Automated radiographic metrics for diagnosing lumbar spine instability: a cross-sectional observational study
New and fully automated approaches to detecting abnormal sagittal plane intervertebral motion may lead to enhanced and standardized diagnosis of lumbar spine instability. Further clinical research is imperative to verify prevalences and validate the efficacy of these metrics in diagnosis and treatment algorithms. If supported by additional research, these metrics may help determine, for example, which lumbar spinal stenosis patients require fusion in addition to decompression surgery.
Authors
- Charles A. Reitman (ORCID: https://orcid.org/0000-0003-3482-959X)
- Christopher D. Chaput
- Trevor F. Grieco (ORCID: https://orcid.org/0000-0001-8631-9845)
- Zoher Ghogawala (ORCID: https://orcid.org/0000-0003-1345-1831)
- John A. Hipp (ORCID: https://orcid.org/0000-0002-2008-3523)
- Zorica Buser (ORCID: https://orcid.org/0000-0002-5680-0643)
Institutions
- Lahey Hospital and Medical Center (US)
- Medical University of South Carolina (US)
- The University of Texas at San Antonio Health Science Center (US)
- Lahey Medical Center (US)
- New York University (US)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.21037/qims-24-1931
- Citations
- 3
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.02