Optimizing TLIF Approach Selection: An Algorithmic Framework with Illustrative Cases.

Area of Science:

• Neurosurgery
• Orthopedic Surgery
• Spinal Fusion Techniques

Background:

• Transforaminal lumbar interbody fusion (TLIF) is a common procedure for lumbar degenerative disease and instability.
• Traditional TLIF can lead to prolonged recovery and morbidity due to extensive muscle retraction.
• Minimally invasive techniques like MIS-TLIF, PE-TLIF, and TF-TLIF have emerged to improve outcomes.

Purpose of the Study:

• To review the evolution of TLIF surgical techniques.
• To propose a novel algorithm for selecting the optimal TLIF approach based on patient-specific anatomy.
• To guide surgical decision-making and improve patient outcomes in spinal fusion.

Main Methods:

• Review of current TLIF techniques, including their anatomical and technical advantages.
• Development of a decision-making algorithm integrating patient imaging and anatomical data.
• Analysis of surgical corridors, indications, and limitations of various TLIF approaches.
• Presentation of clinical cases to illustrate algorithm application.

Main Results:

• The proposed algorithm integrates patient-specific imaging and anatomical variability for tailored TLIF approach selection.
• Specific examples demonstrate suitability: TF-TLIF for direct decompression, PE-TLIF for indirect decompression in suitable Kambin's triangles.
• The framework aims to optimize outcomes and minimize complications by matching technique to patient anatomy.

Conclusions:

• A novel algorithm can guide the selection of TLIF techniques, optimizing patient-specific treatment.
• Tailored surgical approaches can improve outcomes and reduce complications in spinal fusion surgery.
• Further validation and AI integration are recommended for widespread clinical adoption.