Augmented Reality-Guided Mastoidectomy Simulation: A Randomized Controlled Trial Assessing Surgical Proficiency.

OBJECTIVE: Mastoidectomy surgical training is challenging due to the complex nature of the anatomical structures involved. Traditional training methods based on direct patient care and cadaveric temporal bone training have practical shortcomings. 3D-printed temporal bone models and augmented reality (AR) have emerged as promising solutions, particularly for mastoidectomy surgery, which demands an understanding of intricate anatomical structures. Evidence is needed to explore the potential of AR technology in addressing these training challenges. METHODS: 21 medical students in their clinical clerkship were recruited for this prospective, randomized controlled trial assessing mastoidectomy skills. The participants were randomly assigned to the AR group, which received real-time guidance during drilling on 3D-printed temporal bone models, or to the control group, which received traditional training methods. Skills were assessed on a modified Welling scale and evaluated independently by two senior otologists. RESULTS: The AR group outperformed the control group, with a mean overall drilling score of 19.5 out of 25, compared with the control group's score of 12 (p < 0.01). The AR group was significantly better at defining mastoidectomy margins (p < 0.01), exposing the antrum, preserving the lateral semicircular canal (p < 0.05), sharpening the sinodural angle (p < 0.01), exposing the tegmen and attic, preserving the ossicles (p < 0.01), and thinning and preserving the external auditory canal (p < 0.05). CONCLUSION: AR simulation in mastoidectomy, even in a single session, improved the proficiency of novice surgeons compared with traditional methods. LEVEL OF EVIDENCE: NA Laryngoscope, 2024.

Extended Reality in Revolutionizing Neurological Disease: A New Era for Chronic Condition Treatment.

Extended reality (XR), which includes virtual reality (VR), augmented reality (AR), and mixed reality (MR), provides promising advancements in managing chronic neurological disorders such as Parkinson's disease (PD), multiple sclerosis (MS), Alzheimer's disease, and stroke. This review examines the impact of XR technologies on neurological care, highlighting their ability to create immersive, interactive environments that enhance rehabilitation through tailored motor and cognitive exercises. XR supports neuroplasticity by providing engaging, contextually relevant exercises and real-time feedback, offering innovative alternatives to traditional methods. The technical issues, clinical validation, and accessibility must be addressed despite the potential benefits. Future developments should focus on refining XR applications, integrating them with complementary technologies, and establishing robust policies to guide their effective and ethical use. XR is poised to revolutionize neurological rehabilitation, promising improved patient outcomes and transforming medical training.

Biplane 3D overlay guidance for congenital heart disease to assist cardiac catheterization interventions-A pilot study.

Cardiac catheterization for congenital heart disease (CHD) performed under fluoroscopic guidance still lacks definition and requires exposure to ionizing radiation and contrast agents, with most patients needing multiple procedures through their lifetime, leading to cumulative radiation risks. While fusion overlay techniques have been employed in the past to aid, these have been limited to a single plane, while interventions are traditionally performed under biplane fluoroscopy. We describe our initial experience performing cardiac catheterizations guided by an enhanced biplane GuideCCI system© (Siemens Healthcare, Germany) augmented by 3D magnetic resonance imaging and computed tomography modeling. Twenty-one children and young adults with CHD undergoing catheterization procedures between October 2019 and May 2021 were chosen based on their degree of complexity of cardiac anatomy. 3D stereolithography models were generated, overlayed, and displayed in real time, alongside angiographs in both planes on the screen during these procedures. We report successful implementation of this novel technology for performance of 26 interventions including stent placements, balloon dilations, vessel occlusion and percutaneous valve and transvenous pacemaker implantation all in patients with various complex cardiac anatomies. A statistically significant reduction in radiation and contrast use was noted for coarctation of the aorta stent angioplasty and transcatheter pulmonary valve replacement when compared with national benchmarks and local institutional metrics (with and without single plane overlay). No complications were encountered with the use of this technology. Use of a tracheal registration technique provided very good correlation in most cases. Operators preferred using biplane augmented catheterization over traditional fluoroscopy in patients with complex cardiac anatomy undergoing interventions.

Augmenting Reality in Spinal Surgery: A Narrative Review of Augmented Reality Applications in Pedicle Screw Instrumentation.

