Traditional machine learning (ML) methods have long been the main representatives of early exploration and interpretable methods in medical image analysis. These algorithms typically rely on manually designed and carefully extracted features, followed by the use of classifiers or regression models to identify patterns, perform classification, or predict outcomes in medical imaging, including vascular imaging (27–29). Common algorithms include K-nearest neighbor (KNN), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and various clustering algorithms, among others. These methods often exhibit good performance and interpretability when dealing with datasets with relatively low dimensions and clear-meaning features.

Within the domain of cerebrovascular imaging analysis, a pivotal application direction of traditional ML lies in the differential diagnosis of lesions or risk stratification depending on imaging features. Typically, such studies follow a standardized workflow: (1) a large set of quantitative features is extracted from imaging data; (2) feature selection is performed to filter out the most discriminative feature subset; and (3) the selected subset is fed into a classifier for model construction. A key advantage of this approach lies in its ability to unravel complex feature combinations that are imperceptible to radiologists’ eyes. Zhao et al. conducted a multicenter study in South China that included a total of 645 patients with acute ischemic stroke or transient ischemic attack (232 patients were symptomatic, and 413 patients were asymptomatic). Among them, 317 patients (mean age: 70.20 ± 9.14 years; 159 women and 158 men) were included in the internal training set, and the other 169 patients (mean age: 70.71 ± 9.39 years; 85 women and 84 men) and 159 patients (mean age: 65.42 ± 10.41 years; 90 women and 69 men) were included in two external validation sets. A 256-slice CT scanner was used to perform the CTA scans. This study leveraged the radiomic features of perivascular adipose tissue (PVAT) surrounding the carotid artery, integrated with the training of multiple classifiers—including KNN, SVM, logistic regression (LR), RF, LDA, multinomial naive Bayes (multinomialNB), and extreme gradient boosting (XGBoost). By integrating the radiomic score (Rad score), this combined model performed excellently in predicting symptomatic carotid plaques (30). SVMs are frequently employed for binary classification tasks, such as distinguishing electroencephalographic signals between poststroke mild cognitive impairment and vascular dementia (27), primarily because of their robust performance on small-sample and high-dimensional datasets. In contrast, RF is commonly used to predict the risk of future cerebrovascular events in patients with carotid artery stenosis because it enables feature importance evaluation and is relatively insensitive to overfitting (31).

In addition to supervised learning tasks, unsupervised clustering algorithms play a pivotal role in exploratory analysis of disease subtypes. For example, in a notable study, clustering methods were employed to investigate the correlation between necroptosis of cells and immune responses in MMD, with the primary objective of identifying potential biological subtypes (32). This exploratory approach circumvents the need for predefined labels, enabling the discovery of latent disease heterogeneity that may be overlooked by hypothesis-driven analyses. Furthermore, in scenarios where data annotation is challenging—often characterized by labor-intensive processes or limited availability of expert-labeled samples—machine learning methodologies have been leveraged to generate synthetic imaging data for training set augmentation. A typical application is the generation of cerebral microbleed images for classifier training, particularly in contexts where a definitive gold standard for annotation is absent (33). This data augmentation strategy not only mitigates the constraint of insufficient labeled data but also enhances the robustness of subsequent models by introducing diverse synthetic samples that mimic real-world imaging variations. In summary, traditional ML algorithms have provided preliminary solutions for the automated analysis of intracranial vascular lesions and have demonstrated their feasibility in specific tasks. However, their inherent limitations—including heavy reliance on manual feature engineering, high sensitivity to the quality of image preprocessing, and inadequate generalizability across different clinical centers—have propelled research efforts toward DL techniques. These DL approaches are distinguished by their capacity to automatically learn hierarchical feature representations from raw data, thereby addressing the key shortcomings of traditional ML in medical imaging analysis.

As a pivotal subfield of ML, DL simulates the hierarchical information processing mechanism of the human brain by constructing neural networks with multiple hidden layers, thereby demonstrating immense potential in medical imaging analysis (34, 35). Unlike traditional ML methods that rely on handcrafted features, DL possesses the inherent capability to automatically learn multilevel feature representations directly from raw data—a characteristic that renders it particularly suited for analyzing complex imaging datasets such as HR-VWI. The convolutional neural network (CNN) stands out as one of the core DL architectures for processing imaging data (34, 36). Typical CNN models, such as AlexNet, VGG, GoogleLeNet, and ResNet, were developed primarily for image classification. Through the integration of convolutional layers, pooling layers, and fully connected layers, CNNs can capture local features and spatial hierarchical structures within images. These CNN models are heavily reliant on both the size of the training dataset and the accuracy of image annotations. In many medical image analysis tasks—particularly in 3D scenarios—it is often difficult to assemble a sufficiently large and high-quality training dataset because of challenges in data collection and annotation (37).

U-Net is specifically designed for medical image segmentation, and it significantly differs from traditional CNNs in terms of the task characteristics of vessel segmentation, network structure, training methods, and segmentation performance (Figure 2). Compared with the traditional CNNs’ asymmetric structure, where the decoder is simple (only upsampling + convolution) and lacks a dedicated fusion structure, U-Net adopts a symmetric encoder–decoder plus skip connection structure. In traditional CNNs, the decoder only performs upsampling of images, losing a large number of low-dimensional detail features. The max pooling process continuously loses spatial detail information (such as pixel coordinates and edge textures) and retains only high-level semantic features (such as “vessel area”), resulting in missed segmentation of small vessels, blurred vessel edges, and even misclassification of similar tissues around the vessels as vessels (38). U-Net’s dense skip connections are the core advantage. Low-dimensional details extracted by the encoder (such as the thin tubular edges of vessels) are directly transmitted to the corresponding layers in the decoder and fused with the global semantic features restored by upsampling. This not only ensures the spatial localization accuracy (clear edges) of the segmentation result but also enables accurate recognition of small vessels/fine branches, perfectly adapting to the fine-grained structure of vessels. Each layer of the decoder contains convolution operations to further refine the fused features, remove background noise (such as surrounding tissues and image artifacts), and enhance the saliency of vessel features (39, 40). U-Net’s output is at the same resolution as the input, whereas traditional CNNs often require interpolation or upscaling of the output after modification, leading to further loss of pixel accuracy, which is especially pronounced in the segmentation of fine structures such as vessels (41). In terms of training, traditional CNNs are highly dependent on large datasets and have weak generalizability, whereas U-Net is efficient in small sample training and is well adapted to the characteristics of medical imaging. U-Net’s elastic deformation data augmentation method applies nonrigid deformation tailored to the texture/structural characteristics of medical images, enabling the generation of many diverse training samples from a small number of annotated samples and greatly improving the model’s generalization ability (42). In terms of specific performance in vessel segmentation tasks, U-Net effectively captures weak features of small vessels/fine branches and suppresses background noise, and its segmentation results overwhelmingly surpass those of traditional CNNs in terms of completeness, precision, and robustness.

