Improved Accuracy and Lowered Learning Curve of Ventricular Targeting Using Augmented Reality-Phantom and Cadaveric Model Testing

Augmented reality for basic skills training in laparoscopic surgery: a systematic review and meta-analysis

BACKGROUND

Augmented reality (AR) has emerged as a transformative technology in medical education, particularly in training basic laparoscopic skills. Despite its growing applications, the effectiveness of AR in this specific domain remains underexplored, with a lack of standardized assessment frameworks and inconsistent methodologies across studies. This systematic review and meta-analysis aimed to evaluate the effectiveness of AR in laparoscopic basic skills training for medical students and junior physicians.

METHODS

We conducted a systematic review and meta-analysis following PRISMA guidelines. Databases searched included PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov. Studies were selected based on their focus on AR applications in laparoscopic training, involving both randomized controlled trials and non-randomized studies. Inclusion criteria focused on medical students and novice surgeons, assessing educational outcomes such as Global Operative Assessment of Laparoscopic Skills (GOALS) Global, Objective Structured Assessment of Technical Skills (OSATS) Global, OSATS Specific, Training Time, and Subjective Workload.

RESULTS

A total of 12 studies involving 434 participants met the inclusion criteria. The analysis revealed that AR technology significantly improved educational outcomes, with participants achieving higher GOALS and OSATS scores. Specifically, the mean difference for GOALS scores was 2.40 points (95% CI [1.30, 3.50], p < 0.001) and for OSATS scores, 7.71 points (95% CI [3.39, 12.03], p < 0.001). Additionally, AR-assisted training showed a reduction in subjective workload, with a mean decrease of 2.95 points (95% CI [- 4.95, - 0.95], p = 0.003).

CONCLUSIONS

The findings indicate that AR significantly enhances laparoscopic training outcomes, facilitating improved technical skills, efficiency, and learner independence. However, variability in study designs and outcomes limits generalizability. Future research should focus on standardize AR training protocols and evaluate long-term effectiveness to fully leverage AR's potential in surgical education.

Virtual and Augmented Reality in Ventriculostomy: A Systematic Review

BACKGROUND

Ventriculostomy, one of the most common neurosurgical procedures, involves inserting a draining catheter into the brain's ventricular system to alleviate excessive cerebrospinal fluid accumulation. Traditionally, this procedure has relied on freehand techniques guided by anatomical landmarks, which have shown a high rate of misplacement. Recent advancements in virtual reality (VR) and augmented reality (AR) technologies have opened up new possibilities in the field. This comprehensive review aims to analyze the existing literature, examine the diverse applications of VR and AR in ventriculostomy procedures, address their limitations, and propose potential future directions.

METHODS

A systematic search was conducted in Web of Science and PubMed databases to identify studies employing VR and AR technologies in ventriculostomy procedures. Review papers, non-English records, studies unrelated to VR/AR technologies in ventriculostomy, and supplementary documents were excluded. In total 29 papers were included in the review.

RESULTS

The development of various VR and AR systems aimed at enhancing the ventriculostomy procedure are categorized according to the Data, Visualization and View taxonomy. The study investigates the data utilized by these systems, the visualizations employed, and the virtual or augmented environments created. Furthermore, the surgical scenarios and applications of each method, as well as the validation and evaluation metrics used, are discussed.

DISCUSSION

The review delves into the fundamental challenges encountered in the implementation of VR and AR systems in ventriculostomy. Additionally, potential future directions and areas for improvement are proposed, addressing the identified limitations and paving the way for further advancements in the field.

Augmented Reality-Guided External Ventricular Drain Placement: A Case Report

The placement of an external ventricular drain (EVD) is a critical neurosurgical procedure used to relieve intracranial pressure in patients with conditions such as hydrocephalus, traumatic brain injury, and intracranial hemorrhage. Traditional methods rely heavily on anatomical landmarks and the surgeon's experience, which can lead to variability in outcomes and increased risk of complications. Neuronavigation, while available, is infrequently used due to the size, cost, and set-up times associated with these devices. This report explores the use of a headset-based augmented reality (AR) system for guidance during the EVD placement procedure. We describe an AR system that overlays a 3D model of the patient's cranial anatomy, derived from preoperative imaging, onto the patient's head. This system is a head-mounted display and utilizes a rapid fiducial-less registration to provide the surgeon with visualization of 3D anatomy, and targeted trajectories. The system was used with a 32-year-old patient undergoing EVD placement prior to a cranioplasty. Due to the atypical cranial anatomy and due to prior procedures and midline shift, this relatively high-risk catheter placement was an ideal circumstance for the use of AR guidance during the EVD placement. This report described an early use of AR for EVD placement and represents a substantial advancement in neurosurgical practice, offering enhanced precision, efficiency, and safety. Further large-scale studies are warranted to validate these findings and explore the broader applicability of AR in other neurosurgical procedures.

Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies

INTRODUCTION

The integration of augmented reality (AR) in spine surgery marks a significant advancement, enhancing surgical precision and patient outcomes. AR provides immersive, three-dimensional visualizations of anatomical structures, facilitating meticulous planning and execution of spine surgeries. This technology not only improves spatial understanding and real-time navigation during procedures but also aims to reduce surgical invasiveness and operative times. Despite its potential, challenges such as model accuracy, user interface design, and the learning curve for new technology must be addressed. AR's application extends beyond the operating room, offering valuable tools for medical education and improving patient communication and satisfaction.

MATERIAL AND METHODS

A literature review was conducted by searching PubMed and Scopus databases using keywords related to augmented reality in spine surgery, covering publications from January 2020 to January 2024.

