Tele-robotics and artificial-intelligence in stroke care

The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning

BACKGROUND

Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices. Therefore, this study develops an automatic and accurate image-based catheter segmentation method in cerebral angiography using deep learning.

METHODS

Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error.

RESULTS

The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view.

CONCLUSIONS

These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce.

Neurointerventional Radiology: History, Present and Future

Neurointerventional Radiology (NIR), encompassing neuroendovascular surgery, endovascular neurosurgery, and interventional neurology, is an innovative and rapidly evolving multidisciplinary specialty focused on minimally invasive therapies for a wide range of neurological disorders. This review provides a comprehensive overview of NIR, discussing the three routes into the field, highlighting their distinct training paradigms, and emphasizing the importance of unified approaches through organizations like the Society of Neurointerventional Surgery (SNIS). The paper explores the benefits of co-managed care and its potential to improve patient outcomes, as well as the role of interdisciplinary collaboration and cross-disciplinary integration in advancing the field. We discuss the various contributions of neurosurgery, radiology, and neurology to cerebrovascular surgery, aiming to inform and educate those interested in pursuing a career in neurointervention. Additionally, the review examines the adoption of innovative technologies such as robotic-assisted techniques and artificial intelligence in NIR, and their implications for patient care and the future of the specialty. By presenting a comprehensive analysis of the field of neurointervention, we hope to inspire those considering a career in this exciting and rapidly advancing specialty, and underscore the importance of interdisciplinary collaboration in shaping its future.

Robotic Interventional Neuroradiology: Progress, Challenges, and Future Prospects

Advances in robotic technology have improved standard techniques in numerous surgical and endovascular specialties, offering more precision, control, and better patient outcomes. Robotic-assisted interventional neuroradiology is an emerging field at the intersection of interventional neuroradiology and biomedical robotics. Endovascular robotics can automate maneuvers to reduce procedure times and increase its safety, reduce occupational hazards associated with ionizing radiations, and expand networks of care to reduce gaps in geographic access to neurointerventions. To date, many robotic neurointerventional procedures have been successfully performed, including cerebral angiography, intracranial aneurysm embolization, carotid stenting, and epistaxis embolization. This review aims to provide a survey of the state of the art in robotic-assisted interventional neuroradiology, consider their technical and adoption limitations, and explore future developments critical for the widespread adoption of robotic-assisted neurointerventions.

Robotically-assisted neuro-endovascular procedures: Single-Center Experience and a Review of the Literature

INTRODUCTION

Robotics could expand treatment of rapidly progressive pathologies such as acute ischemic stroke, with the potential to provide populations in need prompt access to neuro-endovascular procedures.

METHODS

Robotically-assisted (RA) neuro-endovascular procedures (RANPs) performed at our institution were retrospectively examined (RA-group, RG). A control group of manual neuro-endovascular procedures was selected (manual group, MG). Total operating room (OR) time, procedural time, contrast media use, fluoroscopy time, conversion from RA to manual control, procedural success, and complication rates were compared. A learning curve was identified.

RESULTS

Forty-one (41) RANPs were analyzed. Ages ranged from 20-82 y.o. Indications included diagnostic cerebral angiography (37), extracranial carotid artery stenting (3), and transverse sinus stent (1). Total OR time was longer in RG (median 86 vs. 71 min, p < 0.01). Procedural time (median 56 vs. 45 min, p = 0.12), fluoroscopy time (median 12 vs. 12 min, p = 0.69) and contrast media usage (82 vs. 92 ml, p = 0.54) were not significantly different. Patient radiation exposure was similar, considering similar fluoroscopy times. Radiation exposure and lead apron use were virtually absent for the main surgeon in RG. Procedural success was 83% and conversion from RA to manual control was 17% in RG. No treatment-related complications occurred. A learning curve showed that, after the fifth procedure, procedural times reduced and stabilized.

CONCLUSIONS

This series may contribute to further demonstrating the safety and feasibility of RANPs. RANPs can potentially reduce radiation exposure and physical burden for health personnel, expand acute cerebrovascular treatment to underserved areas, and enhance telementoring. Prospective studies are necessary for results to be generalized.

Novel Endovascular Interventional Surgical Robotic System Based on Biomimetic Manipulation

Endovascular therapy has emerged as a crucial therapeutic method for treating vascular diseases. Endovascular surgical robots have been used to enhance endovascular therapy. However, to date, there are no universal endovascular surgical robots that support molds of different types of devices for treating vascular diseases. We developed a novel endovascular surgical robotic system that can independently navigate the intravascular region, advance and retract devices, and deploy stents. This robot has four features: (1) The bionic design of the robot can fully simulate the entire grasping process; (2) the V-shaped relay gripper waived the need to redesign special guidewires and catheters for continuous rotation; (3) the handles designed based on the feedback mechanism can simulate push resistance and reduce iatrogenic damage; and (4) the detachable design of the grippers can reduce cross-infection risk and medical costs. We verified its performance by demonstrating six different types of endovascular surgeries. Early evaluation of the novel endovascular robotic system demonstrated its practicability and safety in endovascular surgeries.