A major obstacle in medical imaging research is the limited availability of annotated data. Semisupervised learning (SSL) approaches have shown considerable promise in solving this issue in cerebrovascular segmentation. Osman and colleagues developed an SSL method with a dual-consistency approach that jointly pertains to pixel-image transformation, consistent equivariant and feature perturbation invariance. Two 3D time-of-flight (ToF) magnetic resonance angiography (MRA) datasets, including the IXI dataset (healthy subjects with approximately 600 TOF-MRI scans) and the TubeTK dataset (paired T1 and TOF-MRA images of 100 subjects), were used to evaluate the performance of this approach. The 3D UNet architecture was utilized as the teacher and student network, which was employed in collaboration to guide consistency regularization. Additionally, the pixel-level prediction performance was boosted by employing region-specific supervised loss only for the annotated input samples. The results showed that this method achieved a Dice similarity coefficient of 83.3% and an intersection-over-union of 71.5% on the IXI dataset (21). Such advances reduce the dependence on large, manually annotated datasets and help bridge the gap between research models and clinical implementation.

When working with time series medical imaging data, recurrent neural networks (RNNs) and their variant, long short-term memory (LSTM) networks, offer particular benefits (43, 44). A key strength of RNN-based models lies in their internal memory mechanism, which preserves historical information and allows them to capture temporal relationships in sequential data. For example, in analyzing dynamic functional MR images, convolutional recurrent neural networks (CRNNs) have been used to identify brain diseases by combining the spatial feature extraction ability of CNNs with the sequential modeling capacity of RNNs (43). This combined approach can identify spatial features in single fMRI images while also tracking changes over successive scans, leading to improved detection accuracy. Another common issue in longitudinal medical imaging—such as studies involving patient dropout or image artifacts—is incomplete data. Researchers have developed training methods to improve the ability of RNN and LSTM models to handle missing values. These improved models have been used, for example, in modeling the progression of Alzheimer’s disease, where longitudinal data often contain gaps (44). By making the models more robust to incomplete datasets, these methods support more reliable predictions of disease development. Overall, RNN- and LSTM-based approaches show promise for analyzing time-sensitive data in high-resolution vessel wall imaging (HR-VWI). Potential applications include dynamically enhanced HR-VWI, which tracks contrast agent movement over time, and multiple time point follow-up imaging, which is used to monitor changes in vascular lesions.

DL-based algorithms are playing an increasingly pivotal role in the diagnosis and treatment workflow of intracranial vascular lesions, offering novel technical avenues to address bottlenecks inherent in traditional medical imaging analysis (Table 1). Spanning from image preprocessing to prognostic assessment, DL algorithms are gradually being integrated into various segments of clinical workflows, thereby providing robust support for the precise diagnosis and management of cerebrovascular diseases.

In the phase of image preprocessing, DL methods exhibit remarkable efficacy in enhancing the quality of medical images. Eun et al. enrolled 14 healthy volunteers (including 7 men and 7 women) from South Korea who had undergone compressed sensing MRI. They used the basic structure of U-Net as a denoising network. These DL-based image quality enhancement approaches can be applied to compressed sensing MRI (CS-MRI). Specifically, self-supervised and unsupervised DL models have been validated to effectively improve the quality of vessel wall images while mitigating the interference of artifacts (45). Moreover, DL reconstruction techniques enable the generation of high-quality 3D T1-weighted sampling perfection with application-optimized contrasts via different flip angle evolution (3D T1-SPACE) sequence vessel wall images. On the basis of tests on images from 10 healthy volunteers and 5 consecutive patients, these techniques not only maintained or even increased image quality but also significantly reduced scan time. This method might overcome the long-standing challenge of balancing imaging efficiency and quality (46). Ota et al. enrolled 12 healthy subjects (6 males and 4 females) and 2 patients (1 male and 1 female) with coronary artery disease in Japan. By targeting the motion artifact issue commonly encountered in MRI, DL reconstruction algorithms—when combined with the design of specific k-space trajectories—have been shown to substantially enhance the motion robustness of coronary magnetic resonance angiography (CMRA) (47).

Automated vessel segmentation and 3D reconstruction are key tasks in DL applications. Livne and colleagues enrolled 66 patients with steno-occlusive cerebrovascular diseases in Germany. The U-Net model has proven effective in automatic vascular segmentation of cerebral vessels in patients, thus offering a dependable tool for the diagnosis of diseases (48). In a cohort of 19 patients (11 men and 8 women) with coronary artery diseases in Iran, the combination of texture characterization features and the U-Net model improved vascular segmentation in coronary artery disease, indicating its potential in automated vascular disease assessment (49). Zhou et al. enrolled 80 patients with intracranial atherosclerosis disease in the USA and developed a 2.5D UNet model implemented with a ResNet backbone. This DL model significantly enhances segmentation accuracy by effectively addressing the class imbalance and boundary ambiguity issues inherent in intracranial vessel wall imaging (50). These segmentation results provide essential data support for subsequent 3D vessel wall reconstruction and visualization, facilitating intuitive anatomical understanding and quantitative assessment of vascular abnormalities.

DL algorithms have demonstrated substantial improvements in lesion detection and qualitative analysis. For example, Yang et al. enrolled 392 patients with cerebral venous thrombosis (CVT) (n = 294) or without CVT (N = 98) in China. The authors developed a multisequence multitask DL algorithm for detecting CVT on the basis of routine brain MRI data. This DL model achieved an AUC of 0.96, a sensitivity of 96%, and a specificity of 88% at the per-patient diagnosis level. Additionally, a sensitivity of 88% and a specificity of 80% were achieved by this model at the per-segment diagnostic level. Compared with radiologists, this DL algorithm has greater sensitivity at both the per-patient and per-segment diagnostic levels (51). In a cohort of 421 cases (n = 129 with MCA strokes, n = 77 with posterior circulation strokes, n = 65 watershed strokes, n = 150 normal controls) in Turkey, Cetinoglu and colleagues developed a DL model based on the MobileNetV2 and EfficientNet-B0 CNN architectures. This model is capable of identifying lesions on DWI and classifying vascular territories, thereby facilitating the rapid localization and characterization of ischemic lesions (52). Additionally, Lu et al. enrolled 1,136 patients with acute ischemic stroke (AIS) in China (n = 986 in the training and internal validation cohorts, n = 150 in the external validation cohort). They developed a two-stage DL model to overcome the challenge of identifying occult acute ischemic stroke via noncontrast computed tomography (NCCT), a modality where subtle ischemic changes are often indistinguishable to the human eye. With the assistance of this model, the diagnostic accuracy for occult AIS was significantly improved (53).

In terms of quantitative parameter extraction, multiscale 3D CNN models have demonstrated the ability to identify acute ischemic cores and ischemic regions from NCCT and CTA images. For example, Wang et al. enrolled a cohort of 453 patients with AIS in China. They adopted an end-to-end 3D U-net architecture to identify the ischemic core. This model has high potential for objective quantification of the extent of ischemic lesions—a critical basis for guiding clinical intervention strategies (54). For brain lesion segmentation, Chen and colleagues developed a semisupervised approach that integrates a multiscale mean teacher, an adversarial network, and shape-aware embedding. On the basis of public brain lesion datasets from ISBI 2015 (multiple sclerosis lesions, n = 5 patients), ISLES 2015 (ischemic stroke, n = 28 labeled MRI scans), and BRATS 2018 (brain tumor, 285 training MRI scans), this method achieves precise segmentation even with limited annotated data by leveraging unlabeled samples and incorporating shape prior knowledge to constrain segmentation boundaries (55). Additionally, Wang et al. developed a deep neural network based on an attention mechanism. On the basis of datasets from 103 patients with ischemic stroke (dataset from ISLES challenge 2018), this model automatically segments ischemic stroke lesions in CTP images through image synthesis technology—addressing the challenge of ambiguous lesion boundaries in perfusion imaging and improving the accuracy of quantitative lesion assessment (56).

