Surgical Navigation Research Articles

Rational design of NIR-II molecule-engineered nanoplatform for preoperative downstaging and imaging-guided surgery of orthotopic hepatic tumor

Orthotopic advanced hepatic tumor resection without precise location and preoperative downstaging may cause clinical postoperative recurrence and metastasis. Early accurate monitoring and tumor size reduction based on the multifunctional diagnostic-therapeutic integration platform could improve real-time imaging-guided resection efficacy. Here, a Near-Infrared II/Photoacoustic Imaging/Magnetic Resonance Imaging (NIR-II/PAI/MRI) organic nanoplatform IRFEP-FA-DOTA-Gd (IFDG) is developed for integrated diagnosis and treatment of orthotopic hepatic tumor. The IFDG is designed rationally based on the core “S-D-A-D-S” NIR-II probe IRFEP modified with folic acid (FA) for active tumor targeting and Gd-DOTA agent for MR imaging. The IFDG exhibits several advantages, including efficient tumor tissue accumulation, good tumor margin imaging effect, and excellent photothermal conversion effect. Therefore, the IFDG could realize accurate long-term monitoring and photothermal therapy non-invasively of the hepatic tumor to reduce its size. Next, the complete resection of the hepatic tumor in situ lesions could be realized by the intraoperative real-time NIR-II imaging guidance. Notably, the preoperative downstaging strategy is confirmed to lower the postoperative recurrence rate of the liver cancer patients under middle and advanced stage effectively with fewer side effects. Overall, the designed nanoplatform demonstrates great potential as a diagnostic-therapeutic integration platform for precise imaging-guided surgical navigation of orthotopic hepatic tumors with a low recurrence rate after surgery, providing a paradigm for diagnosing and treating the advanced tumors in the future clinical translation application.Graphical

HCS-Net: Multi-level deformation strategy combined with quadruple attention for image registration

Background and objectiveNon-rigid image registration plays a significant role in computer-aided diagnosis and surgical navigation for brain diseases. Registration methods that utilize convolutional neural networks (CNNs) have shown excellent accuracy when applied to brain magnetic resonance images (MRI). However, CNNs have limitations in understanding long-range spatial relationships in images, which makes it challenging to incorporate contextual information. And in intricate image registration tasks, it is difficult to achieve a satisfactory dense prediction field, resulting in poor registration performance. MethodsThis paper proposes a multi-level deformable unsupervised registration model that combines Transformer and CNN to achieve non-rigid registration of brain MRI. Firstly, utilizing a dual encoder structure to establish the dependency relationship between the global features of two images and to merge features of varying scales, as well as to preserve the relative spatial position information of feature maps at different scales. Then the proposed multi-level deformation strategy utilizes different deformable fields of varying resolutions generated by the decoding structure to progressively deform the moving image. Ultimately, the proposed quadruple attention module is incorporated into the decoding structure to merge feature information from various directions and emphasize the spatial features in the dominant channels. ResultsThe experimental results on multiple brain MR datasets demonstrate that the promising network could provide accurate registration and is comparable to state-of-the-art methods. ConclusionThe proposed registration model can generate superior deformable fields and achieve more precise registration effects, enhancing the auxiliary role of medical image registration in various fields and advancing the development of computer-aided diagnosis, surgical navigation, and related domains.

Development of an augmented reality guidance system for head and neck cancer resection.

The use of head-mounted augmented reality (AR) for surgeries has grown rapidly in recent years. AR aids in intraoperative surgical navigation through overlaying three-dimensional (3D) holographic reconstructions of medical data. However, performing AR surgeries on complex areas such as the head and neck region poses challenges in terms of accuracy and speed. This study explores the feasibility of an AR guidance system for resections of positive tumour margins in a cadaveric specimen. The authors present an intraoperative solution that enables surgeons to upload and visualize holographic reconstructions of resected cadaver tissues. The solution involves using a 3D scanner to capture detailed scans of the resected tissue, which are subsequently uploaded into our software. The software converts the scans of resected tissues into specimen holograms that are viewable through a head-mounted AR display. By re-aligning these holograms with cadavers with gestures or voice commands, surgeons can navigate the head and neck tumour site. This workflow can run concurrently with frozen section analysis. On average, the authors achieve an uploading time of 2.98min, visualization time of 1.05min, and re-alignment time of 4.39min, compared to the 20 to 30min typical for frozen section analysis. The authors achieve a mean re-alignment error of 3.1mm. The authors' software provides a foundation for new research and product development for using AR to navigate complex 3D anatomy in surgery.

