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Machine Learning Models Used to Predict Abdominal Aortic Aneurysm Growth and Rupture: A Systematic Review and
Ahmad Aljobeh1, Praveen Parthasarathy2, Jonathan Liao2
1Department of Surgery, Stony Brook University Hospital, Stony Brook, NY; Department of Biomedical Informatics, Stony Brook University Hospital, Stony Brook, NY.
Annals of Vascular Surgery
|March 27, 2026
Summary
Machine learning (ML) models show promise for predicting abdominal aortic aneurysm (AAA) growth and rupture. Further validation is needed for clinical use in AAA risk stratification.
Area of Science:
- Cardiovascular Research
- Medical Imaging Analysis
- Artificial Intelligence in Medicine
Background:
- Abdominal aortic aneurysm (AAA) rupture is a leading cause of mortality.
- Current diameter-based surveillance for AAA is an imperfect predictor of rupture risk.
- Machine learning (ML) offers potential for improved AAA risk stratification by integrating diverse data types.
Purpose of the Study:
- To systematically review ML models for predicting AAA growth and rupture.
- To characterize the performance of these ML models.
- To assess their readiness for clinical translation.
Main Methods:
- A PRISMA-compliant systematic search of major databases (PubMed, Embase, Web of Science) was conducted.
- Studies developing preoperative ML models for AAA growth or rupture were included.
- Risk of bias, applicability, and reporting quality were assessed using established frameworks (PROBAST+AI, TRIPOD+AI).
Main Results:
- Eighteen studies were included, focusing on AAA growth (13 studies) and rupture (5 studies).
- ML models utilized various algorithms and multimodal data, showing promising internal performance (AUCs 0.75-0.93).
- External validation was infrequent, and reporting inconsistencies were noted, particularly regarding transparency and fairness.
Conclusions:
- ML models demonstrate potential for enhancing AAA risk prediction accuracy.
- Standardized feature definitions and robust external validation are crucial for clinical implementation.
- Prospective evaluation is needed to assess the impact of ML on clinical decision-making for AAA management.
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