A Low-Cost iPhone-Assisted Augmented Reality Solution for the Localization of Intracranial Lesions

Evaluation Metrics for Augmented Reality in Neurosurgical Preoperative Planning, Surgical Navigation, and Surgical Treatment Guidance: A Systematic Review

BACKGROUND AND OBJECTIVE

Recent years have shown an advancement in the development of augmented reality (AR) technologies for preoperative visualization, surgical navigation, and intraoperative guidance for neurosurgery. However, proving added value for AR in clinical practice is challenging, partly because of a lack of standardized evaluation metrics. We performed a systematic review to provide an overview of the reported evaluation metrics for AR technologies in neurosurgical practice and to establish a foundation for assessment and comparison of such technologies.

METHODS

PubMed, Embase, and Cochrane were searched systematically for publications on assessment of AR for cranial neurosurgery on September 22, 2022. The findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

RESULTS

The systematic search yielded 830 publications; 114 were screened full text, and 80 were included for analysis. Among the included studies, 5% dealt with preoperative visualization using AR, with user perception as the most frequently reported metric. The majority (75%) researched AR technology for surgical navigation, with registration accuracy, clinical outcome, and time measurements as the most frequently reported metrics. In addition, 20% studied the use of AR for intraoperative guidance, with registration accuracy, task outcome, and user perception as the most frequently reported metrics.

CONCLUSION

For quality benchmarking of AR technologies in neurosurgery, evaluation metrics should be specific to the risk profile and clinical objectives of the technology. A key focus should be on using validated questionnaires to assess user perception; ensuring clear and unambiguous reporting of registration accuracy, precision, robustness, and system stability; and accurately measuring task performance in clinical studies. We provided an overview suggesting which evaluation metrics to use per AR application and innovation phase, aiming to improve the assessment of added value of AR for neurosurgical practice and to facilitate the integration in the clinical workflow.

Deep Neural Network-Based Visual Feedback System for Nasopharyngeal Swab Sampling

During the 2019 coronavirus disease pandemic, robotic-based systems for swab sampling were developed to reduce burdens on healthcare workers and their risk of infection. Teleoperated sampling systems are especially appreciated as they fundamentally prevent contact with suspected COVID-19 patients. However, the limited field of view of the installed cameras prevents the operator from recognizing the position and deformation of the swab inserted into the nasal cavity, which highly decreases the operating performance. To overcome this limitation, this study proposes a visual feedback system that monitors and reconstructs the shape of an NP swab using augmented reality (AR). The sampling device contained three load cells and measured the interaction force applied to the swab, while the shape information was captured using a motion-tracking program. These datasets were used to train a one-dimensional convolution neural network (1DCNN) model, which estimated the coordinates of three feature points of the swab in 2D X-Y plane. Based on these points, the virtual shape of the swab, reflecting the curvature of the actual one, was reconstructed and overlaid on the visual display. The accuracy of the 1DCNN model was evaluated on a 2D plane under ten different bending conditions. The results demonstrate that the x-values of the predicted points show errors of under 0.590 mm from P0, while those of P1 and P2 show a biased error of about -1.5 mm with constant standard deviations. For the y-values, the error of all feature points under positive bending is uniformly estimated with under 1 mm of difference, when the error under negative bending increases depending on the amount of deformation. Finally, experiments using a collaborative robot validate its ability to visualize the actual swab's position and deformation on the camera image of 2D and 3D phantoms.

Intra-operative applications of augmented reality in glioma surgery: a systematic review

Background

Augmented reality (AR) is increasingly being explored in neurosurgical practice. By visualizing patient-specific, three-dimensional (3D) models in real time, surgeons can improve their spatial understanding of complex anatomy and pathology, thereby optimizing intra-operative navigation, localization, and resection. Here, we aimed to capture applications of AR in glioma surgery, their current status and future potential.

Methods

A systematic review of the literature was conducted. This adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Embase, and Scopus electronic databases were queried from inception to October 10, 2022. Leveraging the Population, Intervention, Comparison, Outcomes, and Study design (PICOS) framework, study eligibility was evaluated in the qualitative synthesis. Data regarding AR workflow, surgical application, and associated outcomes were then extracted. The quality of evidence was additionally examined, using hierarchical classes of evidence in neurosurgery.