In the segment of risk prediction and prognostic evaluation, DL models have been developed to construct personalized prediction tools by fusing imaging features with clinical data. Fan et al. enrolled a total of 307 patients with CVD (n = 154 in the vascular cognitive impairment (VCI) group, n = 153 in the CVD patients with normal cognition (NC) group) in China. They developed a DL model based on the Vision Transformer (ViT) and an ML model utilizing clinical nonimaging data. A comprehensive hybrid model was constructed by integrating the ViT model and clinical nonimaging features. This hybrid model achieved an AUC of 0.965 and an area under the precision–recall curve (AP) of 0.972. In the external validation set (157 subjects, n = 74 in the VCI group, n = 83 in the NC group), the model demonstrated an AUC and AP of 0.902 and 0.900, respectively. These findings indicate that DL-based frameworks integrating multimodal data (e.g., structural imaging, functional imaging, and clinical demographics) can be applied to the diagnosis of vascular cognitive impairment, enhancing the discriminative ability between vascular cognitive impairment and other cognitive disorders (57, 58).

Moreover, ML methods have achieved end-to-end stroke triage by leveraging cerebrovascular morphological features, streamlining the process of prioritizing patients requiring urgent intervention (58). Wouters et al. enrolled 27 patients from the MR CLEAN (derivation) and 101 patients from the CRISP study (validation) in Europe. They developed a DL model for predicting the final infarct size in AIS patients with large vessel occlusions in the anterior circulation. Compared with computed tomography perfusion imaging processing software, this DL model exhibited improved prediction of the final infarct volume, thus providing a quantitative basis for determining the window of opportunity for reperfusion therapy (59). Yu et al. enrolled 182 patients with AIS (n = 32 with minimal reperfusion, n = 41 with partial reperfusion, n = 67 with major reperfusion, and n = 42 with unknown reperfusion) in the USA. They developed a DL model based on the U-Net architecture and demonstrated that this model can predict the final ischemic stroke lesion volume, with a diagnostic performance comparable to that of conventional clinical assessment methods (60). Nishi enrolled 324 patients with anterior circulation large vessel occlusion who were treated with mechanical thrombectomy in Japan. They developed a DL model based on a 3D-U-Net architecture. This model had the highest AUC of 0.81 compared with 0.63 and 0.64 for the Alberta Stroke Program early CT score and ischemic core volume models, respectively. This DL model has been validated to predict clinical outcomes, such as functional independence at 90 days post-treatment, in patients with large vessel occlusion (61). These advancements collectively highlight the value of DL in personalized prognostic evaluation and provide crucial references for evidence-based clinical decision-making.

Atherosclerosis represents one of the major etiologies of ischemic stroke, and radiological assessment of plaque stability and rupture risk is directly linked to strategies for secondary prevention and therapeutic decision-making. Unlike traditional imaging modalities that only enable the evaluation of luminal stenosis, HR-VWI offers a unique perspective for identifying vulnerable plaques and assessing stroke risk by directly visualizing the pathological features of the vascular wall (Table 2). The HR-VWI is capable of identifying a variety of features associated with plaque vulnerability. A thin or ruptured fibrous cap, coupled with a large lipid core, is widely recognized as a critical hallmark of vulnerable plaques (62, 63). Intraplaque hemorrhage (IPH), which typically manifests as a hyperintense signal on T1-weighted images, is strongly correlated with plaque instability (62). Compared with male patients, women with atherosclerosis often have more calcified, less lipid-rich plaques (64). Sun et al. enrolled 14 patients (13 males and 1 female) with IPH from the USA. They revealed that carotid plaques with IPH demonstrate a significantly accelerated progression rate, suggesting that hemorrhagic events may expedite the atherosclerotic process (65). Liu et al. enrolled 687 recruited patients (62.7 ± 10.1 years; 69.4% males) with carotid plaques from China. Compared with patients without ACI, those with ACI had a significantly greater BMI, younger age, higher prevalence of males, and larger volume of carotid IPH (66). Compared with other ancestries, there is a greater prevalence of intracranial atherosclerotic disease (IAS) among stroke patients of Asian (approximately 50%), Black (approximately 33%), and Hispanic ancestry (approximately 15%) (67). The high prevalence of diabetes mellitus (DM), smoking, hypertension, hypercholesterolemia, and metabolic syndrome among those populations might partly explain the high prevalence of IAS (68, 69). Collectively, these compositional features form a crucial basis for evaluating plaque stability.

The morphological characteristics and distribution patterns of plaques also hold substantial clinical significance. Vascular remodeling refers to the adaptive changes occurring in blood vessels during the development of atherosclerosis (62, 70). Positive remodeling is characterized by outward expansion of the vascular wall, which may maintain luminal patency. However, this remodeling pattern is associated with increased plaque vulnerability and a greater risk of microembolism. In contrast, negative remodeling involves inward contraction of the vascular wall, which often leads to significant luminal stenosis. The location of plaques on the vascular wall also influences clinical outcomes. HR-VWI analyses have revealed that plaques located at the orifice of perforating arteries may directly occlude these orifices, thereby increasing the risk of infarction in the perforating artery territory (71). A retrospective study enrolled 22 patients (14 males, 58.8 ± 13.0 years) with symptomatic atherosclerosis and 24 asymptomatic patients (18 males, 62.4 ± 12.3 years) from China. This study revealed that, among patients with mild middle cerebral artery stenosis, contrast enhancement and superior distribution of culprit plaques (i.e., plaques located on the ipsilateral side of the stroke) are associated with recent ischemic stroke, indicating that such plaques may still induce prominent clinical symptoms despite mild luminal narrowing (72). These findings underscore the pivotal role of the spatial distribution characteristics of plaques in the mechanisms underlying stroke.

The assessment of plaque activity is crucial for predicting clinical outcomes. The progression of plaque volume and burden is correlated with recurrent ischemic events (73). Plaque enhancement, especially the obvious enhancement observed in contrast-enhanced scans, is generally considered to reflect intraplaque inflammatory activity or neovascularization and is associated with an increased risk of recent ischemic symptoms and stroke recurrence (62). A prospective study by Yang et al. enrolled 58 patients with stroke or transient ischemic attack (mean age: 64 years; 35 males: 60%) from Hong Kong, China. This study revealed that in acute lesions, symptomatic plaques exhibit significantly stronger enhancement than asymptomatic plaques do; moreover, in patients with symptomatic ICAD, a reduction in plaque enhancement after intensive treatment is associated with improved clinical prognosis (74). Fakih et al. enrolled 344 patients with cryptogenic stroke from the USA, 34 of whom received 7 T HR-VWI MRI. The detection and quantification of symptomatic atherosclerotic plaques via 7 T HR-VWI help identify the etiology of stroke. Culprit plaques are characterized by greater contrast enhancement, more severe stenosis, and a concentric morphology (75).