Background and Objectives: The advent of augmented reality (AR) in spinal surgery represents a key technological evolution, enhancing precision and safety in procedures such as pedicle screw instrumentation. This review assesses the current applications, benefits, and challenges of AR technology in spinal surgery, focusing on its effects on surgical accuracy and patient outcomes. Materials and Methods: A comprehensive review of the literature published between January 2023 and December 2024 was conducted, focusing on AR and navigational technologies in spinal surgery. Key outcomes such as accuracy, efficiency, and complications were emphasized. Results: Thirteen studies were included, highlighting substantial improvements in surgical accuracy, efficiency, and safety with AR and navigational systems. AR technology was found to significantly reduce the learning curve for spinal surgeons, improve procedural efficiency, and potentially reduce surgical complications. The challenges identified include high system costs, the complexity of training requirements, the integration with existing workflows, and limited clinical evidence. Conclusions: AR technology holds promise for advancements in spinal surgery, particularly in improving the accuracy and safety of pedicle screw instrumentation. Despite existing challenges such as cost, training needs, and regulatory hurdles, AR has the potential to transform spinal surgical practices. Ongoing research, technological refinements, and the development of implementation strategies are essential to fully leverage AR's capabilities in enhancing patient care.

Cerebellar tDCS combined with augmented reality treadmill for freezing of gait in Parkinson's disease: a randomized controlled trial.

BACKGROUND: Parkinson's disease (PD) is often accompanied by gait disorders and freezing of gait (FoG), disabling symptoms that are resistant to conventional dopamine treatments. Given the cerebellum's connectivity with the motor cortex and basal ganglia, and its implication in PD, combining transcranial direct current stimulation targeting the cerebellum (ctDCS) with physical exercise might improve gait and balance. OBJECTIVE: This study aimed to evaluate the effectiveness of a novel rehabilitation approach that combines noninvasive cerebellar stimulation with motor-cognitive training via an augmented reality treadmill (C-Mill VR+) in individuals with PD and FoG. METHODS: Seventeen individuals with PD exhibiting FoG were enrolled in a randomized controlled trial. The participants were randomly assigned to a group receiving motor-cognitive training on the C-Mill VR+ with either ctDCS or sham ctDCS. Assessments were conducted pre-intervention (T0), post-intervention (T1) after 10 sessions, and at 4-week follow-up (T2), using various clinical scales. Additionally, C-Mill assessments of postural stability and gait were conducted at T0 and T1. RESULTS: Although no significant time*group interactions were observed for any of the clinical variables measured, some were found in the C-Mill measures. Specifically, right lower limb sway in static conditions, both with eyes open (OAD) and eyes closed (OCD), significantly improved at T1 in the ctDCS group compared with the sham group. CONCLUSIONS: C-Mill outcomes indicate that the combined treatment may enhance motor control. Participants who received ctDCS along with augmented reality motor-cognitive training showed better postural stability.

Mixed Reality in Clinical Settings for Pediatric Patients and Their Families: A Literature Review.

In the post-pandemic context, there has been an increasing demand for technology-based interventions in education and healthcare systems, such as augmented and mixed reality technologies. Despite the promising outcomes of applying mixed reality (MR), there is limited aggregated evidence focusing on child-patient interventions in hospital-based or clinical settings. This literature review aimed to identify and synthesize existing knowledge on MR technologies applied to pediatric patients in healthcare settings. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search of the Scopus and Web of Science databases was conducted to identify articles published in the last 10 years that address the application of augmented and/or MR technologies in pediatric hospital settings or clinical environments to improve patient and family outcomes. A total of 45 articles were identified, and following a rigorous screening and eligibility process, 4 review articles were selected for qualitative synthesis. From these reviews, 10 studies with relevant interventions and measured effects were extracted. The extracted studies were analyzed based on eight key attributes: country of origin, study design, characteristics of the study population, primary clinical setting, type of MR device used, nature of the intervention, variables measured, and significant effects observed in the outcome variables. The analysis revealed diverse approaches across different clinical settings, with a common focus on improving both emotional well-being and learning outcomes in pediatric patients and their families. These findings suggest that MR-based pediatric interventions generally provide children and their parents with positive emotional experiences, enhancing both learning and treatment outcomes. However, the studies reviewed were heterogeneous and varied significantly in terms of clinical settings and MR applications. Future research should focus on developing more controlled study designs that specifically target the pediatric population to strengthen the evidence base for MR interventions in healthcare.

Liposomal Drug Delivery against Helicobacter pylori Using Furazolidone and N-Acetyl Cysteine in Augmented Therapy.

