Objective: To evaluate the accuracy of computer-assisted pedicle screw installation and its clinical benefit as compared with conventional pedicle screw installation techniques.
Methods: Total 176 thoracic pedicle screws placed in 42 thoracic fracture patients were involved in the study randomly, 20 patients under conventional fluoroscopic control (84 screws) and 22 patients had screw insertion under three dimensional (3D) computer-assisted navigation (92 screws). The 2 groups were compared for accuracy of screw placement, time for screw insertion by postoperative thin-cut CT scans and statistical analysis by X(2) test. The cortical perforations were then graded by 2-mm increments: Grade I (good, no cortical perforation), Grade II (screw outside the pedicle less than 2 mm), Grade III (screw outside the pedicle larger than 2 mm).
Results: In computer assisted group, 88 (95.65%) were Grade I (good), 4 (4.35%) were Grade II (less than 2mm), no Grade III (larger than 2 mm) violations. In conventional group, there were 14 cortical violations (16.67%), 70 (83.33%) were Grade I (good), 11 (13.1%) were Grade II (less than 2 mm), and 3 (3.57%) were Grade III (larger than 2 mm) violations (P less than 0.001). The number (19.57%) of upper thoracic pedicle screws ( T(1)-T(4) ) inserted under 3D computer-assisted navigation was significantly higher than that (3.57%) by conventional fluoroscopic control (P less than 0.001). Average screw insertion time in conventional group was (4.56+/-1.03) min and (2.54+/-0.63) min in computer assisted group (P less than 0.001). In the conventional group, one patient had pleura injury and one had a minor dura violation.
Conclusions: This study provides further evidence that 3D computer-assisted navigation placement of pedicle screws can increase accuracy, reduce surgical time, and be performed safely and effectively at all levels of the thoracic spine, particularly upper thoracic spine.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Clinical Study on the Effects of Total Hip Arthroplasty Assisted by Virtual Planning Combined With Intraoperative Navigation Templates.
Orthop Surg
December 2024
South China University of Technology School of Medicine, Guangzhou, Guangdong, China.
Objectives: Although total hip arthroplasty (THA) effectively alleviates pain and restores joint function in the end-stage hip disease, challenges remain in achieving precise osteotomy and minimizing subjective dependency on prosthesis positioning. This study aims to evaluate the efficacy and safety of preoperative virtual planning and navigation templates compared to conventional techniques, providing new methods to enhance the precision and personalization of THA.
Methods: During the period from 2022 to 2023, we conducted a retrospective case-control study on 74 patients who underwent THA surgery at our hospital, based on the inclusion and exclusion criteria.
DeBo: Contrast enhancement for image registration using binary differential evolution and bat optimization.
PLoS One
December 2024
Shanghai Key Laboratory of Navigation and Location-based Services, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.
Image registration has demonstrated its significance as an essential tool for target recognition, classification, tracking, and damage assessment during natural catastrophes. The image registration process relies on the identification of numerous reliable features; thus, low resolutions, poor lighting conditions, and low image contrast substantially diminish the number of dependable features available for registration. Contrast stretching enhances image quality, facilitating the object detection process.
View Article and Find Full Text PDFAll Enabling Technology Is Not Created Equal: Comparing Outcomes of Computer-Assisted Fluoroscopic Navigation Versus Robotic-Assisted Total Hip Arthroplasty.
J Am Acad Orthop Surg Glob Res Rev
December 2024
From the Connecticut Orthopaedics, Fairfield, CT (Dr. Bernstein); the Epidemiology and Real-World Data Sciences, Johnson & Johnson MedTech, New Brunswick, NJ (Dr. Gupta and Ruppenkamp); the Global Health Economics and Market Access, Johnson & Johnson MedTech, Raynham, MA (Dr. Kabiri and Goldstein); and the Medical Affairs, Johnson & Johnson MedTech, Palm Beach Gardens, FL (Dr. Diaz).
Background: Computer-assisted fluoroscopic navigation and robotic technologies aim to optimize implant placement and alignment in primary total hip arthroplasty (THA) to improve patient outcomes. This study uses a retrospective hospital billing database covering 1,300 hospitals to compare the clinical and economic effect of these technologies.
Methods: The study compared patients undergoing THA with robotic versus computer-assisted fluoroscopic navigation technologies between January 1, 2016, and September 30, 2021, using the Premier Healthcare Database.
Background: Navigation surgical systems have been widely used in spinal fusion to ensure accuracy and safety during pedicle screw insertion.
Methods: The research was performed under laboratory conditions, using stereotactic navigation, surgical instruments for spinal fusion, development of additional devices and software. During the experiments, all stages of the computed tomography-guided navigation system use were performed-preoperative preparation of patient data and planning to provide visual control of the navigation of surgical instruments during the insertion of screws.
A Spatial Registration Method Based on Point Cloud and Deep Learning for Augmented Reality Neurosurgical Navigation.
Int J Med Robot
December 2024
School of Mechanical Engineering, Tianjin University, Tianjin, China.
Background: In order to achieve spatial registration for surgical navigation, a spatial registration method based on point cloud and deep learning is proposed.
Methods: Neural networks are used to register medical image point clouds and patient surface point clouds to complete spatial registration of surgical navigation. An image processing method is designed to convert medical images into point clouds, and a structured light robot is used to extract patient surface point clouds.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!