These dynamic features provide potential indicators for monitoring disease progression and treatment response. With technological advancements, quantitative analysis and automatic segmentation methods for vascular walls have been continuously developed. Neural network-enhanced three-dimensional turbo spin–echo sequences have improved the quality and efficiency of intracranial vascular wall imaging (76). Wu and colleagues developed a DL model (named the proposed DeepMAD network) based on a modified U-shaped convolutional network (U-Net). This model was trained on the CAREII dataset, which includes 1,057 carotid MR images collected from 13 hospitals and medical centers in China. The AIM-HIGH trial dataset, which includes 425 carotid MRI scans collected at 21 clinical sites in China, was used as the test set. The DeepMAD model performs automated segmentation of the carotid artery wall and provides diagnostic support for atherosclerosis (77). Ma et al. enrolled 40 patients with atherosclerotic plaques in the intracranial or carotid artery in China. AI-constrained compressed sensing accelerates the acquisition of 3D isotropic T1-VISTA sequences, demonstrating the potential to substantially reduce scan times without compromising image quality (78). Furthermore, Wang et al. enrolled 115 patients (120 carotid arteries) from China. They developed a multichannel shape-aware 3D nnU-net model for segmenting the vessel wall of the carotid artery. This model allows simultaneous analysis of multisequence MR images, enabling integrated vascular wall segmentation and plaque component characterization (79). Together, these developments offer robust support for the precise quantitative assessment of atherosclerosis.

HR-VWI offers both qualitative and quantitative insights into intracranial vascular structures, making it increasingly valuable for assessing stroke risk, evaluating treatment prognosis, and guiding patient selection and strategy development for vascular recanalization. When predicting the likelihood of stroke recurrence, specific vessel wall characteristics observed on HR-VWI have demonstrated a strong correlation with subsequent ischemic events. For example, Lv et al. enrolled 132 patients with symptomatic ICAD (mean age: 59.83 ± 11.06 years; 46.2% female and 53.8% male) in China. Quantitative 3D HR-MRI analyses indicate that increased plaque burden and increased plaque enhancement ratios serve as independent predictors of long-term stroke recurrence in symptomatic ICAD patients (80). A prospective study enrolled 169 patients (70 (65, 73) years, 62.9% male) with large atherosclerotic ischemic stroke from China. This study confirmed that the total number of intracranial and extracranial carotid plaques, as well as the coexistence of extracranial carotid plaques and significantly enhanced intracranial plaques, are independent risk factors for ischemic stroke recurrence (81). Such plaque enhancement is considered to reflect the active inflammatory process or neovascularization of the lesion. Furthermore, HR-VWI can detect culprit plaques in cases where luminal stenosis is insignificant. A study based on 3D HR-VWI demonstrated that plaque volume and the total plaque enhancement ratio are significantly associated with plaques implicated in stroke. The characteristics of these culprit plaques on 3D HR-VWI have potential for predicting risk in patients whose ischemic stroke stems from arteries without major stenosis (82). These findings indicate that HR-VWI has substantial potential in identifying high-risk individuals and directing targeted, intensive secondary prevention.

HR-VWI findings are also correlated with recanalization outcomes and long-term functional recovery. In acute stroke patients, the vascular wall changes observed following recanalization therapy have important clinical implications. One study enrolled 29 patients with stroke from Korea and explored vascular wall changes following recanalization therapy for acute stroke and reported that HR-VWI can visualize concentric enhancement of the vascular wall posttreatment, which is associated with hemorrhagic transformation (83). Chao et al. enrolled 75 patients with ipsilateral recurrent ischemic stroke (53 men, mean age 57.51 ± 9.71 years) in China and investigated the feasibility of HR-VWI-guided endovascular recanalization therapy for nonacute intracranial arterial occlusion (ICAO). The study revealed that preprocedural HR-VWI evaluation of luminal and vessel wall alterations could predict the likelihood of successful endovascular recanalization, thereby supporting more precise interventional planning (84). These results underscore the clinical value of HR-VWI in selecting appropriate candidates and optimizing revascularization protocols. In symptomatic internal carotid artery occlusion (ICAO) patients, endovascular recanalization has been shown to improve outcomes. HR-VWI enables qualitative and quantitative characterization of the occluded segment—including residual lumen patency and occlusion length—which aids in assessing the technical feasibility of recanalization (85). Comparative analyses further indicate that HR-VWI supplements digital subtraction angiography (DSA) by improving patient selection for endovascular treatment of carotid tandem occlusions (86). These findings support the use of HR-VWI as a key tool for preoperative evaluation and individualized revascularization strategy design.

IAs are localized outpouchings of the cerebral vasculature that pose a significant risk of rupture and life-threatening hemorrhage. In IA evaluation, aneurysm wall enhancement (AWE) observed on HR-VWI is correlated with underlying inflammatory activity and hemodynamic stress. Huang et al. studied 100 patients (112 unruptured aneurysms) in China and reported that the intensity of AWE in unruptured IAs corresponds to more complex aneurysm morphology and irregular hemodynamic patterns (87), potentially reflecting greater wall inflammation and structural instability. In terms of clinical relevance, patients on long-term daily aspirin (≥6 months) demonstrate less AWE, implying that anti-inflammatory medication may modify aneurysm wall biology (88). HR-VWI also holds significant value for identifying aneurysms after treatment. The occurrence of AWE in aneurysms after coil embolization is associated with an increased risk of aneurysm recanalization (89), whereas after treatment with flow diverters, HR-VWI can be used to evaluate the occlusion status of the aneurysm effectively and the patency of the parent artery (90).

Arterial dissection refers to a tear of the arterial intima, where blood enters the arterial wall to form a false lumen, which may lead to vascular stenosis or occlusion. HR-VWI can clearly visualize typical dissecting features, including intimal flaps, double-lumen signs, and intramural hematomas. In the assessment of spontaneous unruptured chronic ICAD, HR-VWI is capable of identifying distinctive morphological characteristics, thereby providing important evidence for diagnosis (91). For vertebrobasilar dissecting aneurysms (VBDAs), which are clinically difficult to diagnose, 3D HR-VWI has practical differential diagnostic value and can accurately distinguish dissecting aneurysms from segmental dilations (92). HR-VWI features are closely associated with treatment decision-making and prognostic assessment. In a prospective study by Hu et al., the preoperative vascular wall imaging patterns of patients with UIAs were found to be correlated with surgical outcomes, and specific enhancement patterns may indicate different treatment responses (93). A retrospective study on cervicocranial arterial dissection (CCAD) demonstrated that HR-VWI can be used to evaluate wall changes at different stages, including the signal characteristics of hematomas and the process of vascular wall remodeling (94). Collectively, these findings establish the critical role of HR-VWI in the individualized diagnosis and treatment of IAs and arterial dissections.