Results

The search returned 77 articles. Forty were subject to title and abstract screening, while 25 proceeded to full text screening. Of these, 22 articles met eligibility criteria and were included in the final review. During abstraction, studies were classified as "development" or "intervention" based on primary aims. Overall, AR was qualitatively advantageous, due to enhanced visualization of gliomas and critical structures, frequently aiding in maximal safe resection. Non-rigid applications were also useful in disclosing and compensating for intra-operative brain shift. Irrespective, there was high variance in registration methods and measurements, which considerably impacted projection accuracy. Most studies were of low-level evidence, yielding heterogeneous results.

Conclusions

AR has increasing potential for glioma surgery, with capacity to positively influence the onco-functional balance. However, technical and design limitations are readily apparent. The field must consider the importance of consistency and replicability, as well as the level of evidence, to effectively converge on standard approaches that maximize patient benefit.

Optical Surgical Navigation: A Promising Low-cost Alternative. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38082918 DOI: 10.1109/embc40787.2023.10340384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

State-of-the-art computer-assisted surgery relies on infrared-based cameras for precise positional measurements. However, the cost of purchasing these systems acts as a barrier for smaller healthcare facilities to adopt them. Recently, low-cost optical tracking with cameras has emerged as a promising alternative, but differences in operating room conditions and patient anatomy can cause inconsistencies between procedures. Therefore, it is essential to identify and evaluate individual factors that may affect a procedure. In this study, we evaluate fiducial ArUco markers as a low-cost alternative to traditional markers. To evaluate their effectiveness, we designed a ground truth testing platform, which enables us to measure the real-time difference between the predicted and actual positions. We investigated the effects of warping, line-of-sight obstruction, and operating room lighting as variables that could influence marker tracking in the operating room. Each variable was isolated and simplified to quantifiable modifications to the physical marker and X-Y platform environment. We find that our navigation system is a promising approach for use in computer-navigated surgery, and future work will focus on implementing image processing techniques to improve the accuracy of optical marker tracking.

A Tool-free Neuronavigation Method based on Single-view Hand Tracking

This work presents a novel tool-free neuronavigation method that can be used with a single RGB commodity camera. Compared with freehand craniotomy placement methods, the proposed system is more intuitive and less error prone. The proposed method also has several advantages over standard neuronavigation platforms. First, it has a much lower cost, since it doesn't require the use of an optical tracking camera or electromagnetic field generator, which are typically the most expensive parts of a neuronavigation system, making it much more accessible. Second, it requires minimal setup, meaning that it can be performed at the bedside and in circumstances where using a standard neuronavigation system is impractical. Our system relies on machine-learning-based hand pose estimation that acts as a proxy for optical tool tracking, enabling a 3D-3D pre-operative to intra-operative registration. Qualitative assessment from clinical users showed that the concept is clinically relevant. Quantitative assessment showed that on average a target registration error (TRE) of 1.3cm can be achieved. Furthermore, the system is framework-agnostic, meaning that future improvements to hand-tracking frameworks would directly translate to a higher accuracy.

A Neuronavigation System Using a Mobile Augmented Reality Solution

BACKGROUND

Image-guided surgery has shown great utility in neurosurgery, especially in allowing for more accurate surgical planning and navigation. The current gold standard for image-guided neurosurgery is neuronavigation, which provides millimetric accuracy on such tasks. However, these approaches often require a complicated setup and have high cost, hindering their potential in low- and middle-income countries. The aim of this study was to develop and evaluate the performance of a mobile-based augmented reality neuronavigation solution under different conditions in a preclinical environment.

METHODS

The application was developed using the Swift programming language and was tested on a replica of a human scalp under variable lighting, with different numbers of registration points and target point position conditions. For each condition, reference points were input into the application, and the target points were computed for 10 iterations. The mean registration error and target error were used to assess the performance of the application.

RESULTS

In the best-case scenario, the proposed solution had a mean target error of 2.6 ± 1.6 mm.

CONCLUSIONS

Our approach provides a viable, low-cost, easy-to-use, portable method for locating points on the scalp surface with an accuracy of 2.6 ± 1.6 mm in the best-case scenario.

