Machine Learning Models Used to Predict Abdominal Aortic Aneurysm Growth and Rupture: A Systematic Review and...

Abdominal aortic aneurysm (AAA) rupture remains a major cause of mortality, and diameter-based surveillance is an imperfect predictor of risk. Some aneurysms rupture below operative thresholds, whereas others remain stable despite exceeding them. Machine learning (ML) may improve risk stratification by integrating geometric, hemodynamic, radiomic, and clinical data. We performed a systematic review to evaluate ML models predicting AAA growth and rupture, characterize their performance, and assess readiness for clinical translation.

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant search of PubMed/MEDLINE, Embase, and Web of Science (through March 2025) identified studies developing preoperative ML models for AAA growth or rupture. Data extraction followed the CHecklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were assessed using the Prediction model Risk of Bias ASsessment Artificial Intelligence Tool, and reporting quality was assessed using Transparent Reporting of a multivariable prediction model for Individual Prognosis Artificial Intelligence.

Eighteen studies met inclusion criteria: 13 addressed growth (n = 745 patients) and five rupture (n = 1,394). Growth models commonly employed support vector machines, convolutional neural networks, and gradient boosting, incorporating diameter, intraluminal thrombus thickness, tortuosity, wall shear stress, radiomics, and clinical variables. The area under the curve for growth prediction ranged from 0.79 to 0.93, with root mean square errorof 0.94-2.4 mm. Rupture models used diverse classifiers with similar multimodal inputs and area under the curve from 0.75 to 0.91. Although internal performance was promising, external validation was uncommon. PROBAST + AI demonstrated low risk of bias in 61% of studies but frequent applicability concerns. Transparent Reporting of a multivariable prediction model for Individual Prognosis Artificial Intelligence revealed inconsistent reporting, particularly regarding transparency and fairness.

ML models show promise for improving AAA risk prediction. Clinical implementation will require standardized feature definitions, robust external validation, and prospective evaluation of impact on decision-making.