Central nervous system vasculitis (CNSV) is a group of immune-inflammatory diseases that primarily involve the small- and medium-sized blood vessels of the cerebral parenchyma, spinal cord, and leptomeninges. It presents with diverse and nonspecific clinical manifestations; traditional imaging methods rely mainly on indirect signs such as luminal stenosis for diagnosis, making differential diagnosis challenging. Unlike ICAD, which typically manifests as eccentric and irregular vascular wall thickening, the typical HR-VWI feature of CNSV is concentric, smooth vascular wall thickening involving small- and medium-sized arteries, accompanied by uniform enhancement. A study by Cao et al. revealed that HR-VWI is more sensitive than MRA in detecting CNSV, especially in pediatric patients (95). In pediatric CNSV, lesions mainly involve medium-sized vessels, presenting as grade 1 and grade 2 stenosis (grade 4 stenosis is relatively rare), and the imaging features usually show concentric and moderate enhancement—characteristics that are specific to the diagnosis of CNSV (95). In addition, HR-VWI can clearly visualize strong concentric vascular wall enhancement (VWE), which is particularly prominent in large- and medium-sized vessel vasculopathy (LMVV) and small vessel vasculopathy (SVV) (96). Patients with LMVV are more likely to present with strong concentric VWE, whereas patients with SVV more commonly have contrast-enhanced lesions in the meninges or cerebral parenchyma (96). Furthermore, HR-VWI has revealed that changes in vascular wall inflammation are closely associated with cerebral parenchymal lesions and clinical symptoms, which further supports its value in disease assessment (95).

HR-VWI not only facilitates diagnosis but also effectively monitors the therapeutic response of CNSV. A study by Patzig et al. revealed that serial follow-up imaging after treatment revealed that the degree of vascular wall enhancement gradually diminished or disappeared as clinical symptoms improved, whereas the resolution of vascular wall thickening was relatively slow (97). In a study involving 9 CNSV patients who underwent serial HR-VWI examinations, the reduction in VWE during follow-up was associated with a favorable therapeutic response. At baseline, 25.5% of the vascular segments in these patients exhibited strong concentric VWE; however, this proportion decreased significantly to 6.5% during the follow-up period, indicating effective treatment (98). Additionally, in cases of disease recurrence, VWE scores worsened during follow-up; however, after intensive immunosuppressive therapy, VWE scores improved significantly. These findings demonstrate that the HR-VWI can dynamically reflect disease activity, providing a basis for adjusting treatment regimens.

Moyamoya disease is a cerebrovascular disorder characterized by progressive stenosis/occlusion of the terminal segment of the internal carotid artery and its branches, accompanied by the formation of basal collateral vessels (i.e., “moyamoya vessels”). MMD is more prevalent in East Asian countries, particularly Japan and Korea (99). The diagnosis of MMD relies primarily on luminal changes and moyamoya-like collateral networks visualized via angiography (100). However, these methods are unable to reveal the specific structures and pathological activities of pathological vessel walls. HR-VWI, as reviewed by Yang et al., can clearly reveal features such as vascular stenosis, intimal thickening, and medial thinning in MMD patients (101). Ouyang et al. enrolled 47 patients with MMD from China and retrospectively analyzed the relationships between intravascular enhancement sign (IVES) in HR-VWI and cerebral perfusion indicators. HR-VWI can visualize the degree of vascular wall enhancement in MMD patients, which is closely associated with disease stage (e.g., Suzuki staging); the higher the degree of enhancement is, the faster the disease progresses (102). Similarly, Lu et al. enrolled 170 MMD patients with HR-VWI data from China. They reported that vessel wall enhancement is a prominent feature in MMD patients and is positively associated with rapid progression of arterial stenosis and increased risk for stroke (103). Kim and colleagues enrolled 66 patients with MMD from Korea. Serial HR-MRI indicated that the degree of stenosis increased with negative remodeling (outer diameter shrinkage). Most patients (n = 61) showed vascular enhancement. However, follow-up HR-MRI revealed that only 6 patients experienced progression of enhancement, and 1 patient experienced new vascular enhancement (68). In another study enrolling 48 patients (76 hemispheres) from Korea, HR-VWI revealed significant differences in vascular morphology between ruptured and unruptured choroidal collaterals in adult MMD patients. A larger aortic left atrium (>1.285 mm²) and simple aortic angiography are independently associated with aortic hemisphere rupture (104). The above studies indicate that the features of the vessel walls of MMD patients may vary, even if they are from the same geographical region.

AVM is composed of abnormal arteriovenous shunts and niduses and is often accompanied by complex changes in perfusion and venous drainage. The application of HR-VWI in AVM assessment is relatively preliminary; however, existing studies have shown that HR-VWI can detect enhanced signals in vessel walls within or adjacent to the AVM nidus—even in AVM patients without a history of hemorrhage (105). In a pilot study, Eisenmenger et al. performed HR-VWI on 9 patients with unruptured AVMs and reported that 8 patients presented with abnormally hyperintense signals in the vessel walls of the AVM nidus. Among these 8 patients, 4 had enhancement in more than 50% of the vessel wall area, and 3 had adjacent vessel wall enhancement (105). This observation suggests that structural or inflammatory changes may already exist in AVM vessel walls even in the absence of a clinical history of rupture. A feasibility investigation examined the clinical utility of combining HR-VWI with quantitative blood flow assessment in AVM. The results demonstrated that hemorrhagic AVMs and those with elevated venous volume flow were more frequently associated with enhancing draining veins (106), suggesting that VWI and quantitative flow techniques can detect signal enhancement within AVM venous walls. Further quantitative analysis of AVM draining veins indicated that hemodynamic forces—particularly low wall shear stress—may contribute to inflammatory changes and venous stenosis (107). Collectively, these observations position HR-VWI as a promising technique for characterizing AVM angioarchitecture and hemodynamic status, offering valuable insights to inform therapeutic decision-making.

The integration of DL with HR-VWI is transforming the diagnostic approach to intracranial vascular disease. Embedding DL modules into postprocessing workflows enables rapid identification and characterization of intracranial aneurysms, atherosclerotic plaques, culprit lesions, and vascular territories. This not only reduces the manual workload and improves efficiency but also provides auxiliary judgment in high-throughput screening (Table 3). In terms of automatic IA detection and vascular morphological analysis, DL-based algorithms can directly process 3D MRA data to efficiently complete cerebrovascular segmentation and IA identification. Joo et al. constructed a 3D ResNet architecture algorithm for MRAs to automatically detect IAs. They identified 551 aneurysms (mean diameter, 4.17 ± 2.49 mm) in the training set, 147 aneurysms (mean diameter, 3.98 ± 2.11 mm) in the internal test set, and 63 aneurysms (mean diameter, 3.23 ± 1.69 mm) in the external test set. In the internal test set, the sensitivity, positive predictive value, and specificity were 87.1, 92.8, and 92.0%, respectively; in the external test set, this CNN model achieved a sensitivity of 85.7%, positive predictive value of 91.5%, and specificity of 98.0%. Notably, this model achieved a sensitivity of 84.3% (59/70) in the internal test set and 73.5% (25/34) in the external test set for IAs less than 3 mm. Generally, this model exhibited high diagnostic performance for IAs (108). Another study conducted by Ryu and colleagues included 675 participants, including 189 aneurysm-positive patients and 486 aneurysm-negative patients. They confirmed that the 3D ResNet architecture algorithm achieved patient-level sensitivity (95.2%) and specificity (80.5%). In terms of IA size, the detection sensitivities were 72.3% for lesions <3 mm, 91.8% for those 3–5 mm, and 94.3% for those >5 mm. The AUC was 0.949 (109). Those two studies in Korea also confirmed that the 3D ResNet model has a good ability to detect small aneurysms (less than 3 mm).