Helicobacter pylori (H. pylori) infection is a significant global health concern, affecting approximately 50% of the world's population and leading to gastric ulcers, gastritis, and gastric cancer. The increase in antibiotic resistance has compromised the efficacy of existing therapeutic regimens, necessitating novel approaches for effective eradication. This study aimed to develop a targeted liposomal drug delivery system incorporating furazolidone and N-acetylcysteine (NAC) to enhance mucopenetration and improve Helicobacter pylori eradication. Liposomes were formulated with furazolidone, NAC, and Pluronic F-127 using a modified reverse-phase evaporation technique. The formulations were categorized based on charge as neutral, negative, and positive and tested for mucopenetration using a modified silicon tube method with coumarin-6 as a fluorescent marker. The encapsulation efficiency and particle size were analyzed using HPLC and an Izon q-nano particle size analyzer. The results indicated that charged liposomes showed a higher encapsulation efficiency than neutral liposomes with Pluronic F-127. Notably, combining furazolidone with 1% NAC achieved complete eradication of H. pylori in 2.5 h, compared to six hours without NAC. The findings of this study suggest that incorporating NAC and Pluronic F-127 into liposomal formulations significantly enhances mucopenetration and antimicrobial efficacy.

Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department - a machine learning approach.

BACKGROUND: Haemoglobin variation (ΔHb) induced by fluid transfer through the intestitium has been proposed as a useful tool for detecting hydrostatic pulmonary oedema (HPO). However, its use in the emergency department (ED) setting still needs to be determined. METHODS: In this observational retrospective monocentric study, ED patients admitted for acute dyspnoea were enrolled. Hb values were recorded both at ED presentation (T0) and after 4 to 8 h (T1). ΔHb between T1 and T0 (ΔHbT1-T0) was calculated as absolute and relative value. Two investigators, unaware of Hb values, defined the cause of dyspnoea as HPO and non-HPO. ΔHbT1-T0 ability to detect HPO was evaluated. A machine learning approach was used to develop a predictive tool for HPO, by considering the ability of ΔHb as covariate, together with baseline patient characteristics. RESULTS: Seven-hundred-and-six dyspnoeic patients (203 HPO and 503 non-HPO) were enrolled over 19 months. Hb levels were significantly different between HPO and non-HPO patients both at T0 and T1 (p < 0.001). ΔHbT1-T0 were more pronounced in HPO than non-HPO patients, both as relative (-8.2 [-11.2 to -5.6] vs. 0.6 [-2.1 to 3.3] %) and absolute (-1.0 [-1.4 to -0.8] vs. 0.1 [-0.3 to 0.4] g/dL) values (p < 0.001). A relative ΔHbT1-T0 of -5% detected HPO with an area under the receiver operating characteristic curve (AUROC) of 0.901 [0.896-0.906]. Among the considered models, Gradient Boosting Machine showed excellent predictive ability in identifying HPO patients and was used to create a web-based application. ΔHbT1-T0 was confirmed as the most important covariate for HPO prediction. CONCLUSIONS: ΔHbT1-T0 in patients admitted for acute dyspnoea reliably identifies HPO in the ED setting. The machine learning predictive tool may represent a performing and clinically handy tool for confirming HPO.

A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality.

Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.

[The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

[Visual object detection system based on augmented reality and steady-state visual evoked potential].

This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.

Gait and Balance Assessments with Augmented Reality Glasses in People with Parkinson's Disease: Concurrent Validity and Test-Retest Reliability.

State-of-the-art augmented reality (AR) glasses record their 3D pose in space, enabling measurements and analyses of clinical gait and balance tests. This study's objective was to evaluate concurrent validity and test-retest reliability for common clinical gait and balance tests in people with Parkinson's disease: Five Times Sit To Stand (FTSTS) and Timed Up and Go (TUG) tests. Position and orientation data were collected in 22 participants with Parkinson's disease using HoloLens 2 and Magic Leap 2 AR glasses, from which test completion durations and durations of distinct sub-parts (e.g., sit to stand, turning) were derived and compared to reference systems and over test repetitions. Regarding concurrent validity, for both tests, an excellent between-systems agreement was found for position and orientation time series (ICC(C,1) > 0.933) and test completion durations (ICC(A,1) > 0.984). Between-systems agreement for FTSTS (sub-)durations were all excellent (ICC(A,1) > 0.921). TUG turning sub-durations were excellent (turn 1, ICC(A,1) = 0.913) and moderate (turn 2, ICC(A,1) = 0.589). Regarding test-retest reliability, the within-system test-retest variation in test completion times and sub-durations was always much greater than the between-systems variation, implying that (sub-)durations may be derived interchangeably from AR and reference system data. In conclusion, AR data are of sufficient quality to evaluate gait and balance aspects in people with Parkinson's disease, with valid quantification of test completion durations and sub-durations of distinct FTSTS and TUG sub-parts.