RMAP-ResNet: Segmentation of brain tumor OCT images using residual multicore attention pooling networks for intelligent minimally invasive theranostics

Background and objectiveThe occurrence of brain tumors seriously affects patients both mentally and physically, reducing their quality of life. Automatic recognition and segmentation of tumor boundaries are essential for computer-aided diagnosis and treatment. While optical coherence tomography (OCT) has been widely employed in the detection and analysis fundus lesions and vascular segmentation due to its high resolution and real-time imaging advantages, the segmentation of brain tumor OCT images based on deep neural network has not been studied. However, despite the encoding and decoding structure based on deep learning has achieved good results in medical image segmentation, these models still face limitations in accurately segmenting tumor OCT images due to variations in tumor shapes, sizes, and numbers, and the low contrast of OCT images. Accurate segmentation of tumor tissue contours is crucial as the absence of this mechanism hampers the effective application of OCT imaging methods for brain tumor diagnosis and treatment in clinical practice. Therefore, we propose a novel neural network that can accurately segment brain tumors and their residual tissues in OCT images. MethodsIn this study, we propose a novel residual multi-core attention pooling network (RMAP ResNet) for the segmentation of tumorous and its residual tissue. The RMAP module primarily relies on multiple associative effective receptive fields. These fields enable the detection of targets of various sizes. The module also incorporates an attention structure to adjust connection weights, suppressing irrelevant information and emphasizing the characteristics of tumor OCT images. This process restores the contextual dependencies. ResultsWe validated our proposed RMAP ResNet method on the dataset of mouse tumor OCT imaging obtained using the sweep source OCT imaging system. The experimental results demonstrate that RMAP ResNet achieves superior performance in tumor tissue segmentation. The proposed method improves the quality of tumor segmentation for various shapes and sizes, leading to superior results. RMAP ResNet achieved Dice and IoU values of 94.78% and 90.58%, respectively, confirming the effectiveness and advancement of our proposed technology. ConclusionsOur proposed method addresses the crucial issue of the limited use of the OCT imaging modality in tumor resection surgery. It overcomes the challenge of visually identifying the tumor boundary in OCT modality imaging, where physicians using the naked eye may encounter difficulties. This study has potential applications in the fields of surgical navigation and intelligent minimally invasive treatment of brain tumors.

Status Quo of Surgical Navigation

Surgical navigation, also referred to as computer-assisted or image-guided surgery, is a technique that employs a variety of methods - such as 3D imaging, tracking systems, specialised software, and robotics to support surgeons during surgical interventions. These emerging technologies aim not only to enhance the accuracy and precision of surgical procedures, but also to enable less invasive approaches, with the objective of reducing complications and improving operative outcomes for patients. By harnessing the integration of emerging digital technologies, surgical navigation holds the promise of assisting complex procedures across various medical disciplines. In recent years, the field of surgical navigation has witnessed significant advances. Abdominal surgical navigation, particularly endoscopy, laparoscopic, and robot-assisted surgery, is currently undergoing a phase of rapid evolution. Emphases include image-guided navigation, instrument tracking, and the potential integration of augmented and mixed reality (AR, MR). This article will comprehensively delve into the latest developments in surgical navigation, spanning state-of-the-art intraoperative technologies like hyperspectral and fluorescent imaging, to the integration of preoperative radiological imaging within the intraoperative setting.

Nanomaterials for visualized tumor surgical navigation and postoperative recurrence inhibition

Nanomaterials for visualized tumor surgical navigation and postoperative recurrence inhibition

Surgical Navigation System for Pedicle Screw Placement Based on Mixed Reality

Surgical Navigation System for Pedicle Screw Placement Based on Mixed Reality

Mixed Reality Surgical Navigation System; Positional Accuracy Based on Food and Drug Administration Standard.

