Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis.

Background: The use of artificial intelligence (AI) technology has been growing in the management of intracranial aneurysms (IAs). This study aims to conduct a bibliometric analysis of researches on intracranial aneurysm management with artificial intelligence technology (IAMWAIT) to gain insights into global research trends and potential future directions. Methods: A comprehensive search of articles and reviews related to IAMWAIT, published from January 1, 1900 to July 20, 2023, was conducted using the Web of Science Core Collection (WoWCC).Visualizations of the bibliometric analysis were generated utilizing WPS Office, Scimago Graphica, VOSviewer, CiteSpace, and R. Results: A total of 277 papers were included in the study. China emerged as the most prolific country in terms of publications, institutions, cooperating countries, and prolific authors. The United States garnered the highest number of total citations, institutions with the highest citations/H index, cooperating countries (n=9), and 3 of the top 10 cited papers. Both the total number of papers and the citation count exhibited a positive and significant correlation with the gross domestic product (GDP) of countries. The journal with the highest publication frequency was Frontiers in Neurology, while Stroke recorded the highest number of citations, H-index, and impact factor (IF). Areas of primary interest in IAMWAIT, leveraging AI technology, included rupture risk assessment/prediction, computer-assisted diagnosis, outcome prediction, hemodynamics, and laboratory research of IAs. Conclusions: IAMWAIT is an active area of research that has undergone rapid development in recent years. Future endeavors should focus on broader application of AI algorithms in various sub-fields of IAMWAIT to better suit the real world.

Appraising the causal association among depression, anxiety and intracranial aneurysms: Evidence from genetic studies.

BACKGROUND: The risk of intracranial aneurysms (IAs) is increased in individuals with depression and anxiety. This indicates that depression and anxiety may contribute to the development of physical disorders. Herein, to investigate the association between genetic variants related to depression and anxiety and the risk of IA, two-sample Mendelian randomization was performed. METHODS: The genome-wide association study (GWAS) comprised genome-wide genotype data of 2248 clinically well-characterized patients with anxiety and 7992 ethnically matched controls from four European countries. Sex-specific summary-level outcome data were obtained from the GWAS of IA, including 23 cohorts with a total of 10,754 cases and 306,882 controls of European and East Asian ancestry. To improve validity, five varying Mendelian randomization techniques were used in the analysis, namely Mendelian randomization-Egger, weighted median, inverse variance weighted, simple mode, and weighted mode. RESULTS: The inverse variance weighted results indicated the causal effect of depression on IA (P = 0.03, OR = 1.32 [95 % CI, 1.03-1.70]) and unruptured IA (UIA) (P = 0.02, OR = 1.68 [95 % CI, 1.08-2.61]). However, the causal relationship between depression and subarachnoid hemorrhage (SAH) was not found (P = 0.16). We identified 43 anxiety-associated single-nucleotide polymorphisms as genetic instruments and found no causal relationship between anxiety and IA, UIA, and SAH. LIMITATIONS: Potential pleiotropy, possible weak instruments, and low statistical power limited our findings. CONCLUSION: Our MR study suggested a possible causal effect of depression on the increased risk of UIAs. Future research is required to investigate whether rational intervention in depression treatment can help to decrease the societal burden of IAs.

DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data.

BACKGROUND: Intracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify potential biomarkers and therapeutic interventions. METHODS: We employed a comprehensive bioinformatics investigation of DNA methylation in IA, utilizing a transcriptomics-based methodology that encompassed 100 machine learning algorithms, genome-wide association studies (GWAS), Mendelian randomization (MR), and summary-data-based Mendelian randomization (SMR). Our sophisticated analytical strategy allowed for a systematic assessment of differentially methylated genes and their implications on the onset, progression, and rupture of IA. RESULTS: We identified DNA methylation-related genes (MRGs) and associated molecular pathways, and the MR and SMR analyses provided evidence for potential causal links between the observed DNA methylation events and IA predisposition. CONCLUSION: These insights not only augment our understanding of the molecular underpinnings of IA but also underscore potential novel biomarkers and therapeutic avenues. Although our study faces inherent limitations and hurdles, it represents a groundbreaking initiative in deciphering the intricate relationship between genetic, epigenetic, and environmental factors implicated in IA pathogenesis.

