Spine Journal - 2026-07-01 - Journal Article
Diagnosis of lumbar spine pseudoarthrosis: a strain-based approach.
Hipp JA, Mikhael MM, Reitman CA, Patel VV, Chaput CD, DeVine J, Berven S, Nunley P, Grieco TF
Topics
Key Takeaway
A strain-based automated method classified 86% of posterolateral and 90% of posterolateral-plus-interbody fusion levels as Motion Compatible with Bridging by 60 months, with a CNN reducing uncertain classifications from 21% to 5%.
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Summary
This study evaluated whether FDA-cleared automated strain analysis of flexion-extension radiographs could objectively assess lumbar fusion status across multiple postoperative time points. Intervertebral strain was categorized as Motion Compatible with Bridging (MCB), Uncertain, or Motion Incompatible with Bridging for PL and PL+IB constructs; PL+IB fusions showed faster strain reduction, and both reached mean strain <5% by 60 months. A proof-of-concept CNN trained on motion-stabilized image pairs correctly classified 96% of levels and cut uncertain cases from 21% to 5%.
Key Limitation
The absence of a reference standard (CT, intraoperative exploration, or histology) means the MCB classification threshold is unvalidated against confirmed fusion or confirmed pseudoarthrosis, making sensitivity and specificity incalculable.
Original Abstract
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.