20.500.12556/RUL-144659 Deep shape features for predicting future intracranial aneurysm growth Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support. Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model. Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively. Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging. intracranial aneurysm growth prediction vascular disease deep learning classification morphological features intrakranialne anevrizme predikcija rasti žilne bolezni globoko učenje klasifikacija morfološke značilnosti true false true Angleški jezik Slovenski jezik Članek v reviji 2023-03-07 11:02:22 2023-03-07 11:02:24 2023-03-08 03:33:17 0000-00-00 00:00:00 2021 0 0 10 str. art. 644349 Vol. 12 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8:616.13-007.64 1664-042X 10.3389/fphys.2021.644349 69012739 fphys-12-644349.pdf fphys-12-644349.pdf 1 757C134ED394DA9D4E6C9A97FF3C42A0 3ca8a12bcdb9a2e478f4acc3604a6e702e1b9a5991ff0f8ad7d83d78a5612341 6ba5b366-bccf-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167110 https://www.frontiersin.org/articles/10.3389/fphys.2021.644349/full 1 214b7293-bccf-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167108 Fakulteta za elektrotehniko 0 0 0