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Data Driven Models Merging Geometric, Biomechanical, and Clinical Data to Assess the Rupture of Abdominal Aortic Aneurysms
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Material and Structural Mechanics.ORCID iD: 0000-0001-6544-628X
Department of Molecular Medicine and Surgery, KI Karolinska Institute, Stockholm, Sweden.
Department of Molecular Medicine and Surgery, KI Karolinska Institute, Stockholm, Sweden.
Vascular Surgery Medical Faculty, University of Augsburg, Augsburg, Germany.
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2025 (English)In: European Journal of Vascular and Endovascular Surgery, ISSN 1078-5884, E-ISSN 1532-2165, Vol. 70, no 5, p. 591-600Article in journal (Refereed) Published
Abstract [en]

Objective: Despite elective repair of a large portion of stable abdominal aortic aneurysms (AAAs), the diameter criterion cannot prevent all small AAA ruptures. Since rupture depends on many factors, this study explored whether machine learning (ML) models (logistic regression [LogR], linear and non-linear support vector machine [SVM-Lin and SVM-Nlin], and Gaussian Naïve Bayes [GNB]) might improve the diameter based risk assessment by comparing already ruptured (diameter 52.8 – 174.5 mm) with asymptomatic (diameter 40.4 – 95.5 mm) aortas. Methods: A retrospective case-control observational study included ruptured AAAs from two centres (2010 – 2012) with computed tomography angiography images for finite element analysis. Clinical patient data and geometric and biomechanical AAA properties were fed into ML models, whose output was compared with the results from intact cases. Classifications were explored for all cases and those having diameters below 70 mm. All data trained and validated the ML models, with a five-fold cross-validation. SHapley Additive exPlanations (SHAP) analysis ranked the factors for rupture identification. Results: One hundred and seven ruptured (20.6% female, mean age 77 years, mean diameter 86.3 mm) and 200 non-ruptured aneurysmal infrarenal aortas (21.5% female, mean age 74 years, mean diameter 57 mm) were investigated through cross-validation methods. Given the entire dataset, the diameter threshold of 55 mm in males and 50 mm in females provided a 58.0% accurate rupture classification. It was 99.1% sensitive (AAA rupture identified correctly) and 36.0% specific (intact AAAs identified correctly). ML models improved accuracy (LogR 90.2%, SVM-Lin 89.5%, SVM-Nlin 88.7%, and GNB 86.4%); accuracy decreased when trained on the ≤ 70 mm group (55/50 mm diameter threshold 44.2%, LogR 82.5%, SVM-Lin 83.6%, SVM-Nlin 65.9%, and GNB 84.7%). SHAP ranked biomechanical parameters other than the diameter as the most relevant. Conclusion: A multiparameter estimate enhanced the purely diameter-based approach. The proposed predictability method should be further tested in longitudinal studies.

Place, publisher, year, edition, pages
Elsevier BV , 2025. Vol. 70, no 5, p. 591-600
Keywords [en]
Abdominal aortic aneurysm, Aortic rupture, Artificial intelligence, Machine learning, Prognosis, Surgery
National Category
Cardiology and Cardiovascular Disease Surgery
Identifiers
URN: urn:nbn:se:kth:diva-371191DOI: 10.1016/j.ejvs.2025.06.002ISI: 001622680000009PubMedID: 40484216Scopus ID: 2-s2.0-105016624442OAI: oai:DiVA.org:kth-371191DiVA, id: diva2:2004210
Note

QC 20251217

Available from: 2025-10-07 Created: 2025-10-07 Last updated: 2025-12-29Bibliographically approved
In thesis
1.
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2. In-Vitro Testing and Numerical Modelling towards Uncovering Aortic Wall Fracture Mechanisms
Open this publication in new window or tab >>In-Vitro Testing and Numerical Modelling towards Uncovering Aortic Wall Fracture Mechanisms
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cardiovascular pathologies such as aortic aneurysm and dissection remain one of the leading causes of mortality worldwide. Current clinical standards for assessing rupture risk in aneurysmal aortas rely primarily on external diameter and its growth rate, despite the inherently multifactorial nature of rupture. Although tissue fracture plays a crucial role in the onset and progression of vascular diseases, understanding in this area remains limited. The hierarchical histological structure of vascular tissue gives rise to complex mechanical behaviour, while existing experimental protocols for soft tissue fracture are often inadequate for a sound characterisation of the fracture response.

A comprehensive understanding of fracture requires the assessment of fracture mechanisms and the quantification of key parameters, including resistance to rupture and the size of the fracture process zone. Concerning biological soft tissue, most mechanistic information stems from studies on skin, which is extremely resistant to fracture. However, the histological structure of vascular tissue differs from that of skin, and impedes the translation of such information. Moreover, the influence of clinical factors on the mechanics of diseased vessel walls cannot be ignored, as focusing solely on normal tissue may yield clinically irrelevant estimates of mechanical properties. Bridging engineering fracture mechanics with medical application thus represents both a critical and challenging task.

A major part of this thesis was dedicated to the design and application of a fracture test experiment, the symmetry-constraint Compact Tension (symconCT) test. The setup enabled a stable propagation of the crack in a pre-notched specimen orthogonal to the loading direction. Investigations could be carried out up to complete rupture of the specimen, and image analysis captured local mechanisms at the fracture tip. Pronounced rounding/flattening of the crack notch, called blunting, characterised the fracture. Besides, the study demonstrated the strong dependence of crack morphology on loading orientation relative to fiber alignment. Despite a slow displacement rate being applied, the experiments revealed significant strain-rate effects ahead of the notch. The protocol allowed testing of both normal porcine tissue and human aneurysmal aorta, with results linking fracture properties to clinical and histological data. Collagen content increased fracture resistance, while energy dissipation decreased with age, underscoring the relevance of patient-specific factors in rupture prediction. To further validate this hypothesis, mechanical, geometrical, and clinical information were integrated through different machine learning models to assess abdominal aortic aneurysms' rupture. The models outperformed the clinical standard, revealing that rupture identification depends on multiple interacting factors rather than any single dominant parameter.

Based on the experimental data, finite element models were developed to simulate the fracture behaviour during the symconCT test. Elastic and fracture properties were identified at a specimen-specific level, exploring two different methods to fracture: the cohesive zone and phase-field approaches. As the fracture resistance (strength) of notched specimens was significantly lower than that of unnotched tensile specimens, this indicates that conventional tests on flawless tissue overestimate fracture properties, especially in diseased tissues, which contain microvoids and microdamage. Future work should aim to simulate entire vessel walls using patient-specific geometries and boundary conditions.

The combined experimental and computational framework in this thesis advanced the understanding of the fracture processes and mechanical behaviour of the aortic vessel wall. It provided essential groundwork for patient-specific rupture risk prediction, supported the translation of biomechanics into clinical decision-making, and paved the way for future studies addressing more realistic and complex physiological scenarios.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2025. p. 153
Series
TRITA-SCI-FOU ; 2025:74
Keywords
Fracture mechanics, aorta, aneurysm, machine learning, material modelling
National Category
Solid and Structural Mechanics
Research subject
Solid Mechanics
Identifiers
urn:nbn:se:kth:diva-374824 (URN)978-91-8106-495-7 (ISBN)
Public defence
2026-01-16, Kollegiesalen, Brinellvägen 8, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2020–04447
Note

QC 251229

Available from: 2025-12-29 Created: 2025-12-25 Last updated: 2025-12-29Bibliographically approved

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Alloisio, MartaGasser, T. Christian

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