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

## TL;DR

This paper introduces a new algorithm to help surgeons choose the best TLIF surgical approach based on patient-specific anatomy.

## Contribution

A novel algorithmic framework for selecting TLIF approaches based on patient-specific imaging and anatomical data.

## Key findings

- The proposed algorithm integrates patient-specific imaging and anatomical variability to guide TLIF approach selection.
- TF-TLIF is suggested for direct decompression cases, while PE-TLIF is suitable for patients with Kambin’s triangles ≥ 9 mm.
- The framework requires further validation and AI-driven tools for broader clinical use.

## Abstract

Transforaminal lumbar interbody fusion (TLIF) is a commonly employed surgical technique for managing lumbar degenerative disease and spinal instability. While it offers advantages over posterior lumbar interbody fusion (PLIF), traditional TLIF often involves prolonged recovery and morbidity due to muscle retraction. To improve outcomes, several alternative techniques have emerged, including minimally invasive TLIF (MIS-TLIF), trans-Kambin percutaneous TLIF (PE-TLIF), and transfacet TLIF (TF-TLIF). Each approach presents distinct anatomical and technical advantages, yet no standardized framework exists to guide their selection based on individual patient anatomy. In this study, we review the evolution of TLIF techniques and propose a novel algorithm that integrates patient-specific imaging, anatomical variability, and segmentation data to guide surgical decision-making. By analyzing the surgical corridors, indications, and limitations of each approach, and presenting representative clinical cases, we demonstrate how this algorithm can be applied in practice. For instance, TF-TLIF may be optimal in patients requiring direct decompression without major deformity, while PE-TLIF may be appropriate for those with Kambin’s triangles measuring ≥ 9 mm, allowing for indirect decompression. This tailored framework aims to optimize outcomes and reduce complications. Further prospective validation and incorporation of AI-driven segmentation tools are needed to support broader clinical implementation.