When DL models are extended to integrate HR-VWI, they can also classify vulnerable intracranial atherosclerotic plaques simultaneously, thereby improving diagnostic efficiency. For example, Li et al. enrolled 1806 plaques from 726 patients with symptomatic intracranial atherosclerotic stenosis in China. Two DL models, ResNet50 and ViT, exhibited good performance. The ResNet50 model achieved an AUC of 0.879, a sensitivity of 0.752, a specificity of 0.842, and an accuracy of 0.804 in the validation set. In contrast, the ViT model achieved an AUC of 0.927, sensitivity of 0.840, specificity of 0.884, and accuracy of 0.865, suggesting better performance than ResNet50 (110). Lu et al. developed a DenseNet-121 model based on MRA images from patients with MMD (n = 200), atherosclerotic disease (ASD, n = 200), or normal controls (NCs, n = 200) in China. The analysis results revealed that the macroaverage accuracies were 0.911 for DenseNet121, 0.887 for ResNet50, 0.867 for SENet154, 0.922 for SEResNet50, and 0.833 for SEResNext50. Compared with other CNN models, DenseNet-121 achieved an AUC of 0.977 in the internal test sets and 0.880 in the external validation sets, suggesting its superior discrimination capabilities. The authors also performed subgroup analysis stratified by age and sex and verified that the classification performance of the CNN models in MMD, ASD, and NC patients was not significantly different across different age or sex groups. These results indicate that CNN-based DL models improve the accuracy and efficiency of MMD detection and reduce the workload of radiologists (111).

In the fields of culprit plaque identification and vascular territory division, DL models can accurately recognize the culprit plaques causing ischemic stroke by extracting information on plaque morphology, texture, and spatial distribution contained in HR-VWI. Xu et al. obtained MRVWI images acquired from 124 patients with atherosclerotic plaques. They proposed a VWISegNet model for pixel-level classification of arterial walls and plaques. Compared with the traditional U-Net architecture, the VWISegNet model has more residual units, which makes it easier to propagate information between low and high levels, thus alleviating the vanishing gradient problem. VWISegNet achieved better segmentation performance, with a Dice similarity coefficient (DSC) of 93.8% for lumen contours and 86.0% for outer wall contours, and the average surface distance (ASD) values were less than 0.198 mm. A coefficient value over 0.780 was identified when comparing the automatic method and manual method (112). Gao et al. performed a retrospective study enrolling 1806 intracranial plaques from 729 patients (median age: 58.0 (51.0, 66.0) years; 492 males and 234 females) with symptomatic intracranial atherosclerotic stenosis. The included patients were divided into a training set (n = 400), a validation set (n = 158), and a test set (n = 168). These patients were from four center hospitals in China. They combined the Radiomics model with the Vision Transformer (ViT) model and constructed a Habitat + ViT fusion model. In the training set, the Radiomics model, Vit model, and fusion model achieved AUCs of 0.874, 0.916, and 0.953, respectively; in the validation set, the AUCs of those models were 0.839, 0.927, and 0.949; and in the test set, the AUCs of those models were 0.857, 0.913, and 0.960, respectively. This integrated Habitat + ViT model has superior performance in identifying high-risk vulnerable plaques in symptomatic intracranial atherosclerotic stenosis (sICAS) patients (113). Zhang et al. built a DL platform called VWI Assistant for training multicenter 3D MR-VWI datasets (involving 1981 patients) in China. Compared with expert manual delineation, this model achieved a 92.9% qualified rate and reduced the processing time by more than 90% (10–12 min for each case). On the basis of real-world deployment in 1099 cases, this platform exhibited rapid clinical adoption, and the utilization rates increased from 10.8 to 100.0% within 1 year (114). Moreover, Zhang et al. enrolled 539 patients with cerebrovascular disease. By constructing a 3D V-Net network, they reported that this model is able to automatically and accurately detect the internal carotid artery and middle cerebral artery, improving vessel centerline extraction accuracy and assisting in plaque assessment (115).

In the field of image-guided clinical management, developing risk stratification and outcome prediction models using HR-VWI data offers a powerful pathway for translating imaging features into clinical decision support. DL technology facilitates not only the automated extraction of complex radiographic characteristics but also the integration of clinical parameters, imaging biomarkers, and temporal data, enabling the prediction of stroke recurrence, intracranial aneurysm stability, and long-term functional outcomes. By processing HR-VWI scans, DL models can quantify plaque morphology, internal heterogeneity, and enhancement patterns to achieve precise stroke risk assessment. Yang and colleagues enrolled 235 patients (132 symptomatic plaques and 103 asymptomatic plaques) and divided them into a training set (n = 156) and a testing set (n = 79). They compared three models for differentiating symptom-related intracranial and extracranial plaques: (1) the traditional model, which is based on the clinical and radiological characteristics of the plaques. This model achieved an AUC of 0.717 in the training set and 0.649 in the testing set, and the accuracy was 72.4% versus 68.4%; (2) the radiomics model was based on 1,130 radiomics features extracted from each ROI. This model achieved an AUC of 0.959 in the training set and 0.906 in the testing set, and the accuracy was 89.7% versus 83.5%; (3) five DL models (DenseNet 121, DenseNet 201, GoogleNet, ResNet 18, and ResNet 34) achieved an AUC of 0.947 in the training set and 0.956 in the testing set, the accuracy was 87.8% versus 89.9%, the sensitivity was 88.9% versus 95.2%, and the specificity was 86.4% versus 83.8%. Notably, the combination of the DL model and the traditional model achieved the best performance, with an AUC of 0.966 in the training set and 0.952 in the testing set (116).

By utilizing DL-based segmentation of VWI images from 28 patients in the USA, Guo et al. reported that this model can help identify intraplaque hemorrhage (IPH), quantify the IPH volume, and measure the effects of these methods on carotid plaque burden during long-term follow-up (117). Gao et al. enrolled 363 patients with sICAS in China and divided them into a training set (n = 254) and a test set (n = 109). A trans-CNN architecture was constructed, and its ability to predict the risk of stroke recurrence in patients with sICAS was analyzed and compared with those of two other CNNs (ResNet50 and DenseNet169). The proposed Trans-CNN model achieved an AUC of 0.951 on the training set and 0.912 on the test set. The Trans-CNN had better AUC improvement than the other two CNN models did. These results indicate that the automated detection of culprit plaque characteristics via DL can help identify lesions with a greater likelihood of causing recurrent stroke (118).

The morphological characteristics of IAs serve as important predictors of rupture risk. On the basis of 99 cases containing 120 aneurysms (91 unruptured, 29 ruptured), Ou et al. developed a backbone network of a 3D ResNet18 followed by a fully connected layer. This model achieved an AUC of 0.853, a sensitivity of 0.893, a specificity of 0.838, a PPV of 0.692, an NPV of 0.967, and an accuracy of 0.853. Further integration of clinical data with deep embedding features improved the model’s AUC to 0.853. When neurosurgeons used this system for decision support, their ability to predict rupture risk performance also improved, with AUC values increasing from 0.877 to 0.945. These results not only support the adoption of DL models in clinical settings but also present a viable framework for combining HR-VWI radiomics with DL (119).

