Deep shape features for predicting future intracranial aneurysm growth

Bizjak, Žiga (avtor), Pernuš, Franjo (avtor), Špiclin, Žiga (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: intracranial aneurysm, growth prediction, vascular disease, deep learning, classification, morphological features
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: 10 str.
• Številčenje: Vol. 12, art. 644349
• PID: 20.500.12556/RUL-144659
• UDK: 004.8:616.13-007.64
• ISSN pri članku: 1664-042X
• DOI: 10.3389/fphys.2021.644349
• COBISS.SI-ID: 69012739
• Datum objave v RUL: 07.03.2023

Gradivo je del revije

• Naslov: Frontiers in physiology
• Skrajšan naslov: Front. physiol.
• Založnik: Frontiers Research Foundation
• ISSN: 1664-042X
• COBISS.SI-ID: 1218939

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: intrakranialne anevrizme, predikcija rasti, žilne bolezni, globoko učenje, klasifikacija, morfološke značilnosti