Augmented Reality Surgical Navigation System for External Ventricular Drain

Augmented reality surgery systems are playing an increasing role in the operating room, but applying such systems to neurosurgery presents particular challenges. In addition to using augmented reality technology to display the position of the surgical target position in 3D in real time, the application must also display the scalpel entry point and scalpel orientation, with accurate superposition on the patient. To improve the intuitiveness, efficiency, and accuracy of extra-ventricular drain surgery, this paper proposes an augmented reality surgical navigation system which accurately superimposes the surgical target position, scalpel entry point, and scalpel direction on a patient’s head and displays this data on a tablet. The accuracy of the optical measurement system (NDI Polaris Vicra) was first independently tested, and then complemented by the design of functions to help the surgeon quickly identify the surgical target position and determine the preferred entry point. A tablet PC was used to display the superimposed images of the surgical target, entry point, and scalpel on top of the patient, allowing for correct scalpel orientation. Digital imaging and communications in medicine (DICOM) results for the patient’s computed tomography were used to create a phantom and its associated AR model. This model was then imported into the application, which was then executed on the tablet. In the preoperative phase, the technician first spent 5–7 min to superimpose the virtual image of the head and the scalpel. The surgeon then took 2 min to identify the intended target position and entry point position on the tablet, which then dynamically displayed the superimposed image of the head, target position, entry point position, and scalpel (including the scalpel tip and scalpel orientation). Multiple experiments were successfully conducted on the phantom, along with six practical trials of clinical neurosurgical EVD. In the 2D-plane-superposition model, the optical measurement system (NDI Polaris Vicra) provided highly accurate visualization (2.01 ± 1.12 mm). In hospital-based clinical trials, the average technician preparation time was 6 min, while the surgeon required an average of 3.5 min to set the target and entry-point positions and accurately overlay the orientation with an NDI surgical stick. In the preparation phase, the average time required for the DICOM-formatted image processing and program import was 120 ± 30 min. The accuracy of the designed augmented reality optical surgical navigation system met clinical requirements, and can provide a visual and intuitive guide for neurosurgeons. The surgeon can use the tablet application to obtain real-time DICOM-formatted images of the patient, change the position of the surgical entry point, and instantly obtain an updated surgical path and surgical angle. The proposed design can be used as the basis for various augmented reality brain surgery navigation systems in the future.

Application of computer-aided image reconstruction and image guide in parasagittal meningioma resection

Background

In recent years, smaller-sized (diameter < 2.5 cm) meningiomas are diagnosed due to increased cranial imaging. Symptomatic meningiomas need to be removed surgically. Therefore, it is extremely important to locate the lesion exactly to tailor the craniotomy especially if the neuro-navigation system is not available. Many hospitals located in the underdeveloped countries cannot afford the high costs of neuro-navigation equipment. Hence, it is relevant to discover low-cost associated and effective methods for lesion localization for surgery.

Methods

The use of localization markers in advance can help to acquire preoperative CT images of the patients to create and calculate a three-dimensional (3D) virtual graph using a computer. With the 3D graph, spatial distance of the tumor from the markers is calculated and the tumor location projected on the scalp by the Triangle Pythagorean theorem. This enables precise localization of intracranial microlesions preoperatively.

Results

The location of the tumor was consistent with that of the pre-operative virtual image, and the craniotomy was exact. The patient was discharged 3 days later without any neurological deficits.

Conclusions

This method is simple and reliable, inexpensive, and accurate in the location of small-sized lesions, which can partially compensate for the lack of neuro-navigation and is suitable for widespread application in hospitals in developing countries.

Real-time augmented reality application in presurgical planning and lesion scalp localization by a smartphone

OBJECTIVE

A smartphone augmented reality (AR) application (app) was explored for clinical use in presurgical planning and lesion scalp localization.

METHODS

We programmed an AR App on a smartphone. The accuracy of the AR app was tested on a 3D-printed head model, using the Euclidean distance of displacement of virtual objects. For clinical validation, 14 patients with brain tumors were included in the study. Preoperative MRI images were used to generate 3D models for AR contents. The 3D models were then transferred to the smartphone AR app. Tumor scalp localization was marked, and a surgical corridor was planned on the patient's head by viewing AR images on the smartphone screen. Standard neuronavigation was applied to evaluate the accuracy of the smartphone. Max-margin distance (MMD) and area overlap ratio (AOR) were measured to quantitatively validate the clinical accuracy of the smartphone AR technique.