Computer assisted surgical navigation systems are designed to improve outcomes by providing clinicians with procedural guidance information. The use of new technologies, such as mixed reality, offers the potential for more intuitive, efficient, and accurate procedural guidance. The goal of this study is to assess the positional accuracy and consistency of a clinical mixed reality system that utilizes commercially available wireless head-mounted displays (HMDs), custom software, and localization instruments. Independent teams using the second-generation Microsoft HoloLens© hardware, Medivis SurgicalAR© software, and localization instruments, tested the accuracy of the combined system at different institutions, times, and locations. The ASTM F2554-18 consensus standard for computer-assisted surgical systems, as recognized by the U.S. FDA, was utilized to measure the performance. 288 tests were performed. The system demonstrated consistent results, with an average accuracy performance that was better than one millimeter (.75 ± SD .37mm). Independently acquired positional tracking accuracies exceed conventional in-market surgical navigation tracking systems and FDA standards. Importantly, the performance was achieved at two different institutions, using an international testing standard, and with a system that included a commercially available off-the-shelf wireless head mounted display and software.

Accuracy of surgical navigation for patient-specific reconstructions of orbital fractures: A systematic review and meta-analysis

This systematic review and meta-analysis aimed to review the recent literature on the technical accuracy of surgical navigation for patient-specific reconstruction of orbital fractures using a patient-specific implant, and to compare surgical navigation with conventional techniques. A systematic literature search was conducted in PubMed (Medline), Embase, Web of Science, and Cochrane (Core Collection) databases on May 16, 2023. Literature comparing surgical navigation with a conventional method using postoperative three-dimensional computed tomography imaging was collected. Only articles that studied at least one of the following outcomes were included: technical accuracy (angular accuracy, linear accuracy, volumetric accuracy, and degree of enophthalmos), preoperative and perioperative times, need for revision, complications, and total cost of the intervention. MINORS criteria were used to evaluate the quality of the articles. After screening 3733 articles, 696 patients from 27 studies were included. A meta-analysis was conducted to evaluate volumetric accuracy and revision rates. Meta-analysis proved a significant better volumetric accuracy (0.93 cm3 ± 0.47 cm3) when surgical navigation was used compared with conventional surgery (2.17 cm3 ± 1.35 cm3). No meta-analysis of linear accuracy, angular accuracy, or enophthalmos was possible due to methodological heterogeneity. Surgical navigation had a revision rate of 4.9%, which was significantly lower than that of the conventional surgery (17%). Costs were increased when surgical navigation was used. Studies with higher MINORS scores demonstrated enhanced volumetric precision compared with traditional approaches. Surgical navigation has proven effective in reducing revision rates compared to conventional approaches, despite increased costs.

Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery

Established surgical navigation systems for pedicle screw placement have been proven to be accurate, but still reveal limitations in registration or surgical guidance. Registration of preoperative data to the intraoperative anatomy remains a time-consuming, error-prone task that includes exposure to harmful radiation. Surgical guidance through conventional displays has well-known drawbacks, as information cannot be presented in-situ and from the surgeon’s perspective. Consequently, radiation-free and more automatic registration methods with subsequent surgeon-centric navigation feedback are desirable. In this work, we present a marker-less approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models, which then is refined for each vertebra individually and updated in real-time with GPU acceleration while handling surgeon occlusions. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system. The registration method was verified on a public dataset with a median of 100% successful registrations, a median target registration error of 2.7 mm, a median screw trajectory error of 1.6°and a median screw entry point error of 2.3 mm. Additionally, the whole pipeline was validated in an ex-vivo surgery, yielding a 100% screw accuracy and a median target registration error of 1.0 mm. Our results meet clinical demands and emphasize the potential of RGB-D data for fully automatic registration approaches in combination with augmented reality guidance.

