Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview

Computer-assisted microcatheter shaping for intracranial aneurysm embolization: evaluation of safety and efficacy in a multicenter randomized controlled trial

BACKGROUND

This study aimed to evaluate the efficacy, stability, and safety of computer-assisted microcatheter shaping (CAMS) in patients with intracranial aneurysms.

METHODS

A total of 201 patients with intracranial aneurysms receiving endovascular coiling therapy were continuously recruited and randomly assigned to the CAMS and manual microcatheter shaping (MMS) groups. The investigated outcomes included the first-trial success rate, time to position the microcatheter in aneurysms, rate of successful microcatheter placement within 5 min, delivery times, microcatheter stability, and delivery performance.

RESULTS

The rates of first-trial success (96.0% vs 66.0%, P<0.001), successful microcatheter placement within 5 min (96.04% vs 72.00%, P<0.001), microcatheter stability (97.03% vs 84.00%, P=0.002), and 'excellent' delivery performance (45.54% vs 24.00%, P<0.001) in the CAMS group were significantly higher than those in the MMS group. Additionally, the total microcatheter delivery and positioning time (1.05 minutes (0.26) vs 1.53 minutes (1.00)) was significantly shorter in the CAMS group than in the MMS group (P<0.001). Computer assistance (OR 14.464; 95% CI 4.733 to 44.207; P<0.001) and inflow angle (OR 1.014; 95% CI 1.002 to 1.025; P=0.021) were independent predictors of the first-trial success rate. CAMS could decrease the time of microcatheter position compared with MMS, whether for junior or senior surgeons (P<0.001). Moreover, computer assistance technology may be more helpful in treating aneurysms with acute angles (p<0.001).

CONCLUSIONS

The use of computer-assisted procedures can enhance the efficacy, stability, and safety of surgical plans for coiling intracranial aneurysms.

Current understanding of macrophages in intracranial aneurysm: relevant etiological manifestations, signaling modulation and therapeutic strategies

Macrophages activation and inflammatory response play crucial roles in intracranial aneurysm (IA) formation and progression. The outcome of ruptured IA is considerably poor, and the mechanisms that trigger IA progression and rupture remain to be clarified, thereby developing effective therapy to prevent subarachnoid hemorrhage (SAH) become difficult. Recently, climbing evidences have been expanding our understanding of the macrophages relevant IA pathogenesis, such as immune cells population, inflammatory activation, intra-/inter-cellular signaling transductions and drug administration responses. Crosstalk between macrophages disorder, inflammation and cellular signaling transduction aggravates the devastating consequences of IA. Illustrating the pros and cons mechanisms of macrophages in IA progression are expected to achieve more efficient treatment interventions. In this review, we summarized the current advanced knowledge of macrophages activation, infiltration, polarization and inflammatory responses in IA occurrence and development, as well as the most relevant NF-κB, signal transducer and activator of transcription 1 (STAT1) and Toll-Like Receptor 4 (TLR4) regulatory signaling modulation. The understanding of macrophages regulatory mechanisms is important for IA patients' clinical outcomes. Gaining insight into the macrophages regulation potentially contributes to more precise IA interventions and will also greatly facilitate the development of novel medical therapy.

Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants

It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.

Association of Arterial Tortuosity with Hemodynamic Parameters-A Computational Fluid Dynamics Study

BACKGROUND

Tortuosity of intracranial arteries has been proven to be associated with the risk of intracranial aneurysm development. We decided to analyze which aspects of tortuosity are correlated with hemodynamics parameters promoting intracranial aneurysm development.

METHODS

We constructed 73 idealized models of tortuous artery (length: 25 mm, diameter: 2.5 mm) with single bifurcation. For each model, on the course of segment before bifurcation, we placed 1-3 angles with measures 15, 30, 45, 60, or 75 degrees and arc lengths 2, 5, 7, 10, or 15 mm. We performed computational fluid dynamics analysis. Blood was modeled as Newtonian fluid. We have set velocity wave of 2 cardiac cycles. After performing simulation we calculated following hemodynamic parameters at the bifurcation: time average wall shear stress (TAWSS), time average wall shear stress gradient (TAWSSG), oscillatory shear index (OSI), and relative residence time (RRT).