RESULTS

In model validation, the total mean Euclidean distance of virtual object displacement using the smartphone AR app was 4.7 ± 2.3 mm. In clinical validation, the mean duration of AR app usage was 168.5 ± 73.9 s. The total mean MMD was 6.7 ± 3.7 mm, and total mean AOR was 79%.

CONCLUSIONS

The smartphone AR app provides a new way of experience to observe intracranial anatomy in situ, and it makes surgical planning more intuitive and efficient. Localization accuracy is satisfactory with lesions larger than 15 mm.

Navigation of frameless fixation for gamma knife radiosurgery using fixed augmented reality

Augmented reality (AR) offers a new medical treatment approach. We aimed to evaluate frameless (mask) fixation navigation using a 3D-printed patient model with fixed-AR technology for gamma knife radiosurgery (GKRS). Fixed-AR navigation was developed using the inside-out method with visual inertial odometry algorithms, and the flexible Quick Response marker was created for object-feature recognition. Virtual 3D-patient models for AR-rendering were created via 3D-scanning utilizing TrueDepth and cone-beam computed tomography (CBCT) to generate a new GammaKnife Icon™ model. A 3D-printed patient model included fiducial markers, and virtual 3D-patient models were used to validate registration accuracy. Registration accuracy between initial frameless fixation and re-fixation navigated fixed-AR was validated through visualization and quantitative method. The quantitative method was validated through set-up errors, fiducial marker coordinates, and high-definition motion management (HDMM) values. A 3D-printed model and virtual models were correctly overlapped under frameless fixation. Virtual models from both 3D-scanning and CBCT were enough to tolerate the navigated frameless re-fixation. Although the CBCT virtual model consistently delivered more accurate results, 3D-scanning was sufficient. Frameless re-fixation accuracy navigated in virtual models had mean set-up errors within 1 mm and 1.5° in all axes. Mean fiducial marker differences from coordinates in virtual models were within 2.5 mm in all axes, and mean 3D errors were within 3 mm. Mean HDMM difference values in virtual models were within 1.5 mm of initial HDMM values. The variability from navigation fixed-AR is enough to consider repositioning frameless fixation without CBCT scanning for treating patients fractionated with large multiple metastases lesions (> 3 cm) who have difficulty enduring long beam-on time. This system could be applied to novel GKRS navigation for frameless fixation with reduced preparation time.

Multi-Modality guidance based surgical navigation for percutaneous endoscopic transforaminal discectomy

OBJECTIVE

Fluoroscopic guidance is a critical step for the puncture procedure in percutaneous endoscopic transforaminal discectomy (PETD). However, two-dimensional observations of the three-dimensional anatomic structure suffer from the effects of projective simplification. To accurately assess the spatial relations between the patient vertebra tissues and puncture needle, a considerable number of fluoroscopic images from different orientations need to be acquired by the surgeons. This process significantly increases the radiation risk for both the patient and surgeons.

METHODS

In this paper, we propose an augmented reality (AR) surgical navigation system for PETD based on multi-modality information, which contains fluoroscopy, optical tracking, and depth camera. To register the fluoroscopic image with the intraoperative video, we design a lightweight non-invasive fiducial with markers and detect the markers based on the deep learning method. It can display the intraoperative video fused with the registered fluoroscopic images. We also present a self-adaptive calibration and transformation method between a 6-DOF optical tracking device and a depth camera, which are in different coordinate systems.

RESULTS

With the substantially reduced frequency of fluoroscopy imaging, the system can accurately track and superimpose the virtual puncture needle on fluoroscopy images in real-time. From operating theatre in vivo animal experiments, the results illustrate that the system average positioning accuracy can reach 1.98mm and the orientation accuracy can reach 1.19^∘. From the clinical validation results, the system significantly lower the frequency of fluoroscopy imaging (42.7%) and reduce the radiation risk for both the patient and surgeons.

CONCLUSION

Coupled with the user study, both the quantitative and qualitative results indicate that our navigation system has the potential to be highly useful in clinical practice. Compared with the existing navigation systems, which are usually equipped with a variety of large and high-cost medical equipments, such as O-arm, cone-beam CT, and robots, our navigation system does not need special equipment and can be implemented with common equipment in the operating room, such as C-arm, desktop, etc., even in small hospitals.