TMIC-41. LATERAL VENTRICLE PROXIMITY OF GLIOBLASTOMA IS ASSOCIATED WITH LOCALIZED MALIGNANCY-PROMOTING MOLECULAR SIGNATURES IN IMAGE-GUIDED BIOPSIES

Abstract Glioblastoma (GBM) is the most devastating and common form of primary brain cancer in adults. Tumor location plays a significant role in patient prognosis; in particular, GBM tumors contacting the lateral ventricles (LVs) are more aggressive than LV-distal counterparts. This may be due to interaction of tumors with unique features of the LV microenvironment, including the cerebrospinal fluid (CSF) and neural progenitor cells (NPCs) of the subventricular zone. Despite LV contact having a role on tumor malignancy, studies using bulk tumor samples to identify molecular contributors to LV-contacting prognosis have been only moderately successful, in large part due to intratumoral heterogeneity. Here, we leverage intraoperative surgical navigation to collect matched biopsies of adult LV-contacting primary GBM at locations proximal (< 2 cm) and distal ( > 2 cm) from the LV. Matched samples were collected from ten patients with equal sex distribution and an age range of 46 to 81 years. RNA sequencing of biopsies revealed a localized transcriptional signature of gene expression in LV-proximal locations. These genes include those identified to be associated with GBM malignancy and upregulated in primary GBM cells by CSF exposure or co-culture with NPCs, such as SERPINA3, CD44, and CTSB, as well as many newly identified genes. Implicated biological pathways involved in this transcriptional signature involve inflammation, chemotaxis, Notch signaling, angiogenesis, integrin signaling, and more. The top 50 upregulated genes by LV-proximity of GBM were used to cluster patient transcriptomes from the TCGA GBM database and evaluate the contribution to patient outcome. This analysis revealed a cluster with significantly extended overall survival that displayed a general downregulation of LV-proximity genes. Ultimately, these studies provide evidence that regional tumoral analysis is essential in identifying molecular signatures associated with microenvironmental influence, and that LV-contacting GBM has regional gene expression changes in many genes associated with malignancy.

Augmented and Virtual Reality Applications in Facial Plastic Surgery: A Scoping Review.

Augmented reality (AR) and virtual reality (VR) are emerging technologies with wide potential applications in health care. We performed a scoping review of the current literature on the application of augmented and VR in the field of facial plastic and reconstructive surgery (FPRS). PubMed and Web of Science. According to PRISMA guidelines, PubMed and Web of Science were used to perform a scoping review of literature regarding the utilization of AR and/or VR relevant to FPRS. Fifty-eight articles spanning 1997-2023 met the criteria for review. Five overarching categories of AR and/or VR applications were identified across the articles: preoperative, intraoperative, training/education, feasibility, and technical. The following clinical areas were identified: burn, craniomaxillofacial surgery (CMF), face transplant, face lift, facial analysis, facial palsy, free flaps, head and neck surgery, injectables, locoregional flaps, mandible reconstruction, mandibuloplasty, microtia, skin cancer, oculoplastic surgery, rhinology, rhinoplasty, and trauma. AR and VR have broad applications in FPRS. AR for surgical navigation may have the most emerging potential in CMF surgery and free flap harvest. VR is useful as distraction analgesia for patients and as an immersive training tool for surgeons. More data on these technologies' direct impact on objective clinical outcomes are still needed. N/A Laryngoscope, 134:2568-2577, 2024.

Evaluation of Augmented Reality Surgical Navigation in Percutaneous Endoscopic Lumbar Discectomy: Clinical Study.

The puncture procedure in percutaneous endoscopic lumbar discectomy (PELD) is non-visual, and the learning curve for PELD is steep. An augmented reality surgical navigation (ARSN) system was designed and utilized in PELD. The system possesses three core functionalities: augmented reality (AR) radiograph overlay, AR puncture needle real-time tracking, and AR navigation. We conducted a prospective randomized controlled trial to evaluate its feasibility and effectiveness. A total of 20 patients with lumbar disc herniation treated with PELD were analyzed. Of these, 10 patients were treated with the guidance of ARSN (ARSN group). The remaining 10 patients were treated using C-arm fluoroscopy guidance (control group). The AR radiographs and AR puncture needle were successfully superimposed on the intraoperative videos. The anteroposterior and lateral AR tracking distance errors were 1.55 ± 0.17 mm and 1.78 ± 0.21 mm. The ARSN group exhibited a significant reduction in both the number of puncture attempts (2.0 ± 0.4 vs. 6.9 ± 0.5, p = 0.000) and the number of fluoroscopies (10.6 ± 0.9 vs. 18.5 ± 1.6, p = 0.000) compared with the control group. Complications were not observed in either group. The results indicate that the clinical application of the ARSN system in PELD is effective and feasible.