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed.

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.

Open LEARN: Open access linear accelerator education and augmented reality Navigator.

PURPOSE: To create an open-access Linear Accelerator Education and Augmented Reality Navigator (Open LEARN) via 3D printable objects and interactive augmented reality assets. METHODS: This study describes an augmented reality linear accelerator (linac) model accessible through a QR code and a smartphone to address the challenges of medical physics and radiation oncology trainees in low-to-middle-income countries. RESULTS: Major components of a generic linear accelerator are modeled as individual objects. These objects can be 3D printed for hands-on learning and used as interactive 3D assets within the augmented reality app. In the AR app, descriptions are displayed to navigate the components spatially and textually. Items modeled include the treatment couch, klystron, circulator, RF waveguides, electron gun, waveguide, beam steering assemblies, target, collimators, multi-leaf collimators, and imaging systems. The linear accelerator is rendered at nearly 100% of its actual size, allowing users to change magnification and view objects from different angles. CONCLUSIONS: The augmented reality linear accelerators and 3D-printed objects make these complex machines easily accessible with smartphones and 3D-printing technologies, facilitating education and training through physical and virtual interaction.

Extended and augmented reality in vascular surgery: Opportunities and challenges.

Extended reality has brought new opportunities for medical imaging visualization and analysis. It regroups various subfields, including virtual reality, augmented reality, and mixed reality. Various applications have been proposed for surgical practice, as well as education and training. The aim of this review was to summarize current applications of extended reality and augmented reality in vascular surgery, highlighting potential benefits, pitfalls, limitations, and perspectives on improvement.

The Efficacy and Patient Experience of Virtual Reality in Labor: An Integrative Review of Pain and Anxiety Management.

BACKGROUND: Labor pain and anxiety are significant challenges in maternal healthcare, often managed through pharmacological interventions. Virtual Reality (VR), as a non- pharmacological method, has emerged as a potential tool for pain and anxiety relief in labor. This integrative review aims to synthesize evidence from randomized controlled trials (RCTs), qualitative studies, and mixed-methods research to evaluate the effectiveness of VR in labor pain and anxiety management and to understand patient experiences. METHODS: Adhering to the PRISMA guidelines, a structured literature search was conducted across databases, including PsycINFO, CINAHL, and PubMed, yielding 1,227 studies. Following a meticulous screening and selection process by authors, 13 studies (10 RCTs, 2 qualitative, and 1 mixed methods) met the inclusion criteria. Data extraction focused on study design, population characteristics, VR interventions, outcomes measured, and key findings, with a content analysis approach employed for thematic synthesis. RESULTS: The RCTs consistently showed VR's efficacy in reducing labor pain and, to some extent, anxiety. Qualitative studies highlighted VR's role in enhancing patient experiences, offering distraction, relaxation, and improved self-efficacy in pain management. The integration of findings from quantitative and qualitative studies provided a comprehensive understanding of VR's effectiveness and acceptability in labor. Notable themes included the importance of VR's immersive nature and its potential to reduce reliance on pharmacological interventions. CONCLUSION: VR emerges as a promising tool for managing labor pain and anxiety, offering a non-invasive and patient-friendly alternative to traditional pain relief methods. Its implementation in clinical practice could enhance patient satisfaction and overall birthing experiences. However, further research is needed to standardize VR interventions, assess long-term effects, and determine cost-effectiveness. The findings encourage the consideration of VR as part of holistic maternal care, emphasizing the need to integrate patient-centered healthcare technologies.

Chatbot for the Return of Positive Genetic Screening Results for Hereditary Cancer Syndromes: a Prompt Engineering Study.