RESULTS

We found a significant positive correlation with number of angles and TAWSS (R = 0.329; P < 0.01), TAWSSG (R = 0.317; P < 0.01), and negative with RRT (R = -0.335; P < 0.0.01). Similar results were obtained in terms of arcs lengths. On the other hand, mean angle measure was negatively correlated to TAWSS (R = -0.333; P < 0.01), TAWSSG (R = -0.473 P < 0.01), OSI (R = -0.463; P < 0.01), and positively to RRT (R = 0.332; P < 0.01). On the basis of the obtained results, we developed new tortuosity descriptor, which considered angle measures normalized to its arc length and distance from bifurcation. For such descriptor we found strong negative correlation with TAWSS (R = -0.701; P < 0.01), TAWSSG (R = 0.778; P < 0.01), OSI (R = -0.776; P < 0.01), and positive with RRT (R = 0.747; P < 0.01).

CONCLUSIONS

Hemodynamic parameters promoting aneurysm development are correlated with larger number of smaller angles located on larger arcs.

Malpractice Litigation Related to Diagnosis and Treatment of Intracranial Aneurysms

BACKGROUND AND PURPOSE

Approaches to management of intracranial aneurysms are inconsistent, in part due to apprehension relating to potential malpractice claims. The purpose of this article was to review the causes of action underlying medical malpractice lawsuits related to the diagnosis and management of intracranial aneurysms and to identify the factors associated and their outcomes.

MATERIALS AND METHODS

We consulted 2 large legal databases in the United States to search for cases in which there were jury awards and settlements related to the diagnosis and management of patients with intracranial aneurysms in the United States. Files were screened to include only those cases in which the cause of action involved negligence in the diagnosis and management of a patient with an intracranial aneurysm.

RESULTS

Between 2000 and 2020, two hundred eighty-seven published case summaries were identified, of which 133 were eligible for inclusion in the analysis. Radiologists constituted 16% of 159 physicians sued in these lawsuits. Failure to diagnose was the most common medical malpractice claim referenced (100/133 cases), with the most common subgroups being "failure to include cerebral aneurysm as a differential and thus perform adequate work-up" (30 cases), and "failure to correctly interpret aneurysm evidence on CT or MR imaging" (16 cases). Only 6 of these 16 cases were adjudicated at trial, with 2 decided in favor of the plaintiff (awarded $4,000,000 and $43,000,000, respectively).

CONCLUSIONS

Incorrect interpretation of imaging is relatively infrequent as a cause of malpractice litigation compared with failure to diagnose aneurysms in the clinical setting by neurosurgeons, emergency physicians, and primary care providers.

Prediction and analysis of periprocedural complications associated with endovascular treatment for unruptured intracranial aneurysms using machine learning

Background

The management of unruptured intracranial aneurysm (UIA) remains controversial. Recently, machine learning has been widely applied in the field of medicine. This study developed predictive models using machine learning to investigate periprocedural complications associated with endovascular procedures for UIA.

Methods

We enrolled patients with solitary UIA who underwent endovascular procedures. Periprocedural complications were defined as neurological adverse events resulting from endovascular procedures. We incorporated three machine learning algorithms into our prediction models: artificial neural networks (ANN), random forest (RF), and logistic regression (LR). The Shapley Additive Explanations (SHAP) approach and feature importance analysis were used to identify and prioritize significant features associated with periprocedural complications.

Results

In total, 443 patients were included. Forty-eight (10.83%) procedure-related complications occurred. In the testing set, the ANN model produced the largest value (0.761) for area under the curve (AUC). The RF model also achieved an acceptable AUC value of 0.735, while the AUC value of the LR model was 0.668. SHAP and feature importance analysis identified distal aneurysm, aneurysm size and treatment modality as most significant features for the prediction of periprocedural complications following endovascular treatment for UIA.

Conclusion

Periprocedural complications after endovascular treatment for UIA are not negligible. Prediction of periprocedural complications via machine learning is feasible and effective. Machine learning can serve as a promising tool in the decision-making process for UIA treatment.