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Diagnosis of lumbar spine pseudoarthrosis: a strain-based approach
John A Hipp1, Mark M Mikhael2, Charles A Reitman3
1Medical Metrics, Inc., Houston, TX, USA.
Background Context:
Lumbar spine fusion is frequently performed to eliminate motion between vertebrae and thereby relieve symptoms. However, there is currently no clinically validated, biomechanically rational standard for diagnosing failure to achieve this surgical goal. A strain-based method has recently shown promise in assessing fusion status after cervical spine surgery. Its applicability to the lumbar spine remains unknown.
Purpose:
To evaluate the feasibility and performance of a strain-based approach for assessing lumbar spine fusion status.
Study Design:
Retrospective analysis of lumbar flexion-extension radiographs obtained following fusion surgery.
Methods:
Using FDA-cleared automated software, intervertebral strain was calculated from anatomic landmarks on flexion-extension radiographs obtained at multiple time points (3-60 months) following posterior-lateral (PL) or PL plus interbody (PL+IB) fusion. Strain values were categorized as: Motion Compatible with Bridging (MCB), Uncertain, or Motion Incompatible with Bridging. The percentage of levels in each category was determined over time and compared between fusion types. Adjacent-level strain was also evaluated. A proof-of-concept convolutional neural network was trained on motion-stabilized image pairs to classify uncertain levels.
Results:
Strain data were analyzed for 1,958 PL and 2,079 PL+IB fusion levels. PL+IB fusions demonstrated a faster reduction in intervertebral strain. By 60 months, average strain was <5% for both fusion types, with 86% of PL and 90% of PL+IB levels classified as MCB. Adjacent-level strain increased slightly after fusion surgery. The convolutional neural network correctly classified 96% of levels as MCB or Motion Incompatible with Bridging and reduced the proportion of uncertain cases from 21% to 5%.
Conclusions:
A strain-based method provides an objective, biomechanically grounded, and automated approach for monitoring fusion progression after lumbar spine surgery. A neural network can enhance this method by reducing the need for subjective review of borderline cases.
Clinical Significance:
Strain-based fusion assessment enables standardized, reproducible, and scalable evaluation of postsurgical spinal motion. With further validation, it may improve clinical decision-making and facilitate more consistent outcomes reporting in spine surgery research.
