Methods: The hemodynamic response to postural changes (supine to sitting and standing) was evaluated preoperatively and at 6, 12, 24, and 48 hours postoperatively in 30 patients who underwent single-level OLIF versus MIS-TLIF within an ERAS protocol. The protocols were evaluated sequentially, beginning with a change from supine to sitting, followed immediately by standing, with the patient remaining in the standing position for 3 minutes for evaluation. Results: This study compared OLIF and MIS-TLIF in 60 patients and found no significant differences in baseline characteristics. The OLIF group demonstrated greater hemodynamic stability within 6 hours after surgery, exhibiting smaller decreases in systolic blood pressure and mean arterial pressure, along with reduced fluid responsiveness compared to the MIS-TLIF group. Both groups of patients exhibited comparable heart rates and cardiac output stabilization over time. Clinically, OLIF resulted in greater postoperative back pain relief, lower blood loss (45±7.31 mL vs. 99.33±14.13 mL), and higher postoperative hemoglobin levels compared to MIS-TILF. Operative time, hospital stay, and complication rates were comparable between the OLIF and MIS-TLIF groups. Conclusions OLIF was associated with improved hemodynamic parameters within 6 hours postoperatively, less blood loss, and improved pain relief compared to MIS-TLIF, while both procedures demonstrated similar operative times, hospital stays, and no complications.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Objective: : Oblique lumbar interbody fusion (OLIF) is a surgical technique that utilizes a large interbody cage to indirectly decompress neural elements. The position of the cage relative to the vertebral body could affect the degree of foraminal decompression. Previous studies determined the position of the cage using plain radiographs, with conflicting results regarding the influence of the position of the cage to the degree of neural foramen decompression. Because of the cage obliquity, computed tomography (CT) has better accuracy than plain radiograph for the measurement of the obliquely inserted cage. The objective of this study is to find the correlation between the position of the OLIF cage with the degree of indirect decompression of foraminal stenosis using CT and magnetic resonance imaging (MRI). Methods: : We review imaging of 46 patients who underwent OLIF from L2-L5 for 68 levels. Segmental lordosis (SL) was measured in a plain radiograph. The positions of the cage were measured in CT. Spinal canal cross-sectional area (SCSA), and foraminal crosssectional area (FSCA) measurements using MRI were taken into consideration. Results: : Patients’ mean age was 69.7 years. SL increases 3.0±5.1 degrees. Significant increases in SCSA (33.3%), FCSA (43.7% on the left and 45.0% on the right foramen) were found (p<0.001). Multiple linear regression analysis shows putting the cage in the more posterior position correlated with more increase of FSCA and decreases SL correction. The position of the cage does not affect the degree of the central spinal canal decompression. Obliquity of the cage does not result in different degrees of foraminal decompression between right and left side neural foramen. Conclusion : Cage position near the posterior part of the vertebral body increases the decompression effect of the neural foramen while putting the cage in the more anterior position correlated with increases SL.