J-Aggregation Strategy toward Potentiated NIR-II Fluorescence Bioimaging of Molecular Fluorophores.

Molecular fluorophores emitting in the second near-infrared (NIR-II, 1000-1700 nm) window with strong optical harvesting and high quantum yields hold great potential for in vivo deep-tissue bioimaging and high-resolution biosensing. Recently, J-aggregates are harnessed to engineer long-wavelength NIR-II emitters and show unique superiority in tumor detection, vessel mapping, surgical navigation, and phototheranostics due to their bathochromic-shifted optical bands in the required slip-stacked arrangement aggregation state. However, despite the preliminary progress of NIR-II J-aggregates and theoretical study of structure-property relationships, further paradigms of NIR-II J-aggregates remain scarce due to the lack of study on aggregated fluorophores with slip-stacked fashion. In this effort, how to utilize the specific molecular structure to form slip-stacked packing motifs with J-type aggregated exciton coupling is emphatically elucidated. First, several molecular regulating strategies to achieve NIR-II J-aggregates containing intermolecular interactions and external conditions are positively summarized and deeply analyzed. Then, the recent reports on J-aggregates for NIR-II bioimaging and theranostics are systematically summarized to provide a clear reference and direction for promoting the development of NIR-II organic fluorophores. Eventually, the prospective efforts on ameliorating and promoting NIR-II J-aggregates to further clinical practices are outlined.

ChatGPT's contributions to the evolution of neurosurgical practice and education: a systematic review of benefits, concerns and limitations

<p><strong>Aim</strong> <br />This study provides a comprehensive review of the current literature on the use of ChatGPT, a generative Artificial Intelligence (AI) tool, in neurosurgery. The study examines potential benefits and limitations of ChatGPT in neurosurgical practice and education.<br /><strong>Methods <br /></strong>The study involved a systematic review of the current literature on the use of AI in neurosurgery, with a focus on<br />ChatGPT. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to ensure a comprehensive and transparent review process. Thirteen studies met the inclusion criteria and were included in the final analysis. The data extracted from the included studies were analysed and synthesized to provide an overview of the current state of research on the use of ChatGPT in neurosurgery.<br /><strong>Results</strong> <br />The ChatGPT showed a potential to complement and enhance neurosurgical practice. However, there are risks and limitations associated with its use, including question format limitations, validation challenges, and algorithmic bias. The study highlights the importance of validating machine-generated content for accuracy and addressing ethical concerns associated with AI technologies. The study also identifies potential benefits of ChatGPT, such as providing personalized treatment plans, supporting surgical planning and navigation, and enhancing large data processing efficiency and accuracy.<br /><strong>Conclusion</strong> <br />The integration of AI technologies into neurosurgery should be approached with caution and careful consideration of<br />ethical and validation issues. Continued research and development of AI tools in neurosurgery can help us further understand their potential benefits and limitations. </p>

Dimensionality Reduction Hybrid U-Net for Brain Extraction in Magnetic Resonance Imaging.

In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies.

AUGMENTED REALITY-BASED NASAL ENDOSCOPE VIDEO RECONSTRUCTION AND REGISTRATION

Neuroendoscopic surgery is a minimally invasive surgical technique commonly used to remove a tumor through the patient’s mouth or nose, which requires the surgeon to avoid important neural and vascular structures. Augmented reality-based surgical navigation can provide surgeons with more information and visual aids, clarifying the structure and details of the surgical area. It requires real-time registration of intraoperative endoscopic video with pre-operative CT models. However, 3D reconstruction and camera tracking from nasal endoscopic video are challenging due to the narrow nasal cavity and the lack of texture. Besides, the nasal endoscopic datasets that can be used for deep learning-based depth estimation are scarce and hard to elaborate. To this end, a style-augmented module (SAM) is proposed in this study to minimize discrepancies between different endoscopic datasets. An unsupervised approach was trained to generate depth maps and camera motion paths, which were used to reconstruct the nasal scene’s 3D model. The obtained reconstruction was aligned with a pre-operative CT model for augmented reality-based surgical navigation. The results showed that the proposed SAM improved the generalization of the depth estimation model on nasal endoscopy data. The proposed approach to combining augmented reality technology with surgical navigation is considered instrumental in furnishing surgeons with richer information on the nasal endoscopic surgical scene.