Background: The growing demand for genomic testing and limited access to experts necessitate innovative service models. While chatbots have shown promise in supporting genomic services like pre-test counseling, their use in returning positive genetic results, especially using the more recent large language models (LLMs) remains unexplored. Objective: This study reports the prompt engineering process and intrinsic evaluation of the LLM component of a chatbot designed to support returning positive population-wide genomic screening results. Methods: We used a three-step prompt engineering process, including Retrieval-Augmented Generation (RAG) and few-shot techniques to develop an open-response chatbot. This was then evaluated using two hypothetical scenarios, with experts rating its performance using a 5-point Likert scale across eight criteria: tone, clarity, program accuracy, domain accuracy, robustness, efficiency, boundaries, and usability. Results: The chatbot achieved an overall score of 3.88 out of 5 across all criteria and scenarios. The highest ratings were in Tone (4.25), Usability (4.25), and Boundary management (4.0), followed by Efficiency (3.88), Clarity and Robustness (3.81), and Domain Accuracy (3.63). The lowest-rated criterion was Program Accuracy, which scored 3.25. Discussion: The LLM handled open-ended queries and maintained boundaries, while the lower Program Accuracy rating indicates areas for improvement. Future work will focus on refining prompts, expanding evaluations, and exploring optimal hybrid chatbot designs that integrate LLM components with rule-based chatbot components to enhance genomic service delivery.

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.

Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorithms to analyze extensive health and medical data more efficiently. In headache disorders, particularly migraine, AI has shown promising potential in various applications, such as understanding disease mechanisms and predicting patient responses to therapies. Implementing next-generation AI in headache research and treatment could transform the field by providing precision treatments and augmenting clinical practice, thereby improving patient and public health outcomes and reducing clinician workload. AI-powered tools, such as large language models, could facilitate automated clinical notes and faster identification of effective drug combinations in headache patients, reducing cognitive burdens and physician burnout. AI diagnostic models also could enhance diagnostic accuracy for non-headache specialists, making headache management more accessible in general medical practice. Furthermore, virtual health assistants, digital applications, and wearable devices are pivotal in migraine management, enabling symptom tracking, trigger identification, and preventive measures. AI tools also could offer stress management and pain relief solutions to headache patients through digital applications. However, considerations such as technology literacy, compatibility, privacy, and regulatory standards must be adequately addressed. Overall, AI-driven advancements in headache management hold significant potential for enhancing patient care, clinical practice and research, which should encourage the headache community to adopt AI innovations.

CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images.

Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. Nevertheless, limited approaches have been suggested to improve glaucoma classification due to issues like inadequate training data, variations in feature distribution, and the overall quality of samples. Furthermore, fundus images display significant similarities and slight discrepancies in lesion sizes, complicating glaucoma classification when utilizing ViTs. To address these obstacles, we introduce the contour-guided and augmented vision transformer (CA-ViT) for enhanced glaucoma classification using fundus images. We employ a Conditional Variational Generative Adversarial Network (CVGAN) to enhance and diversify the training dataset by incorporating conditional sample generation and reconstruction. Subsequently, a contour-guided approach is integrated to offer crucial insights into the disease, particularly concerning the optic disc and optic cup regions. Both the original images and extracted contours are given to the ViT backbone; then, feature alignment is performed with a weighted cross-entropy loss. Finally, in the inference phase, the ViT backbone, trained on the original fundus images and augmented data, is used for multi-class glaucoma categorization. By utilizing the Standardized Multi-Channel Dataset for Glaucoma (SMDG), which encompasses various datasets (e.g., EYEPACS, DRISHTI-GS, RIM-ONE, REFUGE), we conducted thorough testing. The results indicate that the proposed CA-ViT model significantly outperforms current methods, achieving a precision of 93.0%, a recall of 93.08%, an F1 score of 92.9%, and an accuracy of 93.0%. Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks.

Advanced technology in shoulder arthroplasty.

Background: Glenoid component positioning is an important and challenging aspect of total shoulder arthroplasty. The use of freehand technique with standard instrumentation or preoperative planning based on 2-dimensional computed tomography (CT) scans provides an opportunity for improvement in terms of component accuracy, precision, and deformity correction. These techniques have produced varying outcomes. Methods: Preoperative planning software (PPS), patient specific instrumentation (PSI), and intraoperative navigation (NAV) have been developed to improve the accuracy of implant placement and deformity correction with the ultimate goals of improved patient outcomes and implant longevity. Literature search was conducted on published and available studies comparing the accuracy of glenoid component placement and improvements in surgical and patient outcomes amongst the aforementioned techniques. Results: PPS, PSI, and NAV have demonstrated improved accuracy over freehand techniques with standard instrumentation. However, data demonstrating the clinical benefit and cost effectiveness of these new technologies are lacking. Discussion: In this paper, we reviewed the evidence available to answer the question of whether or not advanced shoulder arthroplasty technologies have been beneficial and reviewed future technologies in development such as virtual/mixed-reality and robotic assisted shoulder surgery. Level of Evidence: 4.