Deep shape features for predicting future intracranial aneurysm growth

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

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.

• Language: English
• Keywords: intracranial aneurysm, growth prediction, vascular disease, deep learning, classification, morphological features
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: 10 str.
• Numbering: Vol. 12, art. 644349
• PID: 20.500.12556/RUL-144659
• UDC: 004.8:616.13-007.64
• ISSN on article: 1664-042X
• DOI: 10.3389/fphys.2021.644349
• COBISS.SI-ID: 69012739
• Publication date in RUL: 07.03.2023

Record is a part of a journal

• Title: Frontiers in physiology
• Shortened title: Front. physiol.
• Publisher: Frontiers Research Foundation
• ISSN: 1664-042X
• COBISS.SI-ID: 1218939

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: intrakranialne anevrizme, predikcija rasti, žilne bolezni, globoko učenje, klasifikacija, morfološke značilnosti