Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network

Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model

Background

Accumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors.

Methods

A total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical-radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical-radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis.

Results

A combined clinical-radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757-0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645-0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625-0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model.

Conclusion

Our study demonstrated the effectiveness of a clinical-radiomics model for predicting rupture status in small aneurysms.

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.

New morphological parameter for intracranial aneurysms and rupture risk prediction based on artificial neural networks

BACKGROUND

Numerous studies have evaluated the rupture risk of intracranial aneurysms using morphological parameters because of their good predictive capacity. However, the limitation of current morphological parameters is that they do not always allow evaluation of irregularities of intracranial aneurysms. The purpose of this study is to propose a new morphological parameter that can quantitatively describe irregularities of intracranial aneurysms and to evaluate its performance regarding rupture risk prediction.

METHODS

In a retrospective study, conventional morphological parameters (aspect ratio, bottleneck ratio, height-to-width ratio, volume to ostium ratio, and size ratio) and a newly proposed morphological parameter (mass moment of inertia) were calculated for 125 intracranial aneurysms (80 unruptured and 45 ruptured aneurysms). Additionally, hemodynamic parameters (wall shear stress and strain) were calculated using computational fluid dynamics and fluid-structure interaction. Artificial neural networks trained with each parameter were used for rupture risk prediction.

RESULTS

All components of the mass moment of inertia (Ixx, Iyy, and Izz) were significantly higher in ruptured cases than in unruptured cases (p values for Ixx, Iyy, and Izz were 0.032, 0.047, and 0.039, respectively). When the conventional morphological and hemodynamic parameters as well as the mass moment of inertia were considered together, the highest performance for rupture risk prediction was obtained (sensitivity 96.3%; specificity 85.7%; area under the receiver operating characteristic curve 0.921).

CONCLUSIONS

The mass moment of inertia would be a useful parameter for evaluating aneurysm irregularity and hence its risk of rupture. The new approach described here may help clinicians to predict the risk of aneurysm rupture more effectively.

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.

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Introduction

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.

Methods

We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.

Results

The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.

Conclusion

The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

A framework for intracranial aneurysm detection and rupture analysis on DSA

BACKGROUND

Intracranial aneurysm is a severe cerebrovascular disease that can result in subarachnoid hemorrhage (SAH), leading to high incidence and mortality rates. Computer-aided detection of aneurysms can assist doctors in enhancing diagnostic accuracy. The analysis of aneurysm imaging holds considerable predictive value for aneurysm rupture. This paper presents a method for the detection of aneurysms and analysis of ruptures using digital subtraction angiography (DSA).

METHODS

A total of 263 aneurysms were analyzed, with 125 being ruptured and 138 being unruptured. Firstly, a filter based on the eigenvalues of the Hessian matrix was proposed for aneurysm detection. The filter's detection parameters can be automatically obtained through Bayesian optimization. Aneurysms were detected based on their structure and the response of the filter. Secondly, considering the variations in blood flow and morphology among aneurysms in DSA, intensity, texture, and blood perfusion features were extracted from the ruptured aneurysms and unruptured aneurysms. Subsequently, a sparse representation (SR) method was utilized to classify unruptured and ruptured aneurysms.

RESULTS

The experimental results for aneurysm detection showed that the F1-score was 94.1%. In the classification of ruptured and unruptured aneurysms, the accuracy, sensitivity, specificity, and area under curve (AUC) were 96.1%, 94.4%, 97.5%, and 0.982, respectively.

CONCLUSION

This paper presents a scheme combining an aneurysm detection filter and machine learning, offering a reliable solution for the diagnosis and prediction of aneurysm rupture.

A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges

Intracranial aneurysms (IAs) are highly prevalent in the population, and their rupture poses a significant risk of death or disability. However, the treatment of aneurysms, whether through interventional embolization or craniotomy clipping surgery, is not always safe and carries a certain proportion of morbidity and mortality. Therefore, early detection and prompt intervention of IAs with a high risk of rupture is of notable clinical significance. Moreover, accurately predicting aneurysms that are likely to remain stable can help avoid the risks and costs of over-intervention, which also has considerable social significance. Recent advances in artificial intelligence (AI) technology offer promising strategies to assist clinical trials. This review will discuss the state-of-the-art AI applications for assessing the rupture risk of IAs, with a focus on achievements, challenges, and potential opportunities.

A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely and appropriate treatment because the fatality rate after rupture is 50 % . Existing methods relying on morphological features (e.g., height-width ratio) measured manually by neuroradiologists are labor intensive and have limited use for risk assessment. Therefore, we propose an end-to-end deep-learning method, called TransIAR net, to automatically learn the morphological features from 3D computed tomography angiography (CTA) data and accurately predict the status of IA rupture. We devise a multiscale 3D convolutional neural network (CNN) to extract the structural patterns of the IA and its neighborhood with a dual branch of shared network structures. Moreover, we learn the spatial dependence within the IA neighborhood with a transformer encoder. Our experiments demonstrated that the features learned by TransIAR are more effective and robust than handcrafted features, resulting in a 10 % - 15 % improvement in the accuracy of rupture status prediction.

Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net

BACKGROUND AND PURPOSE

The prevalence of unruptured intracranial aneurysms in the general population is high and aneurysms are usually asymptomatic. Their diagnosis is often fortuitous on MRI and might be difficult and time consuming for the radiologist. The purpose of this study was to develop a deep learning neural network tool for automated segmentation of intracranial arteries and automated detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA).

MATERIALS AND METHODS

3D TOF-MRA with aneurysms were retrospectively extracted. All were confirmed with angiography. The data were divided into two sets: a training set of 24 examinations and a test set of 25 examinations. Manual annotations of intracranial blood vessels and aneurysms were performed by neuroradiologists. A double convolutional neuronal network based on the U-Net architecture with regularization was used to increase performance despite a small amount of training data. The performance was evaluated for the test set. Subgroup analyses according to size and location of aneurysms were performed.

RESULTS

The average processing time was 15 min. Overall, the sensitivity and the positive predictive value of the proposed algorithm were 78% (21 of 27; 95% CI: 62-94) and 62% (21 of 34; 95%CI: 46-78) respectively, with 0.5 FP/case. Despite gradual improvement in sensitivity regarding aneurysm size, there was no significant difference of sensitivity detection between subgroups of size and location.

CONCLUSIONS

This developed tool based on a double CNN with regularization trained with small dataset, enables accurate intracranial arteries segmentation as well as effective aneurysm detection on 3D TOF MRA.

A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images

BACKGROUND

Intracranial aneurysms (IAs) are a life-threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images. However, critical diagnostic information such as morphology and aneurysm location are not captured by deep learning algorithms and still require manual assessments.

PURPOSE

Digital subtraction angiography (DSA) is the gold standard for aneurysm diagnosis. To facilitate the fully automatic diagnosis of aneurysms, we proposed a comprehensive system for the detection, morphology measurement, and location classification of aneurysms on three-dimensional DSA images, allowing automatic diagnosis without further human input.

METHODS

The system comprised three neural networks: a network for aneurysm detection, a network for morphology measurement, and a network for aneurysm location identification. A cross-scale dual-path transformer module was proposed to effectively fuse local and global information to capture aneurysms of varying sizes. A multitask learning approach was also proposed to allow an accurate localization of aneurysm neck for morphology measurement.

RESULTS

The cross-scale dual-path transformer module was shown to outperform other state-of-the-art network architectures, improving segmentation, and classification accuracy. The detection network in our system achieved an F2 score of 0.946 (recall 93%, precision 100%), better than the winning team in the Cerebral Aneurysm Detection and Analysis challenge. The measurement network achieved a relative error of less than 10% for morphology measurement, at the same level as human operators. Perfect accuracy (100%) was achieved on aneurysm location classification.

CONCLUSIONS

We have demonstrated that a comprehensive system can automatically detect, measure morphology and report the aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of IAs, improving radiologists' performance and reducing their workload.

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study

BACKGROUND

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine.

OBJECTIVE

We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives.

METHODS

A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence.

RESULTS

We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training.

CONCLUSIONS

The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

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.

Machine Learning Algorithms for Rupture Risk Assessment of Intracranial Aneurysms: A Diagnostic Meta-Analysis

OBJECTIVE

Several machine learning algorithms have been increasingly applied to predict the rupture risk of intracranial aneurysms. We performed the present diagnostic meta-analysis to comprehensively evaluate the diagnostic value of machine learning algorithms for assessing the rupture risk of intracranial aneurysms.

METHODS

We systematically searched 3 electronic databases, including Medline (via PubMed), the Cochrane Register of Controlled Trials (via Ovid), and Embase (via Elsevier), to retrieve eligible studies from the databases' inception through March 2021. The latest update was performed in June 2021. StataMP, version 14, was used to estimate all pooled diagnostic values.

RESULTS

A total of 4 studies involving 6 reports were considered to meet the inclusion criteria. Our diagnostic meta-analysis generated the following pooled diagnostic values: sensitivity, 0.84 (95% confidence interval [CI], 0.75-0.90); specificity, 0.78 (95% CI, 0.68-0.85); positive likelihood ratio, 3.8 (95% CI, 2.4-5.9); negative likelihood ratio, 0.21 (95% CI, 0.12-0.35), diagnostic odd ratio, 18 (95% CI, 7-46), and area under the summary receiver operating characteristic curve, 0.88 (95% CI, 0.85-0.90).

CONCLUSIONS

Our findings have demonstrated that the diagnostic performance of machine learning algorithms for the rupture risk assessment of AIs is excellent. Considering that the negative effects resulted from the limited number of eligible studies, we suggest developing more well-designed studies with larger sample sizes to validate our findings.

Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling

Objective

Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.

Methods

From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression.

Results

A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms.

Conclusion

Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

BACKGROUND

A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences.

OBJECTIVE

This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases.

METHODS

Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases.

RESULTS

For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion.

CONCLUSION

Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

The Biological Effects of Smoking on the Formation and Rupture of Intracranial Aneurysms: A Systematic Review and Meta-Analysis

Background

Aneurysms of the cerebral vasculature are relatively common, which grow unpredictably, and even small aneurysms carry a risk of rupture. Rupture of intracranial aneurysms (IA) is a catastrophic event with a high mortality rate. Pieces of evidence have demonstrated that smoking is closely related to the formation and rupture of IA. However, the biological effect of smoking cigarettes on the formation and rupture of IA is still underrepresented.

Methods

The study protocol was prospectively registered in PROSPERO, registration number CRD42020203634. We performed a systematic search in PubMed and CNKI for studies exploring the biological effects of smoking on intracranial aneurysms published up to December 2021, and all studies were included in the analysis. The RevMan software was used for data analysis.

Results

A total of 6,196 patients were included in 14 original articles in this meta-analysis. The risk of ruptured IA in the current smoking group was significantly higher than that in the non-smoking group, with statistical significance (RR_total = 1.23, 95% CI: 1.11–1.37). After heterogeneity among cohorts was removed by the sensitivity analysis, there was still a statistically significant difference in the risk of ruptured IA between the smoking and non-smoking groups (RR total = 1.26, 95% CI: 1.18–1.34). There was no statistically significant difference in the risk of ruptured IA between the former smoking (smoking cessation) group and the non-smoking group (RR_total = 1.09, 95% CI: 0.50–2.38). After heterogeneity among cohorts was removed by sensitivity analysis, there was still no statistically significant difference in the risk of ruptured IA between the former smoking (smoking cessation) group and the non-smoking group (RRtotal = 0.75, 95% CI: 0.47–1.19). The risk of the ruptured IA in the current smoking group was significantly higher than that in the former smoking (smoking cessation) group, with a statistically significant difference (RR_total=1.42, 95%CI: 1.27–1.59).

Conclusion

Although the biological effects of smoking on the formation and rupture of IA are unknown, this study suggests that current smoking is a risk factor for ruptured IA. Quitting smoking is very important for patients with IA.

Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study

The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.

Rupture Risk Assessment for Anterior Communicating Artery Aneurysms Using Decision Tree Modeling

Background

Although anterior communicating artery (ACoA) aneurysms have a higher risk of rupture than aneurysms in other locations, whether to treat unruptured ACoA aneurysms incidentally found is a dilemma because of treatment-related complications. Machine learning models have been widely used in the prediction of clinical medicine. In this study, we aimed to develop an easy-to-use decision tree model to assess the rupture risk of ACoA aneurysms.

Methods

This is a retrospective analysis of rupture risk for patients with ACoA aneurysms from two medical centers. Morphologic parameters of these aneurysms were measured and evaluated. Univariate analysis and multivariate logistic regression analysis were performed to investigate the risk factors of aneurysm rupture. A decision tree model was developed to assess the rupture risk of ACoA aneurysms based on significant risk factors.

Results

In this study, 285 patients were included, among which 67 had unruptured aneurysms and 218 had ruptured aneurysms. Aneurysm irregularity and vessel angle were independent predictors of rupture of ACoA aneurysms. There were five features, including size ratio, aneurysm irregularity, flow angle, vessel angle, and aneurysm size, selected for decision tree modeling. The model provided a visual representation of a decision tree and achieved a good prediction performance with an area under the receiver operating characteristic curve of 0.864 in the training dataset and 0.787 in the test dataset.

Conclusion

The decision tree model is a simple tool to assess the rupture risk of ACoA aneurysms and may be considered for treatment decision-making of unruptured intracranial aneurysms.

An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs

Background

The rupture risk assessment of intracranial aneurysms (IAs) is clinically relevant. How to accurately assess the rupture risk of IAs remains a challenge in clinical decision-making.

Purpose

We aim to build an integrated model to improve the assessment of the rupture risk of IAs.

Materials and Methods

A total of 148 (39 ruptured and 109 unruptured) IA subjects were retrospectively computed with computational fluid dynamics (CFDs), and the integrated models were proposed by combining machine learning (ML) and deep learning (DL) algorithms. ML algorithms that include random forest (RF), k-nearest neighbor (KNN), XGBoost (XGB), support vector machine (SVM), and LightGBM were, respectively, adopted to classify ruptured and unruptured IAs. A Pointnet DL algorithm was applied to extract hemodynamic cloud features from the hemodynamic clouds obtained from CFD. Morphological variables and hemodynamic parameters along with the extracted hemodynamic cloud features were acted as the inputs to the classification models. The classification results with and without hemodynamic cloud features are computed and compared.

