Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Microstructural imaging enables researchers to visualise changes in the arterial wall, allowing for (i) a deeper understanding of the role of specific components in arterial mechanics, (ii) the observation of cellular responses, (iii) insights into pathological alterations in tissue microstructure, and/or (iv) advancements in tissue engineering aimed at replicating healthy native tissue. In this prospective review, we present various imaging modalities spanning from ex vivo to in vivo applications within arterial tissue. The pros, cons, and sensitivities of these modalities are highlighted. By consolidating the latest advancements in microstructural imaging of arterial tissue, the authors aim for this paper to serve as a guide for researchers designing experiments at various stages. Furthermore, the integration of non-invasive, non-destructive imaging techniques into studies provides an additional layer of microstructural information, enhancing scientific findings, improving our understanding of disease, and potentially enabling earlier or more effective diagnostic capabilities. STATEMENT OF SIGNIFICANCE: Microstructural imaging enables researchers to visualise changes in the arterial wall, allowing for (i) a deeper understanding of the role of specific components in arterial mechanics, (ii) the observation of cellular responses, (iii) insights into pathological alterations in tissue microstructure, and/or (iv) advancements in tissue engineering aimed at replicating healthy native tissue. By consolidating the latest advancements in microstructural imaging of arterial tissue, the authors aim for this paper to serve as a guide for researchers designing experiments at various stages. Furthermore, the integration of non-invasive, non-destructive imaging techniques into studies provides an additional layer of microstructural information, enhancing scientific findings, improving our understanding of disease, and potentially enabling earlier or more effective diagnostic capabilities.

The clinical decision to establish if a patient with carotid disease should undergo surgical intervention is primarily based on the percent stenosis. Whilst this applies for high-grade stenosed vessels (>70%), it falls short for other cases. Due to the heterogeneity of plaque tissue, probing the mechanics of the tissue would likely provide further insights into why some plaques are more prone to rupture. Mechanical characterization of such tissue is nontrivial, however, due to the difficulties in collecting fresh, intact plaque tissue and using physiologically relevant mechanical testing of such material. The use of polyvinyl alcohol (PVA) cryogel is thus highly convenient because of its acoustic properties and tunable mechanical properties. Methods. The aim of this study is to demonstrate the potential of PVA phantoms to simulate atherosclerotic features. In addition, a testing and simulation framework is developed for full PVA vessel material characterization using ring tensile testing and inflation testing combined with non-invasive ultrasound imaging and computational modeling. Results. Strain stiffening behavior was observed in PVA through ring tensile tests, particularly at high (n = 6) freeze–thaw cycles (FTCs). Inflation testing of bi-layered phantoms featuring lipid pool inclusions demonstrated high strains at shoulder regions. The application of an inverse finite element framework successfully recovered boundaries and determined the shear moduli for the PVA wall to lie within the range 27–53 kPa. Conclusion. The imaging-modeling framework presented facilitates the use and characterization of arterial mimicking phantoms to further explore plaque rupture. It also shows translational potential for non-invasive mechanical characterization of atherosclerotic plaques to improve the identification of clinically relevant metrics of plaque vulnerability.

A detailed understanding of cerebral artery biomechanics is essential for advancing cerebrovascular research and neurovascular device development. Yet, despite the clinical relevance of these vessels, high-fidelity experimental data remains extremely limited. As a result, current device development is often informed by data from non-cerebral vessels or animals. To address this gap, this study characterized the mechanical behavior and structural composition of human middle cerebral arteries obtained from six donors. Ring-extension testing was used to quantify elastin- and collagen-dominant region stiffnesses, while histological staining was used to assess elastin, collagen, and smooth muscle cell (SMC) content. Histology revealed that, unlike large elastic arteries, cerebral arteries are dominated by SMCs, contain sparse elastin, and lack a distinct external elastic lamina. Comparative analysis showed that mechanical behavior could not be inferred from composition alone, highlighting the importance of considering tissue integrity and organization when assessing structure. To explore clinically relevant differences, samples were grouped by cardiovascular disease (CVD) status and arterial branch type (M1 vs. M2). CVD-affected arteries exhibited significantly higher elastin-dominant region stiffness and reduced medial SMC content (p < 0.05). M2 branches showed significantly lower collagen-dominant region stiffness, internal elastic lamina fraction, and adventitial collagen content compared to M1 branches (p < 0.05). These findings highlight the structural and mechanical heterogeneity of human cerebral arteries and suggest that neurovascular device design and deployment strategies might benefit from considering both disease state and anatomical location. By jointly evaluating mechanics and composition, this study provides a foundational dataset to guide future cerebrovascular research and device development.