Applications of augmented reality in the neurosurgical operating room: A systematic review of the literature

Advancements in imaging techniques are key forces of progress in neurosurgery. The importance of accurate visualization of intraoperative anatomy cannot be overemphasized and is commonly delivered through traditional neuronavigation. Augmented Reality (AR) technology has been tested and applied widely in various neurosurgical subspecialties in intraoperative, clinical use and shows promise for the future. This systematic review of the literature explores the ways in which AR technology has been successfully brought into the operating room (OR) and incorporated into clinical practice. A comprehensive literature search was performed in the following databases from inception-April 2020: Ovid MEDLINE, Ovid EMBASE, and The Cochrane Library. Studies retrieved were then screened for eligibility against predefined inclusion/exclusion criteria. A total of 54 articles were included in this systematic review. The studies were sub- grouped into brain and spine subspecialties and analyzed for their incorporation of AR in the neurosurgical clinical setting. AR technology has the potential to greatly enhance intraoperative visualization and guidance in neurosurgery beyond the traditional neuronavigation systems. However, there are several key challenges to scaling the use of this technology and bringing it into standard operative practice including accurate and efficient brain segmentation of magnetic resonance imaging (MRI) scans, accounting for brain shift, reducing coregistration errors, and improving the AR device hardware. There is also an exciting potential for future work combining AR with multimodal imaging techniques and artificial intelligence to further enhance its impact in neurosurgery.

Minimally invasive supratentorial neurosurgical approaches guided by Smartphone app and compass

The precise location in the scalp of specifically planned points can help to achieve less invasive approaches. This study aims to develop a smartphone app, evaluate the precision and accuracy of the developed tool, and describe a series of cases using the referred technique. The application was developed with the React Native framework for Android and iOS. A phantom was printed based on the patient's CT scan, which was used for the calculation of accuracy and precision of the method. The points of interest were marked with an "x" on the patient's head, with the aid of the app and a compass attached to a skin marker pen. Then, two experienced neurosurgeons checked the plausibility of the demarcations based on the anatomical references. Both evaluators marked the frontal, temporal and parietal targets with a difference of less than 5 mm from the corresponding intended point, in all cases. The overall average accuracy observed was 1.6 ± 1.0 mm. The app was used in the surgical planning of trepanations for ventriculoperitoneal (VP) shunts and for drainage of abscesses, and in the definition of craniotomies for meningiomas, gliomas, brain metastases, intracranial hematomas, cavernomas, and arteriovenous malformation. The sample consisted of 88 volunteers who exhibited the following pathologies: 41 (46.6%) had brain tumors, 17 (19.3%) had traumatic brain injuries, 16 (18.2%) had spontaneous intracerebral hemorrhages, 2 (2.3%) had cavernomas, 1 (1.1%) had arteriovenous malformation (AVM), 4 (4.5%) had brain abscesses, and 7 (7.9%) had a VP shunt placement. In cases approached by craniotomy, with the exception of AVM, straight incisions and minicraniotomy were performed. Surgical planning with the aid of the NeuroKeypoint app is feasible and reliable. It has enabled neurological surgeries by craniotomy and trepanation in an accurate, precise, and less invasive manner.

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

Augmented reality in neurosurgical navigation: a survey

BACKGROUND

Neurosurgery has exceptionally high requirements for minimally invasive and safety. This survey attempts to analyze the practical application of AR in neurosurgical navigation. Also, this survey describes future trends in augmented reality neurosurgical navigation systems.

METHODS

In this survey, we searched related keywords "augmented reality", "virtual reality", "neurosurgery", "surgical simulation", "brain tumor surgery", "neurovascular surgery", "temporal bone surgery", and "spinal surgery" through Google Scholar, World Neurosurgery, PubMed and Science Direct. We collected 85 articles published over the past five years in areas related to this survey.

RESULTS

Detailed study has been conducted on the application of AR in neurosurgery and found that AR is constantly improving the overall efficiency of doctor training and treatment, which can help neurosurgeons learn and practice surgical procedures with zero risks.

CONCLUSIONS

Neurosurgical navigation is essential in neurosurgery. Despite certain technical limitations, it is still a necessary tool for the pursuit of maximum security and minimal intrusiveness. This article is protected by copyright. All rights reserved.