Results

Without consideration of hemodynamic cloud features, the classification accuracy of RF, KNN, XGB, SVM, and LightGBM was 0.824, 0.759, 0.839, 0.860, and 0.829, respectively, and the AUCs of them were 0.897, 0.584, 0.892, 0.925, and 0.890, respectively. With the consideration of hemodynamic cloud features, the accuracy successively increased to 0.908, 0.873, 0.900, 0.926, and 0.917. Meanwhile, the AUCs reached 0.952, 0.881, 0.950, 0.969, and 0.965 eventually. Adding consideration of hemodynamic cloud features, the SVM could perform best with the highest accuracy of 0.926 and AUC of 0.969, respectively.

Conclusion

The integrated model combining ML and DL algorithms could improve the classification of IAs. Adding consideration of hemodynamic cloud features could bring more accurate classification, and hemodynamic cloud features were important for the discrimination of ruptured IAs.

Clipping Could Be the Best Treatment Modality for Recurring Anterior Communicating Artery Aneurysms Treated Endovascularly

BACKGROUND

The anterior communicating artery (AcoA) is the most common location for intracranial aneurysms.

OBJECTIVE

To present occlusion outcomes, complication rate, recurrence rate, and predictors of recurrence in a large cohort with AcoA aneurysms treated primarily with endosaccular embolization. We also attempt to present data on the most effective treatment modality for recurrent AcoA aneurysms.

METHODS

This is a retrospective, single-center study, reviewing the outcomes of 463 AcoA aneurysms treated endovascularly between 2003 and 2018.

RESULTS

The study cohort consisted of 463 patients. Adequate immediate occlusion was achieved in 418 (90.3%). Independent functional status at discharge was observed in 269 patients (58.0%), and the mortality rate was 6.8% (31). At 6 months, adequate occlusion was achieved in 418 (90.4%). Of all the patients, recurrence was observed in 101 cases (21.8%), and of those, 98 (22.4%) underwent retreatment. The combined frequency of retreatment for the coiling group was 42.4%, which was significantly higher than the 0 incident of retreatment in the clipping group (P < .0001). Among the retreatment cohort, there was a significantly higher subsequent retreatment rate in the endovascular group (0% in the clipping group vs 42.4% in the endovascular group, P < .0001).

CONCLUSION

Coiling with and without stent/balloon assistance is a relatively safe and effective modality for the treatment of AcoA aneurysms; however, in the setting of recurrence, microsurgical reconstruction leads to improved outcomes regarding durable occlusion, thus avoiding the potential for multiple interventions in the future.

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.

Artificial Intelligence Applications in Intracranial Aneurysm: Achievements, Challenges and Opportunities

Intracranial aneurysms present in about 3% of the general population and the number of detected aneurysms is continuously rising with the advances in imaging techniques. Intracranial aneurysm rupture carries a high risk of death or permanent disabilities; therefore assessment of the intracranial aneurysm along the entire course is of great clinical importance. Given the outstanding performance of artificial intelligence (AI) in image-based tasks, many AI-based applications have emerged in recent years for the assessment of intracranial aneurysms. In this review we will summarize the state-of-the-art of AI applications in intracranial aneurysms, emphasizing the achievements, and exploring the challenges. We will also discuss the future prospects and potential opportunities. This article provides an updated view of the AI applications in intracranial aneurysms and may act as a basis for guiding the related future works.

Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction

OBJECTIVES

We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data.

METHOD

Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons.

RESULT

Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system.

CONCLUSION

Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture.

KEY POINTS

• A self-supervised learning method was proposed to mitigate the data-hungry issue of deep learning, enabling training deep neural network with a limited amount of data. • Using the proposed method, deep embeddings were extracted to represent intracranial aneurysm morphology. Prediction model based on deep embeddings was significantly better than conventional morphology model and radiomics model. • An assistive diagnosis system was developed using deep embeddings for case-based reasoning, which was shown to significantly improve neurosurgeons' performance to predict rupture.

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the k-nearest neighbor classifier showed the best performance for aneurysm rupture risk estimation, reaching an F2-score of 0.789. Our results suggest that the full use of multidimensional feature fusion can improve the performance of aneurysm rupture risk assessment. Compared with other methods, our method achieves the state-of-the-art performance for aneurysm rupture risk assessment methods based on CADA 2020.

The rupture of an anterior communicating artery aneurysm does not associate with an asymmetry in the A1 or A2 arteries: a retrospective study of radiological features

BACKGROUND AND OBJECTIVES

Although the formation and rupture risk of an anterior communicating artery (ACoA) aneurysm has been the subject of many studies, no previous study has primarily searched for the relationship of the parent and daughter vessels and the impact of their size/diameter ratio on the potential rupture risk of an AcoA aneurysm. The objective of this study is to explore this link and to further analyse the surrounding vasculature of the anterior communicating artery aneurysm.

