European survey on follow-up strategies for unruptured intracranial aneurysms.

Introduction: The increasing detection rates of unruptured intracranial aneurysms (UIA) pose a challenge for both neurovascular centers, tasked with managing a growing pool of patients requiring regular monitoring with imaging, and the healthcare system that must bear the costs of such surveillance. While there is consensus on the need for follow-up of UIA, uncertainties persist regarding the optimal cessation of surveillance, especially when considering diverse patient risk factors and, notably, in cases of treated aneurysms with stable rest perfusion. Detailed guidelines on UIA follow-up are currently lacking, exacerbating these challenges. Research question: We sought to investigate European strategies for follow-up of untreated, microsurgically and endovascularly treated UIA. Material and methods: An online survey consisting of 15 questions about follow-up management of UIA was sent out to the cerebrovascular section of the European Association of Neurosurgical Societies (EANS). Results: The survey response rate was 27.3% (68/249). There was consenus upon the necessity for long-term follow-up of UIA (100% [n = 68]). The recommendation to perform follow-up was inversely correlated with patient age and more prevalent among endovascularly compared to microsurgically treated patients (92.6% [n = 63] vs. 70.6% [n = 48]). A majority recommended continued follow-up of treated aneurysms with stable rest perfusion, with lifelong surveillance in patients under 60 years and continuation for 5-10 years in patients aged 61-80, irrespective of whether they underwent microsurgical (38.3% [n = 23]; 33.3% [n = 20]) or endovascular (41.9% [n = 26]; 30.6% [n = 19]) treatment. Discussion and conclusion: This survey confirmed a European consensus on the necessity of long-term follow-up for untreated UIA. However, significant variations in follow-up strategies, especially for treated UIA and post-treatment rest perfusion, were noted. Despite limited evidence suggesting low risk from aneurysm remnants, respondents favored long-term follow-up, highlighting uncertainty in management. This underscores the need for collaborative research on aneurysm remnants and standardized follow-up protocols for UIA in Europe.

Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients.

BACKGROUND: Over the recent decades, the number of different manufacturers and models of cerebrospinal fluid shunt valves constantly increased. Proper identification of shunt valves on X-ray images is crucial to neurosurgeons and radiologists to derive further details of a specific shunt valve, such as opening pressure settings and MR scanning conditions. The main aim of this study is to evaluate the feasibility of an AI-assisted shunt valve detection system. METHODS: The dataset used contains 2070 anonymized images of ten different, commonly used shunt valve types. All images were acquired from skull X-rays or scout CT-images. The images were randomly split into a 80% training and 20% validation set. An implementation in Python with the FastAi library was used to train a convolutional neural network (CNN) using a transfer learning method on a pre-trained model. RESULTS: Overall, our model achieved an F1-score of 99% to predict the correct shunt valve model. F1-scores for individual shunt valves ranged from 92% for the Sophysa Sophy Mini SM8 to 100% for several other models. CONCLUSION: This technology has the potential to automatically detect different shunt valve models in a fast and precise way and may facilitate the identification of an unknown shunt valve on X-ray or CT scout images. The deep learning model we developed could be integrated into PACS systems or standalone mobile applications to enhance clinical workflows.

Training Performance Assessment for Intracranial Aneurysm Clipping Surgery Using a Patient-Specific Mixed-Reality Simulator: A Learning Curve Study.

BACKGROUND AND OBJECTIVES: The value of simulation-based training in medicine and surgery has been widely demonstrated. This study investigates the introduction and use of a new mixed-reality neurosurgical simulator in aneurysm clipping surgery, focusing on the learning curve and performance improvement. METHODS: Five true-scale craniotomy head models replicating patient-specific neuroanatomy, along with a mixed-reality simulator, a neurosurgical microscope, and a set of microsurgical instruments and clips, were used in the operation theater to simulate aneurysm microsurgery. Six neurosurgical residents participated in five video-recorded simulation sessions over 4 months. Complementary learning modalities were implemented between sessions. Thereafter, three blinded analysts reported on residents' use of the microscope, quality of manipulation, aneurysm occlusion, clipping techniques, and aneurysm rupture. Data were also captured regarding training time and clipping attempts. RESULTS: Over the course of training, clipping time and number of clipping attempts decreased significantly (P = .018, P = .032) and the microscopic skills improved (P = .027). Quality of manipulation and aneurysm occlusion scoring improved initially although the trend was interrupted because the spacing between sessions increased. Significant differences in clipping time and attempts were observed between the most and least challenging patient models (P = .005, P = .0125). The least challenging models presented higher rates of occlusion based on indocyanine green angiography evaluation from the simulator. CONCLUSION: The intracranial aneurysm clipping learning curve can be improved by implementing a new mixed-reality simulator in dedicated training programs. The simulator and the models enable comprehensive training under the guidance of a mentor.