# Predicting the growth of asymptomatic small abdominal aortic aneurysms (AAA) based on deep learning

## TL;DR

This paper introduces a deep learning framework to predict the growth of small abdominal aortic aneurysms using CT scans and clinical data, improving risk assessment for patients.

## Contribution

A novel end-to-end deep learning framework combining ResNet50, YOLOv11, and MedMamba for accurate AAA growth prediction from CTA images and clinical metadata.

## Key findings

- The framework achieved 98.75% predictive accuracy and 97.78% F1-score in predicting AAA growth.
- Explainability analyses showed the model relies on established clinical risk factors and biologically plausible imaging regions.

## Abstract

Accurate prediction of asymptomatic small abdominal aortic aneurysm (AAA) growth is crucial for risk stratification and personalized surveillance. This study developed an end-to-end deep learning framework to predict rapid expansion (≥0.5 cm/6 months) using computed tomography angiography (CTA) images from 81 asymptomatic patients with small AAA (30 rapid-growth and 51 stable patients). The pipeline integrated three core components: a ResNet50 classifier for identifying aortic images (99.86% accuracy, 99.91% F1-score), a YOLOv11 detector for localizing aneurysms (precision–recall: 0.902), and a MedMamba-based feature fusion model that combined imaging features with clinical metadata via multi-head self-attention. Model robustness was ensured through stratified 5-fold cross-validation and comprehensive data augmentation. The fusion model achieved a predictive accuracy of 98.75% and an F1-score of 97.78, outperforming seven classical deep learning backbones. Furthermore, explainability analyses confirmed the model’s reliance on established clinical risk factors and highlighted biologically plausible imaging regions for prediction. The proposed ResNet50–YOLOv11–MedMamba framework demonstrates the feasibility of automating AAA growth prediction directly from CTA and shows promising potential to enhance clinical decision-making.