MATERIALS AND METHODS

We conducted a retrospective analysis of 434 patients: 284 patients with an ACoA aneurysm (121 unruptured and 162 ruptured) and 150 control patients without an ΑCoA aneurysm. Radiological angiography investigations were used to assess the diameter ratios of the parent vessels in addition to ACoA aneurysm morphology parameters.

RESULTS

When comparing the ruptured to the unruptured cases, we observed no significant difference in the parent or daughter vessel diameter ratios. Younger patient age (OR 0.96, p = 0.00) and a higher aneurysm size ratio (OR 1.10, p = 0.02) were of prognostic importance concerning the rupture risk of the aneurysm. The A1 diameter ratio and the A2 diameter were not statistically significant (OR 1.00, p = 0.99, and OR 3.38, p = 0.25 respectively).

CONCLUSIONS

In our study, we focused on asymmetry in the parent and daughter vessels as well as traditional ACoA aneurysm morphological characteristics. We were able to label younger patient age and a greater size ratio as independent prognostic factors for ACoA aneurysm rupture. We were unable to label parent and daughter vessel asymmetry as prognostic factors. To validate our findings, parent and daughter vessel asymmetry should be subjected to future prospective studies.

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

Risk factors for anterior communicating artery aneurysm rupture: A protocol for systematic review and meta-analysis

BACKGROUND

Although the research on the risk factors of anterior communicating artery (AComA) aneurysm has made great progress, the independent effect of each risk factor on the rupture of AComA aneurysm is controversial among different studies. We will perform a protocol for systematic review and meta-analysis to investigate risk factors for AComA aneurysm rupture and quantify their independent effects.

METHODS

A systematic search according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines in PubMed, Embase, and the Cochrane Library databases was conducted from inception to August 31, 2021 for published studies concerning risk factors for AComA aneurysm rupture. In the absence of statistical heterogeneity (ie, P > .10 and I2 < 50%), we will use a fixed-effects model to pool the results across sufficient studies. Otherwise, we will present the results employing the random-effects model. Quality assessment of the included studies will be evaluated using the Newcastle-Ottawa Scale. Statistical analyses will be performed using Stata16 (Stata Corporation, College Station, TX, USA) software.

RESULTS

The findings of this study will be submitted to peer-reviewed journals for publication.

CONCLUSION

This systematic review will provide evidence to determine the risk factors that affect the rupture of the AComA aneurysm and quantify their independent effects.

ETHICS AND DISSEMINATION

Since the proposed study uses pre-published data, ethical approval is not required.

REVIEW REGISTRATION NUMBER

CRD42021284262. (https://www.crd.york.ac.uk/PROSPERO/).

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience.

The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery.

Prediction of bleb formation in intracranial aneurysms using machine learning models based on aneurysm hemodynamics, geometry, location, and patient population

BACKGROUND

Bleb presence in intracranial aneurysms (IAs) is a known indication of instability and vulnerability.

OBJECTIVE

To develop and evaluate predictive models of bleb development in IAs based on hemodynamics, geometry, anatomical location, and patient population.

METHODS

Cross-sectional data (one time point) of 2395 IAs were used for training bleb formation models using machine learning (random forest, support vector machine, logistic regression, k-nearest neighbor, and bagging). Aneurysm hemodynamics and geometry were characterized using image-based computational fluid dynamics. A separate dataset with 266 aneurysms was used for model evaluation. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), true positive rate (TPR), false positive rate (FPR), precision, and balanced accuracy.

RESULTS

The final model retained 18 variables, including hemodynamic, geometrical, location, multiplicity, and morphology parameters, and patient population. Generally, strong and concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns together with larger, more elongated, and more distorted shapes were associated with bleb formation. The best performance on the validation set was achieved by the random forest model (AUC=0.82, TPR=91%, FPR=36%, misclassification error=27%).

CONCLUSIONS

Based on the premise that aneurysm characteristics prior to bleb formation resemble those derived from vascular reconstructions with their blebs virtually removed, machine learning models can identify aneurysms prone to bleb development with good accuracy. Pending further validation with longitudinal data, these models may prove valuable for assessing the propensity of IAs to progress to vulnerable states and potentially rupturing.

Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.

Deep Shape Features for Predicting Future Intracranial Aneurysm Growth

Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support.

Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model.

Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively.

Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging.

A nomogram to predict rupture risk of middle cerebral artery aneurysm

BACKGROUND

Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique.

METHODS

We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model.

RESULTS

Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it.

CONCLUSION

Our model can be used to predict the rupture risk of MCA aneurysm.

Multidimensional predicting model of intracranial aneurysm stability with backpropagation neural network: a preliminary study

OBJECTIVES

The stability of intracranial aneurysms (IAs) may involve in multidimensional factors. Backpropagation (BP) neural network could be adopted to support clinical work. This preliminary study aimed to delve into the feasibility of BP neural network in assessing the risk of IA rupture/growth and to prove the advantage of multidimensional model over single/double-dimensional model.