Robotics and Artificial Intelligence in Endovascular Neurosurgery

The use of artificial intelligence (AI) and robotics in endovascular neurosurgery promises to transform neurovascular care. We present a review of the recently published neurosurgical literature on artificial intelligence and robotics in endovascular neurosurgery to provide insights into the current advances and applications of this technology.

The PubMed database was searched for "neurosurgery" OR "endovascular" OR "interventional" AND "robotics" OR "artificial intelligence" between January 2016 and August 2021. A total of 1296 articles were identified, and after applying the inclusion and exclusion criteria, 38 manuscripts were selected for review and analysis. These manuscripts were divided into four categories: 1) robotics and AI for the diagnosis of cerebrovascular pathology, 2) robotics and AI for the treatment of cerebrovascular pathology, 3) robotics and AI for training in neuroendovascular procedures, and 4) robotics and AI for clinical outcome optimization.

The 38 articles presented include 23 articles on AI-based diagnosis of cerebrovascular disease, 10 articles on AI-based treatment of cerebrovascular disease, two articles on AI-based training techniques for neuroendovascular procedures, and three articles reporting AI prediction models of clinical outcomes in vascular disorders of the brain. Innovation with robotics and AI focus on diagnostic efficiency, optimizing treatment and interventional procedures, improving physician procedural performance, and predicting clinical outcomes with the use of artificial intelligence and robotics. Experimental studies with robotic systems have demonstrated safety and efficacy in treating cerebrovascular disorders, and novel microcatheterization techniques may permit access to deeper brain regions. Other studies show that pre-procedural simulations increase overall physician performance. Artificial intelligence also shows superiority over existing statistical tools in predicting clinical outcomes.

The recent advances and current usage of robotics and AI in the endovascular neurosurgery field suggest that the collaboration between physicians and machines has a bright future for the improvement of patient care. The aim of this work is to equip the medical readership, in particular the neurosurgical specialty, with tools to better understand and apply findings from research on artificial intelligence and robotics in endovascular neurosurgery.

Feasibility of telesurgery in the modern era

Telesurgery is not a foreign concept and dates to as early as the 1920s. The use of robots in medicine has had a very positive effect and improved outcomes with little to no adverse effects. Having global access to telemedicine and telesurgery during the COVID-19 pandemic and being able to provide top medical care to gravely ill and contagious patients without compromising the safety of the medical team would be a very big achievement. We explore the hurdles needed to make it a realistic goal and give recommendations to achieve it utilizing the major advancements that have occurred over the past few years in the fields of engineering, communication etc. The biggest issues needed to be addressed are of financial investment, legal concerns, and availability of high-speed uninterrupted data connections.

Robotics, imaging, and artificial intelligence in the catheterisation laboratory

The catheterisation laboratory today combines diagnosis and therapeutics, through various imaging modalities and a prolific list of interventional tools, led by balloons and stents. In this review, we focus primarily on advances in image-based coronary interventions. The X-ray images that are the primary modality for diagnosis and interventions are combined with novel tools for visualisation and display, including multi-imaging co-registration modalities with three- and four-dimensional presentations. Interpretation of the physiologic significance of coronary stenosis based on prior angiographic images is being explored and implemented. Major efforts to reduce X-ray exposure to the staff and the patients, using computer-based algorithms for image processing, and novel methods to limit the radiation spread are being explored. The use of artificial intelligence (AI) and machine learning for better patient care requires attention to universal methods for sharing and combining large data sets and for allowing interpretation and analysis of large cohorts of patients. Barriers to data sharing using integrated and universal protocols should be overcome to allow these methods to become widely applicable. Robotic catheterisation takes the physician away from the ionising radiation spot, enables coronary angioplasty and stenting without compromising safety, and may allow increased precision. Remote coronary procedures over the internet, that have been explored in virtual and animal studies and already applied to patients in a small pilot study, open possibilities for sharing experience across the world without travelling. Application of those technologies to neurovascular, and particularly stroke interventions, may be very timely in view of the need for expert neuro-interventionalists located mostly in central areas.

Robotics and AI for Teleoperation, Tele-Assessment, and Tele-Training for Surgery in the Era of COVID-19: Existing Challenges, and Future Vision

The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above.