Predicting long-term clinical outcomes on the basis of early AIS might provide essential information for its clinical management. Several studies have developed DL models for better predicting the clinical outcomes of AIS patients. Liu et al. enrolled 640 patients with AIS. They proposed a 3D ResNet model integrated with clinical variables. This fused model exhibited superior performance in predicting the 90-day modified Rankin scale (mRS) score in patients with AIS, achieving an AUC of 0.92 for poor outcomes, with significantly lower error than standalone clinical or imaging models (120). Li et al. retrospectively enrolled 363 patients with sICAS in China and collected HR-VWI images. Among those patients, 21.76% experienced stroke recurrence (79 patients), and the remaining 78.24% did not experience recurrence (284 patients). The training/validation set included 290 patients, and the test set included 73 patients. The cohort was divided into training/validation sets (n = 290) and a test set (n = 73). A combined prediction model was developed by integrating automated multiview deep feature learning and DeepSurv-based survival analysis. This model achieved a C-index of 0.872 in the internal validation set and 0.803 in the external test set; additionally, the AUCs of predictive accuracy for 1-, 2-, and 3-year recurrence rates were 0.841, 0.870, and 0.802, respectively (121). These results highlight how combining the DL with the HR-VWI enables a more personalized and accurate prognosis in patients with ischemic stroke.

The management of cerebrovascular diseases has traditionally relied on empirical judgment and qualitative imaging. Currently, the integration of DL with HR-VWI is shifting this paradigm toward intelligent decision support and dynamic monitoring. For example, DL models based on HR-VWI can accurately predict IA rupture risk and stroke recurrence risk without the need for invasive procedures (119, 121). For example, Simon et al. enrolled 23 patients with AVM. HR-MRI/MRA images were collected. A DL model was developed on the basis of the 2D U-Net architecture, which achieved good classification performance for arteries, veins, brain parenchyma, and cerebrospinal fluid (Dice coefficients of 0.86, 0.91, 0.98, and 0.91, respectively) (122). In a multicenter study, Peng et al. enrolled 236 patients with 266 intracranial aneurysms (IAs) in China. The ML model (4D-Flow logistic regression, 4D-Flow-LR) and the DL model (Multicrop Attention Branch Network, MicroAB-Net) were used to predict the instability of IAs on the basis of the aneurysmal wall and aneurysm morphology-related features. 4D-Flow-LR achieved an AUC of 0.809, and MicroAB-Net achieved an AUC of 0.807. The hybrid model, which combines 4D-Flow-LR and MicroAB-Net, achieves the best performance, with an AUC value of 0.854 (123). Similarly, Tao et al. conducted a multicenter study enrolling 193 patients who underwent HR-MR-VWI examination in China. A DL model, which was named Res U-net, achieved DSCs of 0.936 (outer wall area), 0.942 (lumen area), and 0.845 (vessel wall region) on the internal test set. Robust performance was also identified in the external test set. The model achieved DSCs of 0.928 (outer wall area), 0.936 (lumen area), and 0.844 (vessel wall region) (124). Taken together, DL-based methods enable precise and consistent vessel wall segmentation in clinical HR-VWI, providing a practical approach to simplify cerebrovascular risk assessment and assist decision-making for stroke prevention and monitoring.

The primary constraint for the clinical translation of HR-VWI combined with DL lies in the scarcity of high-quality data and the difficulty of fine-grained annotation. The acquisition of HR-VWI images has high requirements for sequences, parameters, and patient cooperation. Data from different manufacturers, magnetic field strengths, and scanning protocols exhibit significant heterogeneity, which further increases the difficulty of multicenter data integration and model generalization. More importantly, the identification of vascular wall abnormalities (such as thin fibrous caps, IPH, and focal enhancement) often requires joint review by senior neuroradiologists or vascular surgeons. Subjective differences exist among different reviewers, resulting in high costs for “gold standard” annotations and challenges in establishing large-scale, highly consistent annotated databases. This highly specialized annotation process is both expensive and time-consuming, severely restricting the construction of large-scale datasets. Data imbalance is also a prominent issue. The insufficient number of samples for certain rare lesions or specific plaque types may cause the model to tend to learn the features of the majority class. In addition, legal and ethical constraints related to patient privacy and data sharing, as well as technical or compliance barriers in cross-institutional data transmission and storage, limit the establishment of multicenter, open datasets.

Although DL demonstrates excellent performance in image recognition and segmentation tasks, its “black-box” nature remains one of the major obstacles to its clinical adoption. Clinicians need to understand the basis for the conclusions provided by the model during the decision-making process—especially with respect to selecting surgical or interventional treatments. The lack of interpretability weakens clinical trust and increases uncertainty regarding responsibility attribution. In addition, the generalizability and robustness of DL models are insufficient. For example, building a DL model typically depends on precise extraction of the vessel centerline for coordinate transformation; however, inaccuracies in centerline alignment can negatively affect segmentation performance. The model generally presumes that vessels have regular geometries, making it less suitable for cases involving tortuous or irregular vascular structures. This shortcoming is especially significant for certain groups—such as older adults or patients with vascular diseases—where preexisting infarcts, chronic vessel remodeling, vasculitis, or dolichoectasia can lead to atypical vascular anatomy. Future research that adopts adaptive transformation methods may help overcome these issues and enhance the model’s generalizability for complex vascular anatomies (124).

Furthermore, the model’s sensitivity to fluctuations in image quality, artifacts, and noise also poses challenges. In real clinical settings, image quality degradation caused by various factors (e.g., patient motion, equipment calibration errors) can affect the stable performance of the model. Limitations may also exist in the model architecture itself. Many existing methods fail to fully exploit the 3D spatial information contained in HR-VWI data and the complementary information among multicontrast sequences, which restricts the model’s ability to characterize complex vascular lesions. If these algorithm-level flaws are not addressed, they severely hinder the reliable application of DL in real clinical environments.

Insufficient data in the training, validation, or test sets may limit the global deployment of DL models. The sizes (or number of cases) in the training set are often determined on the basis of multichannel images for tissue segmentation. Many factors, such as the type of tissue and the acquisition protocol, can affect the minimum training size. Without the minimum training size needed for accurate analysis, the results may not be completely generalizable.