METHODS

Thirty-six IA patients were recruited from a prospective registration study (ChiCTR1900024547). All patients were followed up until aneurysm ruptured/grew or 36 months after being diagnosed with the IAs. The multidimensional data regarding clinical, morphological, and hemodynamic characteristics were acquired. Hemodynamic analyses were conducted with patient-specific models. Based on these characteristics, seven models were built with BP neural network (the ratio of training set to validation set as 8:1). The area under curves (AUC) was calculated for subsequent comparison.

RESULTS

Forty-five characteristics were determined from 36 patients with 37 IAs. In the models based on the single dimension of IA characteristics, only morphological characteristics exhibited high performance in assessing 3-year IA stability (AUC = 0.703, P = 0.035). Among the models integrating two dimensions of IA characteristics, clinical-morphological (AUC = 0.731, P = 0.016), clinical-hemodynamic (AUC = 0.702, P = 0.036), and morphological-hemodynamic (AUC = 0.785, P = 0.003) models were capable of assessing the risk of 3-year IA rupture/growth. Moreover, the models including all three dimensions exhibited the maximum predicting significance (AUC = 0.811, P = 0.001).

CONCLUSION

The present preliminary study reported that BP neural network might support assessing the 3-year stability of IAs. Models based on multidimensional characteristics could improve the assessment accuracy for IA rupture/growth.

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data

Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment. Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model. Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838-0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729-0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713-0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model. Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.

Predictors of Poor-Grade Aneurysmal Subarachnoid Hemorrhage Caused by Anterior Communicating Artery Aneurysm

BACKGROUND

Patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH) are considered to have a poor prognosis. However, the underlying reason for the association between the aneurysmal characteristics and poor-grade aSAH is still unclear. In the present study, we retrospectively evaluated the independent risk factors for patients with anterior communicating artery (ACoA) aneurysms with poor-grade aSAH.

METHODS

From January 2009 to January 2016, 477 consecutive patients with ruptured ACoA aneurysms were included in the present study. Poor-grade aSAH was defined as a World Federation of Neurosurgical Society grade of IV or V, and good-grade aSAH was defined as a grade of I-III. Univariate and multivariable regression analyses were used to investigate the differences in aneurysm morphology and clinical characteristics between the 2 groups.

RESULTS

On univariate analysis, older patients (P = 0.038), larger aneurysm size (P = 0.013), larger size ratio (P = 0.007), larger aspect ratio (P = 0.009), positive history of stroke (P = 0.001), and posterior projection aneurysms (P = 0.001) were associated with poor-grade aSAH. Multivariate analyses revealed that older patients (odds ratio [OR], 1.654; 95% confidence interval [CI], 1.004-2.728; P = 0.048), larger size ratio (OR, 1.280; 95% CI, 1.111-1.475; P = 0.001), positive history of stroke (OR, 6.051; 95% CI, 1.712-21.381; P = 0.005), and posterior projection aneurysms (OR, 2.718; 95% CI, 1.607-4.598; P < 0.001) were independently associated with poor-grade aSAH.

CONCLUSIONS

Poor-grade aSAH was independently associated with older patients, a larger size ratio, a positive history of stroke, and posterior projection aneurysms in patients with a ruptured ACoA aneurysm. These parameters could contribute to screening for patients with the potential for poor-grade aSAH.

Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58-0.98), accuracy (0.69-0.98), and C-index (0.66-0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.

Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study is to predict the clinical outcome at discharge after rupture of ACoA aneurysms using the random forest machine learning technique.

Methods: A total of 607 patients with ruptured ACoA aneurysms were included in this study between December 2007 and January 2016. In addition to basic clinical variables, 12 aneurysm morphologic parameters were evaluated. A multivariate logistic regression analysis was performed to determine the independent predictors of poor outcome. Of the 607 patients, 485 patients were randomly selected for training and the remaining for internal testing. The random forest model was developed using the training data set. An additional 202 patients from February 2016 to December 2017 were collected for externally validating the model. The prediction performance of the random forest model was compared with two radiologists.

Results: Patients' age (odds ratio [OR] = 1.04), ventilated breathing status (OR = 4.23), World Federation of Neurosurgical Societies (WFNS) grade (OR = 2.13), and Fisher grade (OR = 1.50) are significantly associated with poor outcome. None of the investigated morphological parameters of ACoA aneurysm is an independent predictor of poor outcome. The developed random forest model achieves sensitivities of 78.3% for internal test and 73.8% for external test. The areas under receiver operating characteristic (ROC) curve of the random forest model were 0.90 for the internal test and 0.84 for the external test. Both sensitivities and areas under ROC curves of our model are superior to those of two raters in both internal and external tests.

Conclusions: The random forest model presents good performance in predicting the outcome after rupture of ACoA aneurysms, which may aid in clinical decision making.

Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

Advancements in deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis, prognosis, and treatment decisions. The current state-of-the-art deep learning models for radiology applications consider only pixel-value information without data informing clinical context. Yet in practice, pertinent and accurate non-imaging data based on the clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes. To achieve a similar goal using deep learning, medical imaging pixel-based models must also achieve the capability to process contextual data from electronic health records (EHR) in addition to pixel data. In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published between 2012 and 2020. We conducted a systematic search on PubMed and Scopus for original research articles leveraging deep learning for fusion of multimodality data. In total, we screened 985 studies and extracted data from 17 papers. By means of this systematic review, we present current knowledge, summarize important results and provide implementation guidelines to serve as a reference for researchers interested in the application of multimodal fusion in medical imaging.

A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms

OBJECTIVES

Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture.

METHODS

One hundred twenty-two aneurysms were included in the study (93 unruptured). Morphological and radiomics features were extracted for each case. Statistical analysis was performed to identify significant features which were incorporated into prediction models constructed with a machine learning algorithm. To investigate the usefulness of radiomics features, three models were constructed and compared. The baseline model A was constructed with morphological features, while model B was constructed with addition of radiomics shape features and model C with more radiomics features. Multivariate analysis was performed for the ten most important variables in model C to identify independent risk factors. A simplified model based on independent risk factors was constructed for clinical use.

RESULTS

Five morphological features and 89 radiomics features were significantly associated with rupture. Model A, model B, and model C achieved the area under the receiver operating characteristic curve of 0.767, 0.807, and 0.879, respectively. Model C was significantly better than model A and model B (p < 0.001). Multivariate analysis identified two radiomics features which were used to construct the simplified model showing an AUROC of 0.876.

CONCLUSIONS

Radiomics signatures were different between ruptured and unruptured aneurysms. The use of radiomics features, especially texture features, may significantly improve rupture prediction performance.

KEY POINTS

• Significant radiomics differences exist between ruptured and unruptured intracranial aneurysms. • Radiomics shape features can significantly improve rupture prediction performance over conventional morphology-based prediction model. The inclusion of histogram and texture radiomics features can further improve the performance. • A simplified model with two variables achieved a similar level of performance as the more complex ones. Our prediction model can serve as a promising tool for the risk management of intracranial aneurysms.

Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging

OBJECTIVE

To build radiomic model in differentiating dissecting aneurysm (DA) from complicated saccular aneurysm (SA) based on high-resolution magnetic resonance imaging (HR-MRI) through machine-learning algorithm.

METHODS

Overall, 851 radiomic features from 77 cases were retrospectively analyzed, and the ElasticNet algorithm was used to build the radiomic model. A clinico-radiological model using clinical features and conventional MRI findings was also built. An integrated model was then built by incorporating the radiomic model and clinico-radiological model. The diagnostic abilities of these models were evaluated using leave one out cross validation and quantified using the receiver operating characteristic (ROC) analysis. The diagnostic performance of radiologists was also evaluated for comparison.

RESULTS

Five features were used to form the radiomic model, which yielded an area under the ROC curve (AUC) of 0.912 (95 % CI 0.846-0.976), sensitivity of 0.852, and specificity of 0.861. The radiomic model achieved a better diagnostic performance than the clinico-radiological model (AUC=0.743, 95 % CI 0.623-0.862), integrated model (AUC=0.888, 95 % CI 0.811-0.965), and even many radiologists.

CONCLUSION

Radiomic features derived from HR-MRI can reliably be used to build a radiomic model for effectively differentiating between DA and complicated SA, and it can provide an objective basis for the selection of clinical treatment plan.

Stability Assessment of Intracranial Aneurysms Using Machine Learning Based on Clinical and Morphological Features

Machine learning (ML) as a novel approach could help clinicians address the challenge of accurate stability assessment of unruptured intracranial aneurysms (IAs). We developed multiple ML models for IA stability assessment and compare their performances. We enrolled 1897 consecutive patients with unstable (n = 528) and stable (n = 1539) IAs. Thirteen patient-specific clinical features and eighteen aneurysm morphological features were extracted to generate support vector machine (SVM), random forest (RF), and feed-forward artificial neural network (ANN) models. The discriminatory performances of the models were compared with statistical logistic regression (LR) model and the PHASES score in IA stability assessment. Based on the receiver operating characteristic (ROC) curve and area under the curve (AUC) values for each model in the test set, the AUC values for RF, SVM, and ANN were 0.850 (95% CI 0.806-0.893), 0.858 (95 %CI 0.816-0.900), and 0.867 (95% CI 0.828-0.906), demonstrating good discriminatory ability. All ML models exhibited superior performance compared with the statistical LR and the PHASES score (the AUC values were 0.830 and 0.589, respectively; RF versus PHASES, P < 0.001; RF versus LR, P = 0.038). Important features contributing to the stability discrimination included three clinical features (location, sidewall/bifurcation type, and presence of symptoms) and three morphological features (undulation index, height-width ratio, and irregularity). These findings demonstrate the potential of ML to augment the clinical decision-making process for IA stability assessment, which may enable more optimal management for patients with IAs in the future.

Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

OBJECTIVES

To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets.

METHODS

Patients with intracranial aneurysms diagnosed by computed tomography angiography and confirmed by invasive cerebral angiograph or clipping surgery were included. The prediction models were developed based on clinical, aneurysm morphological, and hemodynamic parameters by conventional LR and ML methods.

RESULTS

The training, internal validation, and external validation cohorts were composed of 807 patients, 200 patients, and 108 patients, respectively. The area under curves (AUCs) of conventional LR models 1 (clinical), 2 (clinical and aneurysm morphological), and 3 (clinical, aneurysm morphological and hemodynamic characteristics) were 0.608, 0.765, and 0.886, respectively (all p < 0.05). The AUCs of ML models using random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM) were 0.871, 0.851, and 0.863, respectively. There were no difference among AUCs of conventional LR, RF, and SVM (all p > 0.05/6), while the AUC of MLP was lower than that of conventional LR (p = 0.0055).

CONCLUSION

Hemodynamic parameters play an important role in the prediction performance of the models. ML methods cannot outperform conventional LR in prediction models for rupture status of UIAs integrating clinical, aneurysm morphological, and hemodynamic parameters.

KEY POINTS

• The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management.

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment

PURPOSE

Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status.

METHODS

Hemodynamic, morphological, and patient-related information of 1631 cerebral aneurysms characterized by computational fluid dynamics simulations were used to train support vector machines (SVMs) with linear and RBF kernel (RBF-SVM), k-nearest neighbors (kNN), decision tree, random forest, and multilayer perceptron (MLP) neural network classifiers for predicting the aneurysm rupture status. The classifiers' accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated and compared to the LRM using 249 test cases obtained from two external cohorts. Additionally, important variables were determined based on the random forest and weights of the linear SVM.

RESULTS

The AUCs of the MLP, LRM, linear SVM, RBF-SVM, kNN, decision tree, and random forest were 0.83, 0.82, 0.80, 0.81, 0.76, 0.70, and 0.79, respectively. The accuracy ranged between 0.76 (decision tree,) and 0.79 (linear SVM, RBF-SVM, and MLP). Important variables for predicting the aneurysm rupture status included aneurysm location, the mean surface curvature, and maximum flow velocity.

CONCLUSION

The performance of the LRM was overall comparable to that of the other ML classifiers, confirming its potential for aneurysm rupture assessment. To further improve the predictions, additional information, e.g., related to the aneurysm wall, might be needed.

Prediction of Aneurysm Stability Using a Machine Learning Model Based on PyRadiomics-Derived Morphological Features

Background and Purpose- Discrimination of the stability of intracranial aneurysms is critical for determining the treatment strategy, especially in small aneurysms. This study aims to evaluate the feasibility of applying machine learning for predicting aneurysm stability with radiomics-derived morphological features. Methods- Morphological features of 719 aneurysms were extracted from PyRadiomics, of which 420 aneurysms with Maximum3DDiameter ranging from 4 mm to 8 mm were enrolled for analysis. The stability of these aneurysms and other clinical characteristics were reviewed from the medical records. Based on the morphologies with/without clinical features, machine learning models were constructed and compared to define the morphological determinants and screen the optimal model for predicting aneurysm stability. The effect of clinical characteristics on the morphology of unstable aneurysms was analyzed. Results- Twelve morphological features were automatically extracted from PyRadiomics implemented in Python for each aneurysm. Lasso regression defined Flatness as the most important morphological feature to predict aneurysm stability, followed by SphericalDisproportion, Maximum2DDiameterSlice, and SurfaceArea. SurfaceArea (odds ratio [OR], 0.697; 95% CI, 0.476-0.998), SphericalDisproportion (OR, 1.730; 95% CI, 1.143-2.658), Flatness (OR, 0.584; 95% CI, 0.374-0.894), Hyperlipemia (OR, 2.410; 95% CI, 1.029-5.721), Multiplicity (OR, 0.182; 95% CI, 0.082-0.380), Location at middle cerebral artery (OR, 0.359; 95% CI, 0.134-0.902), and internal carotid artery (OR, 0.087; 95% CI, 0.030-0.211) were enrolled into the final prediction model. In terms of performance, the area under curve of the model reached 0.853 (95% CI, 0.767-0.940). For unstable aneurysms, Compactness1 (P=0.035), Compactness2 (P=0.036), Sphericity (P=0.035), and Flatness (P=0.010) were low, whereas SphericalDisproportion (P=0.034) was higher in patients with hypertension. Conclusions- Morphological features extracted from PyRadiomics can be used for aneurysm stratification. Flatness is the most important morphological determinant to predict aneurysm stability. Our model can be used to predict aneurysm stability. Unstable aneurysm is more irregular in patients with hypertension.