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This review aims to systematically evaluate the incidence, management strategies, and clinical outcomes of iatrogenic durotomy (ID) in endoscopic spine surgery and to propose a management flowchart based on the tear size and associated complications. A comprehensive literature search was conducted, focusing on studies involving endoscopic spinal procedures and incidental durotomy. The selected studies were analyzed for management techniques and outcomes, particularly in relation to the size of the dural tear and the presence of nerve root herniation. Based on these findings, a flowchart for intraoperative management was developed. A total of 14 studies were included, encompassing 68,546 patients. Varying incidences of ID, with management strategies largely dependent on the size of the dural tear, were found. Small tears (less than 5 mm) were often left untreated or managed with absorbable hemostatic agents, while medium (5–10 mm) and large tears (greater than 10 mm) required more complex approaches like endoscopic patch repair or open surgery. The presence of nerve root herniation necessitated immediate action, often influencing the decision to convert to open repair. Effective management of ID in endoscopic spine surgery requires a nuanced approach tailored to the size of the tear and specific intraoperative challenges, such as nerve root herniation. The proposed flowchart offers a structured approach to these complexities, potentially enhancing clinical outcomes and reducing complication rates. Future research with more rigorous methodologies is necessary to refine these management strategies further and broaden the applications of endoscopic spine surgery. [ABSTRACT FROM AUTHOR]

Simple Summary: The InterVision framework employs advanced deep learning techniques to interpolate or create intermediate images between existing ones using deformable vectors, thereby capturing specific patient characteristics, such as unique anatomical features and variations in organ shape, size, and position. These characteristics are vital for personalizing treatment plans in radiotherapy, as they allow for the use of pre-planning information, which is available before the treatment begins, ensuring a tailored and precise approach to each patient's care. The training process involves two steps: first, generating a general model using a comprehensive dataset, and second, fine-tuning this general model with additional data produced by the InterVision framework. By incorporating the dataset generated through the InterVision framework, we were able to create a more personalized model, surpassing the level of customization achieved by previous fine-tuning approaches. The performance of these models is evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) across 18 anatomical structures in 20 test patients. A total of 18 anatomical structures were selected based on prior treatments that involved the most organs, and 20 test patients were chosen according to the availability that has a re-planning CT and manual contours within the total dataset. This framework is especially valuable for accurately predicting complex organs and targets that present significant challenges for traditional deep learning algorithms, particularly due to the intricate contours and the variability in organ shapes across different patients. Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms. [ABSTRACT FROM AUTHOR]

Study Design: Randomized controlled trial Objective: The primary objective of this study was to compare the short-term clinical and radiological outcomes of full-endoscopic lumbar laminotomy (FEL) with those of subtotal lumbar laminectomy (STL) for lumbar spinal stenosis (LSS). Methods: In this prospective randomized trial a total of 52 patients were enrolled from May 2016 to September 2021 after providing written informed consent. The authors investigated 45 patients who were followed up for more than 6 months. Results: There were significant improvements in visual analogue scale (VAS) and Oswestry Disability Index (ODI) scores after the operation in both groups. The FEL group tended to have significantly shorter hospital stay. Interestingly, a statistically significant increase in postoperative lumbar lordosis and segmental angle was observed in the FEL group and both groups, respectively. Spondylolisthesis was exacerbated or newly developed in five of the 21 patients (24%) in the STL group. In contrast, improvement in spondylolisthesis was observed in two of the 24 patients (8%) in the FEL group. There were no complications that resulted in fatal sequelae and no significant difference in the complication rate. Conclusions: The clinical results of FEL were similar to those of STL. In addition, the results of FEL were superior to those of STL in terms of a decrease in the postoperative length of stay and radiologic instability, such as iatrogenic spondylolisthesis. The results of this study indicate that FEL is a comparable surgical method to STL for LSS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]