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

Conventional-Style CAD Systems.

The conventional-style CAD systems were based on presupplied characteristics or imaging features, such as vessel curvature, thresholding, or a region-growing algorithm.¹⁷ The first CAD system geared toward aneurysms reported in the literature was developed by Arimura et al,¹⁸ in 2004, which consisted of multiple gray-level thresholding techniques. It showed 100% sensitivity with 2.4 false-positives (FPs) per patient based on a leave-one-out-by-patient test method. In subsequent validation studies, Hirai et al¹⁹ and Kakeda et al²⁰ found that the sensitivity was 100% and 84%, respectively. However, this algorithm was not fully automated, nor could it detect small or fusiform type of aneurysms. In addition, the aneurysms in these studies were not verified by DSA as an outside reference standard. By using DSA as the reference, Yang et al¹⁷ developed a more automated CAD algorithm by combining 2 complementary techniques: 1) automatic intracranial artery segmentation and 2) detection of points of interest from the segmented vessels. They achieved a sensitivity of 95% with up to 9 additional FP detection marks. However, the sensitivity was lower for small aneurysms (<5 mm). The conventional-style CAD schemes relied on a similar rules-based approach in which prior domain knowledge was incorporated into hand-crafted features before using ML techniques as a classifier. The major limitation is that many false-positive results are found in bending or branching portions of vessels. Multiple methods have been proposed such as an ellipsoid convex enhancement filter to selectively enhance aneurysms while reducing FPs, but the number of FPs remains high.²¹

Deep Learning CAD Systems.

More recently, DL-based CAD systems have been developed for aneurysm detection on the basis of MRA.^22⇓-24 For example, Nakao et al²² used a 2D convolutional neural network to detect aneurysms and reported a sensitivity of >90% in a single-center study. Similarly, an open-source neural network has also been applied by using 2D MIPs or original image data.^23,24 However, generalization of these studies requires further validation. The work by Ueda et al²⁵ subsequently improved the sophistication of a DL methodology with a sensitivity of 91% and 93% for the internal and external test datasets, respectively. The external dataset contained images from 4 separate institutions under different environments and MR imaging unit manufacturers, configurations, and field strengths; this feature highlights the rigor and general applicability of the model. However, the study did not have any cases negative for aneurysms. Furthermore, only 74 aneurysms and studies exclusively acquired on Siemens imaging systems were integrated in the external test dataset. Additionally, a high rate of FPs may potentially reduce the enthusiasm of radiologists for the use of this system if distinguishing true-positive aneurysms from FPs becomes too menial.

CTA images have been studied by combining a neural network segmentation model (the HeadXNet model) to augment diagnostic performance in the detection of aneurysms.²⁶ In this study, the clinicians showed significant increases in sensitivity, accuracy, and interrater agreement when they were augmented with the model. However, the lack of a reference standard and external data verification, as well as the focus only on nonruptured aneurysms of >3 mm, limited the generalization and further application of the model.

With respect to the detection of aneurysms, there is a general notion that CAD algorithms have the potential to shorten reading times and increase radiologists’ performances in the laboratory and clinical environment.²⁷ Additionally, an AI program of aneurysms may benefit patients who undergo CTA as part of an acute ischemic stroke work-up because it may decrease the likelihood of an incidental aneurysm being undetected. However, to our knowledge, most studies presented so far lack an external reference standard for validation, such as DSA, so further studies are warranted.

Automated Morphologic Analysis.

Identifying the high-risk morphologic features of saccular aneurysms is very important for rupture-risk stratification and treatment decision. Aneurysm size and shape are regarded as the most important criteria.⁵ In clinical practice, the size is routinely measured manually by physicians on 2D/3D projections. However, manual measurements have inherent limitations of subjectivity and inconsistency, which cause intra- and interobserver variations²⁸ and cannot capture the complex geometric features of aneurysms.²⁹ Researchers have introduced several computerized procedures to make morphology assessment more objective and consistent.^30⇓-32 The detection of the neck plane is the key point for multiple subsequent operations. Larrabide et al³⁰ deterministically identified the aneurysm neck based on the topology analysis of the vasculature skeleton and the concepts of deformable cylinders. They can automatically isolate the sac of an aneurysm, reduce interobserver variability, and avoid the bias between the observers. Automatically derived geometric indices were often large, irrespective of segmentation method or operators.³¹

For rapid assessment in the clinical setting, Xiang et al³² devised an image-based vascular analysis toolkit named AView (https://www.eng.buffalo.edu/Research/Hemo/AView.html) to perform automatic computation of morphologic parameters. AView provided a relatively accurate measurement with an average size error of 0.56% and volume error of 2.1% morphologically.³³ This toolkit also enabled increased consistency in morphologic measurement among operators by 62% in size and 82% in neck diameter measurements, which could help potentially avoid inappropriate clinical decisions. In the real-world scenario, Rajabzadeh-Oghaz et al,³⁴ from the same group, tested the algorithm on 39 aneurysms and found that the computer-assisted 3D approach can lead to a more accurate and consistent determination of aneurysm size and neck diameter. Besides, DL methods such as the convolutional neural network model developed by Stember et al²³ can also be used to automatically analyze morphologic indices. However, there is much work to be done before routine clinical application of these technologies is realized.

Automated Calculation of Hemodynamics.