The clinical decision to establish if a patient with carotid disease should undergo surgical intervention is primarily based on the percent stenosis. Whilst this applies for high-grade stenosed vessels (>70%), it falls short for other cases. Due to the heterogeneity of plaque tissue, probing the mechanics of the tissue would likely provide further insights into why some plaques are more prone to rupture. Mechanical characterisation of such tissue is nontrivial, however, due to the difficulties in collecting fresh, intact plaque tissue and using physiologically relevant mechanical testing of such material. The use of polyvinyl alcohol (PVA) is thus highly convenient because of its acoustic properties and tuneable mechanical properties. Methods The aim of this study is to demonstrate the potential of polyvinyl alcohol phantoms to simulate atherosclerotic features. In addition, a testing and simulation framework is developed for full PVA vessel material characterization using ring tensile testing and inflation testing combined with non-invasive ultrasound imaging and computational modelling. Results Strain stiffening behaviour was observed in PVA through ring tensile tests, particularly at high freeze-thaw cycles. Inflation testing of bi-layered phantoms featuring lipid pool inclusions demonstrated high strains at shoulder regions. The application of an inverse finite element framework successfully recovered boundaries and determined the shear moduli for the PVA wall to lie within the range 27 kPa to 53 kPa. Conclusion The imaging-modelling framework presented facilitates the use and characterisation of arterial mimicking phantoms to further explore plaque rupture. It also shows translational potential for non-invasive mechanical characterisation of atherosclerotic plaques to improve the identification of clinically relevant metrics of plaque vulnerability.

Background Neovascularisation, the formation of new vessels within atherosclerotic plaques, has been associated with plaque progression and intraplaque haemorrhage. However, the relationship between neovascularisation and mechanical failure of plaques remains unclear. A better understanding of this link could improve rupture risk prediction beyond traditional reliance on arterial stenosis. Methods Human carotid plaques (n=7) from endarterectomy were sectioned into circumferential strips (n=25) and tested under uniaxial tension with digital image correlation (DIC) to determine ultimate tensile (UT) stress, UT strain, and elastic moduli. Immunohistochemistry with CD31 staining quantified neovascularisation. K-means clustering based on UT stress and strain identified mechanical phenotypes. A decision tree classifier was trained using neovascularisation percentage as the predictor. Results Neovascularisation showed a moderate negative correlation with UT strain (r = -0.46, p = 0.028), indicating plaques with greater neovascularisation failed at lower strains. K-means clustering identified two groups: Cluster 1 (lower UT strain) had significantly higher neovascularisation (median 2.21%) and greater initial stiffness than Cluster 2 (median 1.27%, p < 0.01). The decision tree using neovascularisation alone achieved 78.3% classification accuracy, with an area under the curve of 0.78. DIC analysis revealed rupture consistently occurred at regions of elevated local strain. Conclusion This study demonstrates a link between neovascularisation and mechanical failure in atherosclerotic plaques. Higher neovascularisation was associated with lower UT strain, suggesting it contributes to mechanical instability. These findings support the role of neovascularisation as a marker of plaque vulnerability and encourage its integration into ultrasound-based risk assessment strategies, such as contrast-enhanced ultrasound.