Intracranial aneurysms (IAs) are common vascular pathologies with a risk of fatal rupture. Human assessment of rupture risk is error prone, and treatment decision for unruptured IAs often rely on expert opinion and institutional policy. Therefore, we aimed to develop a computer-assisted aneurysm rupture prediction framework to help guide the decision-making process and create future decision criteria. This retrospective study included 335 patients with 500 IAs, of the 500 IAs studied, 250 were labeled as ruptured and 250 as unruptured. A skilled radiologist and a neurosurgeon visually examined the computed tomography angiography (CTA) images and labeled the IAs. For external validation we included 24 IAs, 10 ruptured and 15 unruptured, imaged with 3D rotational angiography (3D-RA) from the Aneurisk dataset. The pretrained nnU-net model was used for automated vessel segmentation, which was fed to pretrained PointNet++ models for vessel labeling and aneurysm segmentation. From these the latent keypoint representations were extracted as vessel shape and aneurysm shape features, respectively. Additionally, conventional features such as IAs morphological measurements, location and patient data, such as age, sex, were used for training and testing eight machine learning models for rupture status classification. The top-performing model, a random forest with feature selection, achieved an area under the receiver operating curve of 0.851, an accuracy of 0.782, a sensitivity of 0.804, and a specificity of 0.760. This model used 14 aneurysm shape features, seven conventional features, and one vessel shape feature. On the external dataset, it achieved an AUC of 0.805. While aneurysm shape features consistently contributed significantly across the classification models, vessel shape features contributed a small portion. Our proposed automated artificial intelligence framework could assist in clinical decision-making by assessing aneurysm rupture risk using screening tests, such as CTA and 3D-RA.

Despite achieving high volumetric overlap, pixel-based deep learning segmentation approaches do not ensure true vascular connectivity, leading to fragmentations structurally analogous to Branch Retinal Artery Occlusions. This fundamental deficiency means that even segmentations achieving high pixel-wise overlap metrics (such as the Dice Score) do not guarantee or improve true vascular connectivity, rendering them unreliable for quantitative analysis and subsequent clinical risk stratification of high-risk systemic events, such as stroke. To resolve this, we introduce the Differentiable Endpoint Connectivity Loss (DECL), a novel, fully differentiable loss function that directly optimizes vascular continuity by precisely targeting errors in vessel endpoints. DECL leverages a differentiable soft-skeletonization module to impose two specialized penalties: a Normalized Count Penalty (L_c), which rigorously penalizes the discrete difference in the number of predicted versus true endpoints (ΔN_p), and a Normalized Distance Loss (L_d), which penalizes the Euclidean distance between the weighted centers of the true and predicted endpoints. In comparative experiments across four datasets (DRIVE, CHASE DB1, STARE, and OCTA-500), we tested DECL against established methods including Dice CE, clDice, cbDice, and SAC Loss. DECL and its hybrid variant, DECL_clDice, consistently recorded the highest mean Dice Score and clDice scores, specifically, DECL_clDiced improved the mean clDice score from 0.839 to 0.851 on CHASE DB1 and from 0.836 to 0.842 on DRIVE, while minimizing Betti Number Error (β-Error) andVariation of Information. The DECL method establishes a robust, mathematically justified framework for generating high-fidelity vascular maps, essential for accurate biomarker quantification and reliable clinical assessment.

In structural engineering, the iterative optimisation of complex timber wall components is fundamentally limited by the prohibitive computational costs of non-linear Finite Element Analysis (FEA). To overcome this bottleneck, this study introduces a highly efficient machine learning-based surrogate model utilising a convolutional encoder–decoder architecture to predict the global buckling load factor (BLF) directly from structural topology. We evaluate three distinct modeling strategies: a Direct prediction model, a Sequential model, and a multitask Dual-Loss model designed to simultaneously predict the BLF and reconstruct spatial tensile stress fields. Experimental results demonstrate that both the Direct and Dual-Loss approaches achieve near-perfect predictive accuracy on in-distribution data, yielding a coefficient of determination (R²) of approximately 0.996. Crucially, these surrogates accelerate inference times by a factor of roughly 12,800 compared to traditional iterative solvers, reducing evaluation times from hours to mere seconds. Furthermore, the models exhibit exceptional robustness and extrapolative capability under rigorous out-of-distribution testing. The models maintain high predictive fidelity when subjected to cross-dataset distributional shifts (R² $\approx 0.94$) and when evaluating intentionally low-performing, highly vulnerable configurations (R²$\approx 0.967$). Extensive validation on structurally disjoint, hold-out wall geometries confirms the models’ ability to generalise to entirely unseen topologies without introducing systematic bias (R²$>0.95$). By successfully internalising the underlying physical principles of load redistribution, this surrogate framework provides a reliable, computationally inexpensive foundation for enabling real-time, autonomous generative design and structural optimisation.