Spine surgery is essential for restoring alignment, stability, and function in patients with cervical spine injuries, especially when instability, pain, deformity, or progressive nerve damage is present. Effective wound closure is vital in these procedures, aiming to promote rapid healing, reduce infection risks, enable early mobilization, and ensure satisfactory cosmetic results. However, there is limited evidence on the optimal wound closure technique for posterior spine surgery, highlighting the need for innovative approaches. A study by Glener et al. evaluated the effectiveness of STRATAFIX™ Symmetric barbed sutures compared to traditional braided absorbable sutures in spinal surgery. In a randomized trial involving 20 patients, the STRATAFIX™ group demonstrated a shorter mean closure time and significantly fewer sutures used, though without a statistically significant reduction in closure time. No significant differences were observed in postoperative complications between the groups during a six-month follow-up. While the findings suggest potential cost savings and efficiency improvements with STRATAFIX™, the study's small sample size and short follow-up period limit its generalizability. Furthermore, AI-based models, such as the Xception deep learning model, show promise in improving suture training accuracy for medical students, which could enhance surgical outcomes and reduce complications. Despite the promising results, further research with larger sample sizes, extended follow-up periods, and multi-center trials is necessary to validate the effectiveness of barbed sutures like STRATAFIX™ in neurosurgery. The integration of AI in surgical training and continued exploration of innovative techniques are essential to advancing the field and optimizing patient care in spinal surgery.