Treatment of Intracranial Vertebral Artery Dissecting Aneurysms Using Pipeline Embolization Devices : A Multicenter Cohort Study.

PURPOSE: Intracranial vertebral artery dissecting aneurysm (IVADA) is a rare type of aneurysm with high morbidity and mortality. Recently, the application of pipeline embolization devices (PEDs) has been extended to IVADAs. Here, we aim to investigate the safety and effectiveness of PEDs for IVADAs. METHOD: We retrospectively reviewed the PLUS database to identify patients who had IVADAs and were treated with PEDs from 2014 to 2019 at 14 centers across China. Data including patient and aneurysm characteristics, procedure details, angiographic and clinical results, relationship with the ipsilateral posterior inferior cerebellar artery (PICA), and patency of the PICA following PED coverage were analyzed. RESULTS: In this study 52 consecutive patients with 52 IVADAs were included. The mean age was 52.33 years and 82.7% were male. With a median follow-up of 10.5 months, the complete occlusion rate was 93.8% (45/48) and no recurrence or in-stent stenosis was detected. The total postoperative complication rate and mortality were 11.5% and 1.9%, respectively. Complications occurred in 9.6% (5/52) of patients within 30 days after the operation, including ischemic stroke in 3 and hemorrhagic stroke in 2. Another patient suffered an ischemic stroke at follow-up, 78.8% (41/52) PICAs were covered by PEDs, 1 case (2.4%) had a functional disability due to PICA occlusion, while 39.0% (16/41) had reduced flow during follow-up but hardly caused any obvious neurological deficits. Patients with IVADA involving PICA had a trend towards more complications (66.7% vs. 51.1%; P = 1). CONCLUSION: Treating IVADAs with PEDs may be a safe and effective option, with favorable clinical and angiographic outcomes; however, complications associated with this treatment should not be ignored. REGISTRATION: http://www. CLINICALTRIALS: gov . Unique identifier: NCT03831672.

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

OBJECTIVE: The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional statistical methods in predicting aneurysm rupture risk. METHODS: This retrospective study included 1740 patients with 1809 intracranial aneurysms confirmed by digital subtraction angiography at two hospitals in China from January 2014 to December 2018. We randomly divided the dataset (hospital 1) into training (80%) and internal validation (20%). External validation was performed using independent data collected from hospital 2. The prediction models were developed based on clinical, aneurysm morphological, and radiomics parameters by logistic regression (LR). Additionally, the DL model for predicting aneurysm rupture risk using integration parameters was developed and compared with other models. RESULTS: The AUCs of LR models A (clinical), B (morphological), and C (radiomics) were 0.678, 0.708, and 0.738, respectively (all p < 0.05). The AUCs of the combined feature models D (clinical and morphological), E (clinical and radiomics), and F (clinical, morphological, and radiomics) were 0.771, 0.839, and 0.849, respectively. The DL model (AUC = 0.929) outperformed the machine learning (ML) (AUC = 0.878) and the LR models (AUC = 0.849). Also, the DL model has shown good performance in the external validation datasets (AUC: 0.876 vs 0.842 vs 0.823, respectively). CONCLUSION: Radiomics signatures play an important role in predicting aneurysm rupture risk. DL methods outperformed conventional statistical methods in prediction models for the rupture risk of unruptured intracranial aneurysms, integrating clinical, aneurysm morphological, and radiomics parameters. KEY POINTS: • Radiomics parameters are associated with the rupture risk of intracranial aneurysms. • The prediction model based on integrating parameters in the deep learning model was significantly better than a conventional model. • The radiomics signature proposed in this study could guide clinicians in selecting appropriate patients for preventive treatment.