Validation of mixed-reality surgical navigation for glenoid axis pin placement in shoulder arthroplasty using a cadaveric model

Mixed reality may offer an alternative for computer-assisted navigation in shoulder arthroplasty. The purpose of this study was to determine the accuracy and precision of mixed-reality guidance for the placement of the glenoid axis pin in cadaver specimens. This step is essential for accurate glenoid placement in total shoulder arthroplasty. Fourteen cadaveric shoulders underwent simulated shoulder replacement surgery by 7 experienced shoulder surgeons. The surgeons exposed the cadavers through a deltopectoral approach and then used mixed-reality surgical navigation to insert a guide pin in a preplanned position and trajectory in the glenoid. The mixed-reality system used the Microsoft Hololens 2 headset, navigation software, dedicated instruments with fiducial marker cubes, and a securing pin. Computed tomography scans obtained before and after the procedure were used to plan the surgeries and determine the difference between the planned and executed values for the entry point, version, and inclination. One specimen had to be discarded from the analysis because the guide pin was removed accidentally before obtaining the postprocedure computed tomography scan. Regarding the navigated entry point on the glenoid, the mean difference between planned and executed values was 1.7 ± 0.8 mm; this difference was 1.2 ± 0.6 mm in the superior-inferior direction and 0.9 ± 0.8 mm in the anterior-posterior direction. The maximum deviation from the entry point for all 13 specimens analyzed was 3.1 mm. Regarding version, the mean difference between planned and executed version values was 1.6° ± 1.2°, with a maximum deviation in version for all 13 specimens of 4.1°. Regarding inclination, the mean angular difference was 1.7° ± 1.5°, with a maximum deviation in inclination of 5°. The mixed-reality navigation system used in this study allowed surgeons to insert the glenoid guide pin on average within 2 mm from the planned entry point and within 2° of version and inclination. The navigated values did not exceed 3 mm or 5°, respectively, for any of the specimens analyzed. This approach may help surgeons more accurately place the definitive glenoid component.

Feasibility Analysis of the Application of Automatic Intelligent Surgical Robots in Nasal Surgery

This paper discusses the feasibility of otolaryngology surgery to achieve automatic intelligent surgery from the current situation, professional characteristics, anatomical structure characteristics of head and neck, combined with the development of nasal endoscopy, minimally invasive surgical navigation, surgical robot technology and artificial intelligence.

Assessing the Accuracy of Spinal Instrumentation Using Augmented Reality (AR): A Systematic Review of the Literature and Meta-Analysis.

Technological advancements, particularly in the realm of augmented reality (AR), may facilitate more accurate and precise pedicle screw placement. AR integrates virtual data into the operator's real-world view, allowing for the visualization of patient-specific anatomy and navigated trajectories. We aimed to conduct a meta-analysis of the accuracy of pedicle screw placement using AR-based systems. A systematic review of the literature and meta-analysis was performed using the PubMed/MEDLINE database, including studies reporting the accuracy of pedicle screw placement using AR. In total, 8 studies with 163 patients and 1259 screws were included in the analysis. XVision (XVS) was the most commonly used AR system (595 screws) followed by the Allura AR surgical navigation system (ARSN) (462 screws). The overall accuracy was calculated as 97.2% (95% CI 96.2-98.1% p < 0.001). Subgroup analysis revealed that there was no statistically significant difference in the accuracy rates achieved by XVS and Allura ARSN (p = 0.092). AR enables reliable, accurate placement of spinal instrumentation. Future research efforts should focus on comparative studies, cost effectiveness, operative time, and radiation exposure.