20.500.12556/RUL-174956 Prediction of intracranial aneurysm rupture from computed tomography angiography using an automated artificial intelligence framework 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. intracranial aneurysm subarachnoid hemorrhage rupture machine learning deep learning intrakranialna anevrizma možganska kap ruptura globoko učenje strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-10-10 14:00:21 2025-10-10 14:00:28 2025-12-02 04:08:11 0000-00-00 00:00:00 2025 0 0 8 str. part A, art. 110965 Vol. 197 Oct. 2025 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.85 1879-0534 10.1016/j.compbiomed.2025.110965 252732419 1-s2.0-S0010482525013174-main.pdf 1-s2.0-S0010482525013174-main.pdf 1 866869F602C1B522E224ACBBAA30645E d19e2dfd80fd7c99cd919ec711336ad65b9d915f49b5251d3a7647c97ae96c56 b2bd5e7e-a5d0-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=219867 https://www.sciencedirect.com/science/article/pii/S0010482525013174 1 794dddfa-a5d0-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=219866 Fakulteta za elektrotehniko 0 0 0