Innovation in Spinal Fusion Surgery Techniques; A Review of Current Advance and Future Directions

Aymen Nasreldin Abalkariem; Harsha Sai Krishna Gottimukkala; Mohammad Faheem Shaikh; Afshan Khalid

doi:10.70749/ijbr.v3i1.510

Authors

Aymen Nasreldin Abalkariem Anatomy Department, Najran University, KSA.
Harsha Sai Krishna Gottimukkala Institute UT, Health School of Public Health, Houston, USA.
Mohammad Faheem Shaikh Department of Neurosurgery, Security Forces Hospital, Riyadh, KSA.
Afshan Khalid Ibn-e-Sina Hospital, Multan, Punjab, Pakistan.

DOI:

https://doi.org/10.70749/ijbr.v3i1.510

Keywords:

Spinal Fusion Surgery, Minimally Invasive Techniques, Robotic-Assisted Surgery, Biologic Therapies, 3D-Printed Spinal Implants

Abstract

Spinal fusion surgery is a critical procedure used to treat various spinal disorders, including degenerative diseases, deformities, trauma, and tumors. Over the past few decades, advancements have been made to improve patient outcomes, reduce complications, and shorten recovery times. This review highlights key innovations in spinal fusion techniques, focusing on minimally invasive approaches, robotic-assisted surgery, biologic therapies, and advanced spinal implants.

Minimally invasive techniques offer benefits such as reduced blood loss, less postoperative pain, and shorter hospital stays compared to traditional open surgeries, though their success depends on the surgeon’s expertise. Robotic-assisted surgery has transformed implant placement, enhancing precision and reducing screw misplacement, leading to improved long-term outcomes. However, the high cost and steep learning curve remain obstacles for widespread adoption. Biologic treatments, including bone morphogenetic proteins (BMPs) and stem cells, have shown promise in improving fusion rates and accelerating healing, but concerns regarding safety and efficacy continue.

The development of 3D-printed spinal implants and dynamic stabilization systems provides personalized solutions, offering better fit and biomechanical compatibility while potentially reducing adjacent segment degeneration. Additionally, incorporating artificial intelligence (AI) and machine learning (ML) in surgical planning and postoperative care holds the potential to optimize treatment strategies, predict complications, and improve patient-specific outcomes.

While these innovations show great promise, challenges such as cost, accessibility, and the need for further clinical validation persist. The future of spinal fusion surgery will depend on the continued integration of these technologies, improving precision, and offering more tailored treatments for enhanced patient outcomes and long-term spinal health.

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