Clinical & Experimental Cardiology
Open Access

Machine Learning Applications in Aortic Aneurysm Risk Prediction

Carlo Basso^{* *}Correspondence: Carlo Basso, Department of Heart and Vascular Center, University of Chicago, Chicago, USA, Email:

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Conclusion

In conclusion, machine learning represents a transformative approach to aortic aneurysm risk prediction by leveraging complex datasets to provide personalized, accurate assessments of aneurysm development, growth, and rupture risk. This technology has the potential to improve early detection, optimize surveillance strategies, and refine surgical decisionmaking. While challenges related to data quality, interpretability, and clinical integration remain, ongoing research and technological advancements are paving the way for machine learning to become an indispensable tool in the management of aortic aneurysms, ultimately improving patient safety and outcomes.

Description

Aortic aneurysms, including Abdominal Aortic Aneurysms (AAAs) and Thoracic Aortic Aneurysms (TAAs), represent a significant health risk due to their often silent progression and the potential for catastrophic rupture. Traditional risk prediction models mainly depend on clinical factors and aneurysm diameter thresholds, such as the commonly used 5.5 cm cutoff for AAAs, to determine intervention strategies. However, these models frequently fall short of accurately predicting individual patient outcomes because aneurysm rupture can occur at smaller sizes and many large aneurysms may remain stable. This unpredictability highlights the urgent need for more sophisticated, personalized risk prediction methods.

Machine Learning (ML), a branch of artificial intelligence that focuses on algorithms capable of learning from and making predictions based on data, has recently emerged as a promising tool for improving the accuracy of aortic aneurysm risk assessment. Unlike traditional statistical techniques that often rely on linear relationships and require explicit model assumptions, ML can handle complex, nonlinear interactions within large and diverse datasets without predetermined equations. The two primary types of machine learning are supervised learning, which uses labeled datasets to train models to predict outcomes, and unsupervised learning, which seeks to uncover patterns or groupings within unlabeled data. Algorithms commonly employed in aneurysm research include decision trees, random forests, Support Vector Machines (SVM), neural networks, and gradient boosting machines. Logistic regression is also frequently used as a baseline model to compare ML performance.

A critical factor for successful ML application in this field is access to high-quality, comprehensive data. Relevant data sources include clinical records containing demographic information and risk factors such as age, hypertension, smoking status, family history, and medication use. Imaging data from modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound provide detailed information on aneurysm morphology, size, and biomechanical properties such as wall stress and calcification. Additionally, genomic data and biomarkers associated with inflammation and extracellular matrix degradation contribute to understanding individual susceptibility and disease progression. Longitudinal follow-up data from serial imaging and clinical outcomes further enrich datasets by offering insights into aneurysm growth patterns and rupture events over time.

Machine learning has been applied to several aspects of aortic aneurysm risk prediction. One of the earliest and most promising uses is in the detection and classification of aneurysms from imaging studies. Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated remarkable accuracy in identifying aneurysms and segmenting the aortic wall on CT or MRI images. These models can differentiate between thoracic and abdominal aneurysms and detect subtle imaging features that may indicate wall instability or early rupture signs, thus facilitating earlier diagnosis.

Another vital application of ML is in predicting aneurysm growth. Since the rate of aneurysm expansion is a key determinant of rupture risk, accurate growth prediction can guide clinical surveillance and intervention timing. Models trained on longitudinal datasets incorporating clinical variables and imaging features have shown that factors like smoking history, aortic wall thickness, and calcification patterns are strong predictors of aneurysm enlargement. Random forest algorithms, for example, have been particularly effective in capturing these complex relationships and outperform traditional models based solely on aneurysm size.

In addition to risk prediction, machine learning is increasingly being integrated into surgical decision support systems. These tools assimilate patient-specific data such as comorbidities, frailty indices, aneurysm morphology, and risk scores to aid clinicians in deciding the optimal timing and method of intervention, whether open surgical repair or Endovascular Aneurysm Repair (EVAR). Embedding these models into Electronic Medical Records (EMRs) enables real-time risk stratification and alerts, facilitating more informed and timely clinical decision-making.

Despite the exciting potential of ML in aortic aneurysm management, several challenges must be addressed before widespread clinical adoption. One major obstacle is data quality and standardization. Differences in imaging protocols, incomplete clinical records, and small sample sizes can reduce model generalizability. Ensuring that datasets are diverse and representative of different populations is also crucial to avoid biases that may worsen health disparities. Another limitation relates to the interpretability of ML models, especially deep learning, which often functions as a "black box." Clinicians need transparent models that clearly explain how predictions are made to build trust and enable informed decision-making. Regulatory approval and validation in large, independent cohorts remain essential steps before ML tools can be routinely implemented in clinical practice.