Treatment techniques and management guidelines for intracranial aneurysms (IAs) have been continually developing and this rapid development has altered treatment decision-making for clinicians. IAs are treated in one of two ways: surgical treatments such as microsurgical clipping with or without bypass techniques, and endovascular methods such as coiling, balloon- or stent-assisted coiling, or intravascular flow diversion and intrasaccular flow disruption. In certain cases, a single approach may be inadequate in completely resolving the IA and successful treatment requires a combination of microsurgical and endovascular techniques, such as in complex aneurysms. The treatment option should be considered based on factors such as age; past medical history; comorbidities; patient preference; aneurysm characteristics such as location, morphology, and size; and finally the operator’s experience. The purpose of this review is to provide practicing neurosurgeons with a summary of the techniques available, and to aid decision-making by highlighting ideal or less ideal cases for a given technique. Next, we illustrate the evolution of techniques to overcome the shortfalls of preceding techniques. At the outset, we emphasize that this decision-making process is dynamic and will be directed by current best scientific evidence, and future technological advances.

Objective: A scoping review of the literature was conducted to identify intraoperative AI applications for robotic surgery under development and categorise them by 1) purpose of the applications, 2) level of autonomy, 3) stage of development, and 4) type of measured outcome. Background: In robotic surgery, artificial intelligence (AI) based applications have the potential to disrupt a field so far based on a master-slave paradigm. However, there is no available overview about this technology’s current stage of development and level of autonomy. Methods: MEDLINE and EMBASE were searched between January 1st 2010 and May 21^st 2022. Abstract screening, full text review and data extraction were performed independently by two reviewers. Level of autonomy was defined according to the Yang et al classification and stage of development according to the IDEAL framework. Results: 129 studies were included in the review. 97 studies (75%) described applications providing Robot Assistance (autonomy level 1), 30 studies (23%) application enabling Task Autonomy (autonomy level 2), and two studies (2%) application achieving Conditional autonomy (autonomy level 3). All studies were at IDEAL stage 0 and no clinical investigations on humans were found. 116 (90%) conducted in silico or ex-vivo experiments on inorganic material, 9 (7%) ex-vivo experiments on organic material, and 4 (3%) performed in vivo experiments in porcine models. Conclusion: Clinical evaluation of intraoperative AI applications for robotic surgery is still in its infancy and most applications have a low level of autonomy. With increasing levels of autonomy, the evaluation focus seems to shift from AI-specific metrics to process outcomes, although common standards are needed to allow comparison between systems.

Neurosurgeons today are inundated with rapidly amassing neurosurgical research publications. Systematic reviews and meta-analyses have consequently surged in popularity because, when executed properly, they constitute a high level of evidence and may save busy neurosurgeons many hours of combing and reviewing the literature for relevant articles. Meta-analysis refers to the quantitative (and discretionary) component of systematic reviews. It involves applying statistical techniques to combine effect sizes from multiple studies, which might offer more actionable insights than a systematic review without meta-analysis. Well-executed meta-analyses may prove instructive for clinical practice, but poorly conducted ones sow confusion and have the potential to cause harm. Unfortunately, recent audits have found the conduct and reporting of meta-analyses in neurosurgery (but also other surgical disciplines) to be relatively lackluster in methodologic rigor and compliance to established guidelines. Some of these deficiencies can be easily remedied through better awareness and adherence to prescribed standards-which will be reviewed in this article-but others stem from inherent problems with the source data (e.g., poor reporting of original research) as well as unique constraints faced by surgery as a field (e.g., lack of equipoise for randomized trials, or existence of learning curves for novel surgical procedures, which can lead to temporal heterogeneity), which may require unconventional tools (e.g., cumulative meta-analysis) to address. Therefore, it is also our goal to take stock of the unique issues encountered by surgeons who do meta-analysis and to highlight various techniques-some of which less well-known-to address such challenges.