Advancing Spinal Instability Diagnosis with Artificial Intelligence

Dear Colleagues,

Spinal instability is a widely cited yet poorly defined diagnosis in both research and clinical practice, often guiding treatment decisions despite the absence of well-validated diagnostic tests. Challenges in routinely obtaining high-quality imaging, accurately assessing intervertebral motion and soft tissue abnormalities, and understanding how different forms of instability affect patients have hindered progress. Artificial intelligence may offer transformative solutions to these challenges. This Special Issue aims to showcase cutting-edge AI-driven approaches for diagnosing spinal instability, understanding its underlying causes, and monitoring its progression or response to treatment, paving the way for more precise and effective clinical management strategies.

Topics of interest include (but are not limited to) the following:

• The clinical relevance of spinal stability, including the pain/suffering and healthcare costs resulting from an incorrect diagnosis. What are the intervention points in the clinical pathway where AI might improve diagnostic accuracy and patient management.
• AI-enhanced radiographic measurements of stability/instability and related abnormalities.
• AI-enhanced MRI assessments of instability, including detecting damage to soft-tissues, accounting for normal degenerative changes, and identifying abnormalities associated with instability, including spinal malalignment. This includes AI-enhanced assessments of the facet fluid sign, disc and facet vacuum signs, muscle properties, etc.
• AI-enhanced 3D assessments to document motion abnormalities diagnostic of instability in multiple anatomic planes.
• AI to provide rapid feedback to technicians to improve image quality.
• AI-enhanced assessments in the context of trauma care.
• AI/LLMs for multi-modal integration—clinic notes, physical exam, history, blood tests, assessments, and measurements from multiple imaging modalities/findings.
• AI to provide personalized normative data for each patient, to mitigate the need to use population norms that may not represent normal for a specific patient.
• AI to establish and deploy companion diagnostics for specific treatment options, such as biologics to treat degenerative disc disease.

Dr. John Hipp
Guest Editor

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• spine
• lumbar
• cervical
• thoracic
• instability
• stability
• diagnosis
• medical imaging
• degenerative
• trauma
• intervertebral motion
• spinal alignment
• loss of motion segment integrity