RESULTS

In total, 319 articles were identified through the initial search of the databases. After screening titles and abstracts, 11 articles in total were included in the qualitative synthesis.

CONCLUSION

Augmented reality (AR) is becoming a transformative force in spine surgery, enhancing precision, education, and outcomes despite hurdles like technical limitations and integration challenges. AR's immersive visualizations and educational innovations, coupled with its potential synergy with AI and machine learning, indicate a bright future for surgical care. Despite the existing obstacles, AR's impact on improving surgical accuracy and safety marks a significant leap forward in patient treatment and care.

Intraoperative Augmented Reality for Complex Glioma Resection: A Case Report

Augmented reality (AR) is an emerging technology that can display three-dimensional patient anatomy in the surgeons' field of view. The use of this technology has grown considerably for both presurgical and intraoperative guidance. A patient diagnosed with breast cancer started to experience numbness in the left hand, which progressed to weakness in the left hand and arm. An MRI was performed demonstrating a 2.9 cm X 1.8 cm lesion with extensive surrounding edema in the posterior fronto-parietal lobes. Surgery was recommended for presumed metastatic disease. Preoperatively, an AR system and Brainlab navigation were registered to the patient. AR, traditional navigation, and ultrasound were all used to localize the lesion and determine the craniotomy site and size. The tumor was removed along the direction of the lesion. Intraoperatively, we used AR to reexamine the tumor details and could appreciate that we had to redirect our surgical trajectory anteriorly and laterally in order to follow along the main axis of the tumor. In doing this, we were able to more confidently remain with the tumor, which by this time was poorly defined by 2D navigation and by direct vision. Postoperative MRI confirmed gross total removal of the tumor. The patient had an uneventful postoperative course with resolution of preoperative symptoms and the final surgical pathology was grade 4 glioblastoma. Here, we describe the valuable use of AR for the resection of a glioma. The system has a seamless registration process and provides the surgeon with a unique view of 3D anatomy overlaid onto the patient's head. This exciting technology can add tremendous value to complex cranial surgeries.

NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans

PURPOSE

This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA).

METHODS

We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared.

RESULTS

The nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945-0.979], skin: 0.997 [95CI: 0.984-0.999], tumor: 0.926 [95CI: 0.508-0.968], and ventricles: 0.910 [95CI: 0.812-0.968]. Compared to the MGA's median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000-0.926], and ventricles: 0.856 [95CI: 0.216-0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0-1616]) than the MGA (mean: 2851 s [95CI: 1482-6246]).

CONCLUSIONS

The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.

Effects of Augmented Reality on Thoracolumbar Pedicle Screw Instrumentation Across Different Levels of Surgical Experience

OBJECTIVE

Augmented reality (AR) is an emerging technology that may accelerate skill acquisition and improve accuracy of thoracolumbar pedicle screw placements. We aimed to quantify the relative assistance of AR compared with freehand (FH) pedicle screw accuracy across different surgical experience levels.

METHODS

A spine fellowship-trained and board-certified attending neurosurgeon, postgraduate year 4 neurosurgery resident, and second-year medical student placed 32 FH and 32 AR-assisted thoracolumbar pedicle screws in 3 cadavers. A cableless, voice-activated AR system was paired with a headset. Accuracy was assessed using χ2 analysis and the Gertzbein-Robbins scale. Angular error, distance error, and time per pedicle screw were collected and compared.

RESULTS

The attending neurosurgeon had 91.6% (11/12) clinically acceptable (Gertzbein-Robbins scale A or B) insertion in both FH and AR groups; the resident neurosurgeon had 100% (9/9) FH and AR in both cases; the medical student had 72.3% (8/11) FH accuracy and 81.8% (9/11) AR accuracy. The medical student displayed significantly lower ideal (Gertzbein-Robbins scale A) FH accuracy compared with the resident neurosurgeon (P = 0.017) and attending neurosurgeon (P = 0.005), but no difference when using AR. FH screw placement was faster by both the attending neurosurgeon (median 46 seconds vs. 94.5 seconds, P = 0.0047) and the neurosurgery resident neurosurgeon (median 144 seconds vs. 140 seconds, P = 0.05). Total clinically acceptable AR and FH accuracy was 90.6% (29/32) and 87.5% (28/32), respectively (P = 0.69).

CONCLUSIONS

AR screw placement allowed an inexperienced medical student to double their accuracy in 1 training session. With subsequent iterations, this promising technology could serve as an important tool for surgical training.

Augmented reality for intracranial meningioma resection: a mini-review

Augmented reality (AR) integrates computer-generated content and real-world scenarios. Artificial intelligence's continuous development has allowed AR to be integrated into medicine. Neurosurgery has progressively introduced image-guided technologies. Integration of AR into the operating room has permitted a new perception of neurosurgical diseases, not only for neurosurgical planning, patient positioning, and incision design but also for intraoperative maneuvering and identification of critical neurovascular structures and tumor boundaries. Implementing AR, virtual reality, and mixed reality has introduced neurosurgeons into a new era of artificial interfaces. Meningiomas are the most frequent primary benign tumors commonly related to paramount neurovascular structures and bone landmarks. Integration of preoperative 3D reconstructions used for surgical planning into AR can now be inserted into the microsurgical field, injecting information into head-up displays and microscopes with integrated head-up displays, aiming to guide neurosurgeons intraoperatively to prevent potential injuries. This manuscript aims to provide a mini-review of the usage of AR for intracranial meningioma resection.