# An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes

## TL;DR

This paper introduces a machine learning tool to predict outcomes for patients with abdominal aortic aneurysms, offering a more personalized approach than current guidelines.

## Contribution

The study presents the largest cohort using medical images and clinical data to develop a prognosis classifier for AAA outcomes.

## Key findings

- A machine learning model was trained on 381 patients to predict AAA outcomes: stable, repair, or rupture.
- The model could classify outcomes using biomechanical, morphological, and clinical data more effectively than current diameter-based guidelines.
- Such a tool may improve clinical decision-making for AAA patient management.

## Abstract

Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their clinically-estimated risk of rupture—an event that is the 13th leading cause of death in the US—exceeds the risk associated with repair. Yet the current clinical guideline remains a one-size-fits-all “maximum diameter criterion” whereby AAA exceeding a threshold diameter is thought to make the risk of rupture high enough to warrant intervention. However, between 7 and 23.4% of smaller-sized AAA have been reported to rupture with diameters below the threshold. In this study, we train and assess machine learning models using clinical, biomechanical, and morphological indices from 381 patients to develop an aneurysm prognosis classifier to predict one of three outcomes for a given AAA patient: their AAA will remain stable, their AAA will require repair based as currently indicated from the maximum diameter criterion, or their AAA will rupture. This study represents the largest cohort of AAA patients that utilizes the first available medical image and clinical data to classify patient outcomes. The APC model therefore represents a potential clinical tool to striate specific patient outcomes using machine learning models and patient-specific image-based (biomechanical and morphological) and clinical data as input. Such a tool could greatly assist clinicians in their management decisions for patients with AAA.