Intracranial Aneurysms are a prevalent vascular pathology present in 3-4% of the population with an inherent risk of rupture. The growing accessibility of angiography has led to a rising incidence of detected aneurysms. An accurate assessment of the rupture risk is of utmost importance for the very high disability and mortality rates in case of rupture and the non-negligible risk inherent to surgical treatment. However, human evaluation is rather subjective, and current treatment guidelines, such as the PHASES score, remain inefficient. Therefore we aimed to develop an automatic machine learning-based rupture prediction model. Our study utilized 686 CTA scans, comprising 844 intracranial aneurysms. Among these aneurysms, 579 were classified as ruptured, while 265 were categorized as non-ruptured. Notably, the CTAs of ruptured aneurysms were obtained within a week after rupture, during which negligible morphological changes were observed compared to the aneurysm’s pre-rupture shape, as established by previous research. Based on this observation, our rupture risk assessment focused on the models’ ability to classify between ruptured and unruptured IAs. In our investigation, we implemented an automated vessel and aneurysm segmentation, vessel labeling, and feature extraction framework. The rupture risk prediction involved the use of deep learning-based vessel and aneurysm shape features, along with a combination of demographic features (patient sex and age) and morphological features (aneurysm location, size, surface area, volume, sphericity, etc.). An ablation-type study was conducted to evaluate these features. Eight different machine learning models were trained with the objective of identifying ruptured aneurysms. The best performing model achieved an area under the receiver operating characteristic curve (AUC) of 0.833, utilizing a random forest algorithm with feature selection based on Spearman’s rank correlation thresholding, which effectively eliminated highly correlated and anti-correlated features...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook Bibliography / Intrakranielle Aneurysmen sind eine weit verbreitete vaskuläre Pathologie, die bei 3 bis 4% der Bevölkerung auftritt und ein inhärentes Rupturrisiko birgt. Mit der zunehmenden Verfügbarkeit von Angiographie wird eine steigende Anzahl von Aneurysmen entdeckt. Angesichts der sehr hohen permanenten Beeinträchtigungs- und Sterblichkeitsraten im Falle einer Ruptur und des nicht zu vernachlässigenden Risikos einer chirurgischen Behandlung ist eine genaue Bewertung des Rupturrisikos von größter Bedeutung. Die Beurteilung durch den Menschen ist jedoch sehr subjektiv, und die derzeitigen Behandlungsrichtlinien, wie der PHASES-Score, sind nach wie vor ineffizient. Daher wollten wir ein automatisches, auf maschinellem Lernen basierendes Modell zur Rupturvorhersage entwickeln. Für unsere Studie wurden 686 CTA-Scans von 844 intrakraniellen Aneurysmen verwendet, von denen 579 rupturiert waren und 265 nicht rupturiert waren. Dabei ist zu beachten, dass die CTAs der rupturierten Aneurysmen innerhalb einer Woche nach der Ruptur gewonnen wurden, in der im Vergleich zur Form des Aneurysmas vor der Ruptur nur geringfügige morphologische Veränderungen zu beobachten waren, wie in vorhergegangenen Studient festgestellt wurde. Im Rahmen unserer Untersuchung haben wir eine automatische Segmentierung von Adern und Aneurysmen, ein Aderlabeling und eine Merkmalsextraktion implementiert. Für die Vorhersage des Rupturrisikos wurden auf Deep Learning basierende Ader- und Aneurysmaformmerkmale zusammen mit einer Kombination aus demografischen Merkmalen (Geschlecht und Alter des Patienten) und morphologischen Merkmalen (u. A. Lage, Größe, Oberfläche, Volumen, Sphärizität des Aneurysmas) verwendet. Zur Bewertung dieser Merkmale wurde eine Ablationsstudie durchgeführt. Acht verschiedene maschinelle Lernmodelle wurden mit dem Ziel trainiert, rupturierte Aneurysmen zu erkennen...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook Bibliography