Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

This video presents a case of L4–5 unstable spondylolisthesis treated with full-endoscopic transforaminal lumbar interbody fusion (Endo-TLIF), emphasizing the GUARD (Glider Used as a Rotary Device) technique for nerve root protection. This innovative approach involves controlled rotation of the cage glider before cage insertion to minimize the risk of nerve root injury, a significant complication in Endo-TLIF procedures. The GUARD technique, validated in previous cadaveric studies, provides enhanced safety during cage insertion by protecting the nerve root. A 48-year-old woman with a 3-year history of progressive low back pain and bilateral lower extremity radiculopathy (right-sided predominance) was diagnosed with L4–5 unstable spondylolisthesis and spinal stenosis. After failure of conservative management, she underwent uniportal full-endoscopic facet-resecting transforaminal lumbar interbody fusion using the GUARD technique. Postoperatively, the patient experienced significant symptomatic improvement and resolution of radiculopathy, without any intraoperative nerve root injury or postoperative neurological deficits. This case demonstrates the effectiveness of the GUARD technique in reducing neurological complications and improving patient outcomes.

Objective: Uniportal full-endoscopic transforaminal lumbar interbody fusion (FE-TLIF) carries a unique risk of nerve traction and abrasion injury during cage insertion. This study aims to evaluate the clinical efficacy of the GUARD technique and delayed ligamentum flavectomy in reducing postoperative radicular pain and neurapraxia in patients undergoing uniportal FE-TLIF. Methods: A retrospective analysis was conducted on 45 patients with an average age of 53.9±12.4 years who underwent either FE facet-sparing TLIF (FE fs-TLIF) or FE facet-resecting TLIF (FE fr-TLIF). Patients were divided into 2 groups: the sentinel group (21 patients) using traditional sentinel pin techniques, and the GUARD group (24 patients) using the GUARD technique with delayed ligamentum flavectomy. Patient-reported outcomes included the visual analogue scale (VAS) for leg and back pain, and Oswestry Disability Index. Complication rates, including incidental durotomy, postoperative neurapraxia, and hematoma, were also documented. Results: Postoperative radicular pain in the legs was significantly reduced at 6 weeks in the GUARD group compared to the sentinel group (VAS: 2.201 vs. 3.267, p=0.021). The incidence of postoperative neurapraxia was markedly lower in the GUARD group (0% vs. 19%, p=0.047). Both groups showed similar improvements in disc height, segmental lordosis, and lumbar lordosis at the 1-year follow-up, with no significant differences in endplate injury or fusion rates. Conclusion The GUARD technique and delayed ligamentum flavectomy significantly enhance patient safety by reducing postoperative radicular pain and neurapraxia without incurring additional costs. These techniques are easy to learn and integrate into existing surgical workflows, offering a valuable improvement for surgeons performing FE-TLIF procedures.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

This video presents a case of L4–5 unstable spondylolisthesis treated with full-endoscopic transforaminal lumbar interbody fusion (Endo-TLIF), emphasizing the GUARD (Glider Used as a Rotary Device) technique for nerve root protection. This innovative approach involves controlled rotation of the cage glider before cage insertion to minimize the risk of nerve root injury, a significant complication in Endo-TLIF procedures. The GUARD technique, validated in previous cadaveric studies, provides enhanced safety during cage insertion by protecting the nerve root. A 48-year-old woman with a 3-year history of progressive low back pain and bilateral lower extremity radiculopathy (right-sided predominance) was diagnosed with L4–5 unstable spondylolisthesis and spinal stenosis. After failure of conservative management, she underwent uniportal full-endoscopic facet-resecting transforaminal lumbar interbody fusion using the GUARD technique. Postoperatively, the patient experienced significant symptomatic improvement and resolution of radiculopathy, without any intraoperative nerve root injury or postoperative neurological deficits. This case demonstrates the effectiveness of the GUARD technique in reducing neurological complications and improving patient outcomes.

Objective: Uniportal full-endoscopic transforaminal lumbar interbody fusion (FE-TLIF) carries a unique risk of nerve traction and abrasion injury during cage insertion. This study aims to evaluate the clinical efficacy of the GUARD technique and delayed ligamentum flavectomy in reducing postoperative radicular pain and neurapraxia in patients undergoing uniportal FE-TLIF. Methods: A retrospective analysis was conducted on 45 patients with an average age of 53.9±12.4 years who underwent either FE facet-sparing TLIF (FE fs-TLIF) or FE facet-resecting TLIF (FE fr-TLIF). Patients were divided into 2 groups: the sentinel group (21 patients) using traditional sentinel pin techniques, and the GUARD group (24 patients) using the GUARD technique with delayed ligamentum flavectomy. Patient-reported outcomes included the visual analogue scale (VAS) for leg and back pain, and Oswestry Disability Index. Complication rates, including incidental durotomy, postoperative neurapraxia, and hematoma, were also documented. Results: Postoperative radicular pain in the legs was significantly reduced at 6 weeks in the GUARD group compared to the sentinel group (VAS: 2.201 vs. 3.267, p=0.021). The incidence of postoperative neurapraxia was markedly lower in the GUARD group (0% vs. 19%, p=0.047). Both groups showed similar improvements in disc height, segmental lordosis, and lumbar lordosis at the 1-year follow-up, with no significant differences in endplate injury or fusion rates. Conclusion The GUARD technique and delayed ligamentum flavectomy significantly enhance patient safety by reducing postoperative radicular pain and neurapraxia without incurring additional costs. These techniques are easy to learn and integrate into existing surgical workflows, offering a valuable improvement for surgeons performing FE-TLIF procedures.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.