The integration of DL and HR-VWI technologies from research platforms to clinical tools faces multidimensional institutional and practical barriers. At this stage, the vast majority of studies are retrospective or single-center validations, lacking large-scale prospective, multicenter clinical trials to demonstrate their actual value in improving the clinical outcomes of patients from different hospitals or countries. However, regulatory authorities and hospital procurement decisions often rely on such high-quality evidence. Regulatory approval and the lack of standardization also represent important barriers. Currently, there is no clear regulatory pathway for approving DL-based HR-VWI analysis tools as medical devices. Moreover, the industry lacks unified technical standards and evaluation norms, making it difficult for these tools to enter clinical use in a legal and compliant manner. In addition, seamlessly integrating DL tools into existing radiology workflows and hospital information systems—without increasing the workload of clinicians—involves issues such as interoperability between IT systems (e.g., PACS, RIS, and electronic medical records) and DL systems, data interface specifications, real-time performance requirements, and information security. Furthermore, clinicians’ acceptance and willingness to use these tools are key factors. Clinicians may adopt a cautious attitude toward new technologies or lack sufficient training to correctly understand and apply the output results of these tools. Cost-effectiveness considerations also cannot be ignored. Deploying and maintaining DL systems requires considerable resource investment, and whether they can lead to corresponding improvements in clinical benefits and economic value still needs further verification. Therefore, promoting clinical translation requires collaborative efforts in evidence generation, regulatory compliance, information system integration, user training, and economic evaluation.

Advancing clinically useful HR-VWI, ML, and DL models will require progress on both the technical and study design fronts. Technically, priority should be given to building multimodal frameworks that combine HR-VWI with perfusion imaging, hemodynamics, and clinical data to improve pathological assessment and risk prediction. Approaches such as self-supervised pretraining, semisupervised learning, and federated learning can strengthen model generalizability on limited or diverse datasets while safeguarding privacy. Interpretability research should advance alongside model performance, with visual, traceable outputs for key features such as plaque enhancement or intraplaque hemorrhage. Additionally, unified evaluation indicators, benchmark datasets, and open challenges should be established to facilitate objective comparisons of different models and methods. For clinical deployment, attention should also be given to the development of continuous performance monitoring mechanisms, model retraining strategies, and regulatory compliance pathways. This creates a closed-loop framework spanning technical development, clinical validation, and regulatory approval. The long-term objective is a multimodal precision medicine ecosystem built around HR-VWI and powered by DL—one that enables individualized cerebrovascular disease management while maintaining safety and clinical interpretability.

Population diversity, which encompasses demographic, ethnic, geographic, and socioeconomic variations, is a core factor that shapes the generalizability, accuracy, and clinical translation of DL and HR-VWI in the assessment of various intracranial vascular lesions. DL models for cerebral vascular image analysis are predominantly trained on homogeneous or hospital-specific cohorts, which lack representation of the full spectrum of population diversity. This leads to three major clinical limitations: (1) Reduced performance in underrepresented populations. Ethnic/racial variations in cerebral vascular anatomical features (e.g., MMD and cerebrovascular atherosclerosis incidence, arterial calibre, and intracranial artery tortuosity), as well as geographic differences in disease risk factors (e.g., hypertension, diabetes, and smoking prevalence), cause DL models to exhibit lower accuracy and higher false positive/negative rates when applied to non-Caucasian, low-resource region, or elderly/pediatric cohorts. For example, models trained on Asian populations may fail to detect small vessel occlusion accurately in European stroke patients with distinct vascular morphologies. (2) Exacerbation of clinical diagnostic disparities. Socioeconomic diversity further amplifies this issue—populations with limited access to high-quality imaging or early screening have underrepresented data in training sets, leading DL models to perform poorly for these groups and widening gaps in diagnostic equity for cerebral vascular diseases across different socioeconomic strata; (3) population diversity also includes institutional/scanner diversity (a practical subset in clinical imaging), where variations in imaging equipment, postprocessing workflows, and radiologist annotation standards across hospitals create a “domain shift”—a direct consequence of population diversity in data collection—and render model performance inconsistent across clinical centers.

Biological heterogeneity also has a great impact on the clinical application of DL models. Cerebral vascular diseases exhibit extreme biological heterogeneity: (1) Interindividual differences in disease etiology. e.g., ischemic stroke from cardioembolism vs. large artery atherosclerosis; (2) pathological manifestations. e.g., aneurysm size, location, and wall enhancement; (3) intraindividual changes in disease progression. e.g., stroke lesion evolution and posttreatment vascular remodeling; (4) comorbidity profiles. e.g., cerebral vascular disease combined with dementia, heart disease, or renal failure.

These heterogeneities pose fundamental challenges to DL models: (1) Degraded performance in complex pathological scenarios. Since DL models rely on learned patterns from labeled disease features, biological heterogeneity leads to feature variability (such as diverse stroke lesion locations/sizes and atypical aneurysm morphologies) that may fall outside the distribution of training data. This results in model uncertainty or misclassification for rare or complex cerebral vascular pathologies, reducing the reliability of automated diagnosis and outcome prediction. (2) Limited validity of personalized outcome prediction. A core clinical application of DL in cerebral vascular disease is for predicting treatment response and clinical outcomes. Biological heterogeneity in individual physiological responses to treatment (e.g., varying antiplatelet drug efficacy, different rates of vascular repair) means that DL models trained on aggregate population data fail to capture individual-specific biological traits, leading to inaccurate personalized predictions and limiting the model’s value for clinical decision-making. (3) Intraindividual temporal heterogeneity. There are dynamic changes in cerebral vascular pathology over time, such as from acute stroke lesion expansion to chronic small vessel disease progression. The process requires DL models to adapt to temporal feature shifts. However, static training data (cross-sectional imaging) cannot capture this heterogeneity, resulting in poor long-term outcome prediction performance.

Population diversity and biological heterogeneity do not act independently—their synergy further complicates the clinical application of DL models. For example, a specific ethnic population (population diversity) may have a greater prevalence of a rare pathological subtype of cerebral vascular disease (biological heterogeneity); the combined underrepresentation of this group in training data leads to severe model underperformance that cannot be attributed to either factor alone. Additionally, population-level differences in risk factor exposure (e.g., high hypertension prevalence in a certain geographic region) drive biological heterogeneity in disease severity and manifestation, creating a feedback loop that deepens the mismatch between DL models and real-world clinical populations. This synergy makes it difficult to isolate and address model limitations, significantly hindering the seamless translation of DL-based image analysis from the laboratory to clinical practice.

Despite being major challenges, population diversity and biological heterogeneity act as critical drivers for advancing DL-based HR-VWI analysis. There are several potential strategies for overcoming such challenges: (1) Demand for diverse and representative training datasets. The limitations imposed by population diversity have spurred global efforts to construct multicenter, multiethnic, and cross-geographic imaging datasets for cerebral vascular diseases, including standardized annotation and harmonization of imaging protocols to reduce domain shifts. (2) Development of heterogeneity-aware DL models. To address biological heterogeneity, advanced DL architectures (e.g., attention mechanisms, graph neural networks, and multimodal fusion models) can capture complex, heterogeneous pathological features and individual-specific biological traits. Additionally, personalized DL models (e.g., transfer learning, few-shot learning, and federated learning) are being developed to adapt to rare disease subtypes and intraindividual temporal changes, improving the accuracy of diagnosis and outcome prediction for heterogeneous clinical cases. (3) Integration of clinical and biological metadata. Population diversity and biological heterogeneity highlight the need to move beyond pure image-based DL models to integrate multimodal data (e.g., clinical demographics, genetic information, blood biomarkers, and lifestyle factors). This integration enables models to account for population-level variations and individual biological heterogeneity, enhancing the interpretability and predictive validity of DL-based analysis for clinical decision-making.