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Data Driven Models Merging Geometric, Biomechanical, and Clinical Data to Assess the Rupture of Abdominal Aortic
Marta Alloisio1, Antti Siika2, Joy Roy2
1Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.
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.
