Spine
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Multicenter Study Observational Study
Deep Learning Application in Spinal Implant Identification.
Retrospective observational study. ⋯ The deep learning application is effective for spinal implant identification. This demonstrates that clinicians can use ML-based deep learning applications to improve clinical practice and patient care.Level of Evidence: 3.
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Single-center retrospective study. ⋯ PESES must be ruled out at diagnosis of a spinal tumor when facing a fast-growing lesion with neurological deficits in a young adult. Thoracoabdominopelvic extension should be carried out. Presurgical biopsy must be performed. In case of PESES, neoadjuvant chemotherapy must be established before considering surgical intervention.Level of Evidence: 4.
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A retrospective case-control study. ⋯ The FT on the sagittal, axial, and coronal planes are all associated with CDH in the subaxial cervical spine. The greater facet angle at the left or right side does not affect the side of herniation. The severity of cervical disc degeneration is not associated with FT.Level of Evidence: 3.