Rhinology: Simulation Training (Part 1)

Purpose of Review

Recently, there has been an expansion of novel technologies in simulation training. Different models target different aspects of training. The aim of this review was to examine existing evidence about training simulators in rhinology, their incorporation into real training programmes and translation of these skills into the operating room. The first part focuses on the virtual and augmented reality simulators. The second part describes the role of physical (i.e. non-computer-based) models of endoscopic sinus surgery.

Recent Findings

Virtual reality simulators are still evolving and facing challenges due to their inherent cost and lack of realism in terms of the type of haptic feedback they provide. On the other hand, augmented reality seems to be a promising platform with a growing number of applications in preoperative planning, intraoperative navigation and education. Limitations in validity, registration error and level of evidence prevent the adoption of augmented reality on a wider scale or in clinical practice.

Summary

Simulation training is a maturing field that shows reasonable evidence for a number of models. The incorporation of these models into real training programmes requires further evaluation to ensure that training opportunities are being maximized.

Distinctive accuracy measurement of binary descriptors in mobile augmented reality

Mobile Augmented Reality (MAR) requires a descriptor that is robust to changes in viewing conditions in real time application. Many different descriptors had been proposed in the literature for example floating-point descriptors (SIFT and SURF) and binary descriptors (BRIEF, ORB, BRISK and FREAK). According to literature, floating-point descriptors are not suitable for real-time application because its operating speed does not satisfy real-time constraints. Binary descriptors have been developed with compact sizes and lower computation requirements. However, it is unclear which binary descriptors are more appropriate for MAR. Hence, a distinctive and efficient accuracy measurement of four state-of-the-art binary descriptors, namely, BRIEF, ORB, BRISK and FREAK were performed using the Mikolajczyk dataset and ALOI dataset to identify the most appropriate descriptor for MAR in terms of computation time and robustness to brightness, scale and rotation changes. The obtained results showed that FREAK is the most appropriate descriptor for MAR application as it able to produce an application that are efficient (shortest computation time) and robust towards scale, rotation and brightness changes.

Quantifying attention shifts in augmented reality image-guided neurosurgery

Image-guided surgery (IGS) has allowed for more minimally invasive procedures, leading to better patient outcomes, reduced risk of infection, less pain, shorter hospital stays and faster recoveries. One drawback that has emerged with IGS is that the surgeon must shift their attention from the patient to the monitor for guidance. Yet both cognitive and motor tasks are negatively affected with attention shifts. Augmented reality (AR), which merges the realworld surgical scene with preoperative virtual patient images and plans, has been proposed as a solution to this drawback. In this work, we studied the impact of two different types of AR IGS set-ups (mobile AR and desktop AR) and traditional navigation on attention shifts for the specific task of craniotomy planning. We found a significant difference in terms of the time taken to perform the task and attention shifts between traditional navigation, but no significant difference between the different AR set-ups. With mobile AR, however, users felt that the system was easier to use and that their performance was better. These results suggest that regardless of where the AR visualisation is shown to the surgeon, AR may reduce attention shifts, leading to more streamlined and focused procedures.

Computer-assisted Orthopaedic Surgery

Nowadays, operating rooms can be inefficient and overcrowded. Patient data and images are at times not well integrated and displayed in a timely fashion. This lack of coordination may cause further reductions in efficiency, jeopardize patient safety, and increase costs. Fortunately, technology has much to offer the surgical disciplines and the ongoing and recent operating room innovations have advanced preoperative planning and surgical procedures by providing visual, navigational, and mechanical computerized assistance. The field of computer-assisted surgery (CAS) broadly refers to surgical interface between surgeons and machines. It is also part of the ongoing initiatives to move away from invasive to less invasive or even noninvasive procedures. CAS can be applied preoperatively, intraoperatively, and/or postoperatively to improve the outcome of orthopaedic surgical procedures as it has the potential for greater precision, control, and flexibility in carrying out surgical tasks, and enables much better visualization of the operating field than conventional methods have afforded. CAS is an active research discipline, which brings together orthopaedic practitioners with traditional technical disciplines such as engineering, computer science, and robotics. However, to achieve the best outcomes, teamwork, open communication, and willingness to adapt and adopt new skills and processes are critical. Because of the relatively short time period over which CAS has developed, long-term follow-up studies have not yet been possible. Consequently, this review aims to outline current CAS applications, limitations, and promising future developments that will continue to impact the operating room (OR) environment and the OR in the future, particularly within orthopedic and spine surgery.