# Machine Learning and Abdominal Aortic Aneurysm: A New Paradigm in Prediction and Prognosis after Endovascular Aneurysm Repair

## TL;DR

Machine learning improves AAA prediction and prognosis after surgery by integrating complex data, but challenges like generalizability and interpretability remain.

## Contribution

ML models outperform traditional methods by capturing nonlinear interactions in AAA management.

## Key findings

- ML algorithms integrate clinical and imaging data to estimate survival and guide AAA treatment decisions.
- Multicenter data and explainable AI techniques are improving model robustness and transparency.
- ML has potential to enhance long-term outcomes in AAA management after endovascular repair.

## Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming vascular surgery by enabling precise risk stratification, individualized treatment planning, and improved prognostic prediction. In abdominal aortic aneurysm (AAA) management, ML algorithms integrate complex clinical and imaging data to estimate survival, guide procedural decisions, and identify key factors influencing aneurysm remodeling. These models outperform traditional statistical approaches by capturing nonlinear interactions among variables such as nutritional status, immune function, and anatomical features. Despite these advances, challenges remain. Many studies rely on single-center datasets, raising concerns about overfitting and limited generalizability. The use of black-box models can hinder clinical trust due to limited interpretability. However, recent developments in multicenter data collection and explainable AI techniques are improving model robustness and transparency. As these tools continue to evolve, ML is poised to contribute meaningfully to precision vascular care. By supporting more individualized and data-informed decision-making, ML has the potential to enhance long-term outcomes and guide the future of AAA management after endovascular aneurysm repair.