cavernous fistulas (dCCFs), also known as indirect carotid-cavernous fistulas, represent abnormal connections between the arterial and venous systems within the cavernous sinus that are typically treated via endovascular approach. We aim to investigate the clinical characteristics of patients with dCCFs based on the endovascular treatment approach and assess angiographic and clinical outcomes. METHODS: A systematic review of the literature was performed. Data including number of patients, demographics, presenting clinical symptoms, etiology of fistula, Barrow classification, and embolization material were collected and evaluated. Outcome measures collected included degree of fistula occlusion, postoperative symptoms, complications, and mean follow-up time. RESULTS: A total of 52 studies were included examining four primary endovascular approaches for treating dCCFs: transarterial, transfemoral-transvenous (transpetrosal or other), transorbital (percutaneous or via cutdown), and direct transfacial access. Overall data was collected from 736 patients with 817 dCCFs. Transarterial approaches exhibit lower dCCF occlusion rates (75.6%) compared to transvenous techniques via the inferior petrosal sinus (88.1%). The transorbital approach via direct puncture or surgical cutdown offers a more direct path to the cavernous sinus, although with greater complications including risk of orbital hematoma. The direct transfacial vein approach, though limited, shows up to 100% occlusion rates and minimal complications. CONCLUSION: We provide a comprehensive review of four main endovascular approaches for dCCFs. In summary, available endovascular treatment options for dCCFs have expanded and provide effective solutions with generally favorable outcomes. While the choice of approach depends on individual patient factors and technique availability, traditional transvenous procedures have emerged as the first-line endovascular treatment. There is growing, favorable literature on direct transorbital and transfacial approaches; however, more studies directly comparing these general transvenous options are necessary to refine treatment strategies.

Computed tomography assessment of robotic versus fluoroscopic implant accuracy in sacroiliac joint fusion.

OBJECTIVE: Computed tomography is considered the gold-standard imaging tool to evaluate spinal implant accuracy. However, there are no studies that evaluate the accuracy of robotic sacroiliac joint (SIJ) implant placement using CT to date. The aim of this study was to compare the accuracy of implant placement on CT between robotic and fluoroscopic navigation for SIJ fusion and the subsequent complications and clinical outcomes of suboptimally placed screws. METHODS: A retrospective analysis of SIJ fusions utilizing either robotic or fluoroscopic guidance at a single institution was conducted from 2014 to 2023. Implant placement accuracy was evaluated on intra- or postoperative CT. Primary endpoints were SIJ screw placement accuracy and complications. Secondary endpoints were pain status at the first and second follow-ups and rates of 2-year revision surgery. Statistical analysis was performed using chi-square tests. RESULTS: Sixty-nine patients who underwent 78 SIJ fusions were included, of which 63 were robotic and 15 were fluoroscopic. The mean age of the cohort at the time of surgery was 55.9 ± 14.2 years, and 35 patients (50.7%) were female. There were 135 robotically placed and 34 fluoroscopically placed implants. A significant difference was found in implant placement accuracy between robotic and fluoroscopic fusion (97.8% vs 76.5%, p < 0.001). When comparing optimal versus suboptimal implant placement, no difference was found in the presence of 30-day complications (p = 0.98). No intraoperative complications were present in this cohort. No difference was found in subjective pain status at the first (p = 0.69) and second (p = 0.45) follow-ups between optimal and suboptimal implant placement. No patients underwent 2-year revision surgery. CONCLUSIONS: Use of robotic navigation was significantly more accurate than the use of fluoroscopic navigation for SIJ implant placement. Complications overall were low and not different between optimally and suboptimally placed implants. Suboptimally placed implants did not differ in degree of subjective pain improvement or rates of revision surgery postoperatively.

Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.

OBJECTIVE: Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited to some degree by manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry. METHODS: SpinePose was trained and validated on 761 sagittal whole-spine radiographs to predict the sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), lumbar lordosis (LL), T1 pelvic angle (T1PA), and L1 pelvic angle (L1PA). A separate test set of 40 radiographs was labeled by four reviewers, including fellowship-trained spine surgeons and a fellowship-trained radiologist with neuroradiology subspecialty certification. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test images. Intraclass correlation coefficients (ICCs) were used to assess interrater reliability. RESULTS: SpinePose exhibited the following median (interquartile range) parameter errors: SVA 2.2 mm (2.3 mm) (p = 0.93), PT 1.3° (1.2°) (p = 0.48), SS 1.7° (2.2°) (p = 0.64), PI 2.2° (2.1°) (p = 0.24), LL 2.6° (4.0°) (p = 0.89), T1PA 1.1° (0.9°) (p = 0.42), and L1PA 1.4° (1.6°) (p = 0.49). Model predictions also exhibited excellent reliability at all parameters (ICC 0.91-1.0). CONCLUSIONS: SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to that of fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spinal imaging can substantially aid in patient selection and surgical planning.