Hemodynamics is currently deemed as an important factor for aneurysm formation and rupture risk.³⁵ Modern imaging modalities are adequate for the application of computational fluid dynamics modeling. However, complex procedures are time-consuming and demand substantial human interaction, resulting in limited application in real-time clinical practice, which requires automated tools to execute analysis. Seo et al³⁶ developed a highly automated method to execute a computational process with direct use of a voxelized contrast information from 3D angiograms to construct a level-set-based computational “mask” for a hemodynamic simulation. By testing the method in 7 patient-specific cases against the results of manual evaluation by an experienced neurosurgeon, they found that their proposed algorithm was capable of identifying the lesion and connected vessels for various types of aneurysms. The simulation results presented by the algorithm, which include the values and distribution of wall shear stress, were in line with previous computational studies.³⁷ However, this study was only hypothesis-generating and requires further refinement and validation. Considering that hemodynamic parameters are critical for the development and rupture risk, such approaches bode rather well for clinical utility due to their automation and diminishing need for human interaction.

AI-Based Prediction of Rupture Risk.

An increasing number of unruptured aneurysms are detected with the growing use of advanced imaging techniques. However, we are now confronted with the dilemma of making clinical decisions regarding treatment of unruptured aneurysms, because the risk of treatment-related fatality is relatively high, while the rupture risk is low.^5,6 Predicting rupture risk of aneurysms is challenging and ML is expected to mitigate this problem. Liu et al³⁸ adopted 17 parameters as inputs to a 2-layer feed-forward artificial neural network aimed at predicting the rupture status of anterior communicating artery aneurysms and found an excellent performance. However, this study included only 1 single-center population and used an imbalanced number of samples between ruptured (n = 540) and unruptured aneurysms (n = 54). The instability of aneurysms is considered a rupture risk. With this knowledge, Liu et al³⁹ applied radiomics tools to extract morphologic features to predict stability and found that flatness was the most important parameter; the area under the curve (AUC) in the testing set reached 0.729 when only flatness was used to predict aneurysm stability, implying the usefulness of radiomics-derived morphologic features for aneurysm rupture risk.

With the introduction of ML methods, it is interesting to understand the distinctive performances of different ML statistical learning approaches. Detmer et al⁴⁰ and Silva et al⁴¹ worked at predicting aneurysm rupture status. They trained several ML methods, including SVM and random forests classifiers. Detmer et al found that multilayer perceptron had the best performance with an AUC of 0.826 (95% CI, 0.768, 0.883) in the test set; important variables included aneurysm location, mean surface curvature, and maximum flow velocity. Silva et al found random forests to have the best performance. In their work, aneurysm location and size were the 2 features that contributed most significantly to the efficacy of the model. This difference may contribute to the uniqueness of input variants and candidate ML methods.

Prediction of Complications of SAH.

Delayed cerebral ischemia, vasospasm, and cerebral infarction are among the complications of aneurysm rupture; several studies have explored the applications of ML methods to predict at least one of them. Dumont et al⁴² developed a proof-of-concept artificial neural network prediction model of symptomatic cerebral vasospasm and found the artificial neural network–based model had a better predictive value (AUC of 0.960) than 2 multiple logistic regression models (AUC = 0.933 and 0.897) developed by Adams et al⁴³ and Qureshi et al,⁴⁴ respectively. Further validation provided an excellent performance in a markedly distinct geographic population setting of southern Arizona with a prospective use of the artificial neural network predictive model.⁴⁵ However, the artificial neural network did not incorporate the timing of symptomatic cerebral vasospasm onset and was not validated in a larger-scale population. Another study had similar findings: ML methods (SVM, random forests, and multilayer perceptron) have a higher performance than logistic regression models in the prediction of delayed cerebral ischemia.⁴⁶

Multiple-task AI has also been studied. Tanioka et al⁴⁷ used random forests to construct early prediction models of delayed cerebral ischemia, angiographic vasospasm, and cerebral infarction development with clinical variables and matricellular proteins at post-onset days 1–3. Three such proteins have been reported to be relevant to delayed cerebral ischemia: osteopontin, periostin, and galectin-3. The prediction accuracies of the 3 conditions were 95.1%, 78.1%, and 83.8%, respectively. The random forest models found that osteopontin and galectin-3 were among the top 3 most important variables. These novel studies in the assessment of complications of aneurysms have demonstrated excellent performances using ML methods.

Another interesting application is the use of clinical data and CT perfusion from hospital admissions⁴⁸ to predict outcomes of aneurysmal SAH. A random forest model was trained to predict dichotomized mRS (≤2 and >2), and the accuracy was 84.4% in the training folds and 70.9% in the validation folds. However, this study had a small population size and therefore cannot be introduced into clinical practice to practically benefit those who have SAH.

Prediction of Treatment Outcomes.

Besides aneurysmal SAH, the outcomes of aneurysm treatment have also been explored by ML. For example, the flow diverter has emerged in recent years as one of the endovascular treatments of aneurysms and is particularly suitable for treating wide-neck and intractable aneurysms with unusual morphologies. However, 25% of treated patients are at high risk for thromboembolism formation and aneurysm rerupture.⁴⁹ Paliwal et al¹¹ compared 4 supervised ML algorithms (logistic regression, SVM, K-nearest neighbor, and neural networks) to predict 6-month outcomes of flow diverter–treated aneurysms and found that neural network (AUC = 0.967) performed the best during training; G-SVM (Gaussian-SVM) and neural network had 90% prediction accuracies in the testing cohort. Although a major concern is the absence of an external validation, it is imperative to develop models that help clinicians choose flow-diverter placement in the appropriate patients.