Individual risk assessment for rupture of abdominal aortic aneurysm using artificial intelligence

Joachim Sejr Skovbo*, Nicklas Sindlev Andersen, Lasse Møllegaard Obel, Malene Skaarup Laursen, Andreas Stoklund Riis, Kim Christian Houlind, Axel Cosmus Pyndt Diederichsen, Jes Sanddal Lindholt

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

1 Downloads (Pure)

Abstract

OBJECTIVE: This study aimed to develop a prediction tool to identify abdominal aortic aneurysms (AAAs) at increased risk of rupture incorporating demographic, clinical, imaging, and medication data using artificial intelligence (AI).

DESIGN: A development and validation study for individual prognosis using AI in a case-control design.

METHODS: From two Danish hospitals, all available ruptured AAA cases between January 2009 and December 2016 were included in a ratio of 1:2 with elective surgery controls. Cases with previous AAA surgery or missing preoperative scans were excluded. Features from computed tomography angiography scans and hospital records were manually retrieved. The sample was divided randomly and evenly into developmental and internal validation groups. A SHapley Additive exPlanations Feature Importance Rank Ensembling (SHAPFire) AI tool was developed using a gradient boosting decision tree framework. The final SHAPFire AI model was compared with models using (1) solely infrarenal anterior-posterior diameter and (2) all available features.

RESULTS: The study included 637 individuals (84.8% men, mean age 73 ± 7 years, 213 ruptured AAAs). The SHAPFire AI incorporated 20 of 68 available features, and aneurysm size, blood pressure, and relationships between height and weight were given highest rankings. The receiver operating characteristic curve for the SHAPFire AI model displayed a significant increase in accuracy identifying ruptured AAA cases compared with the conventional model based solely on diameter with areas under the curves of 0.86 ± 0.04 and 0.74 ± 0.03 (P = .008), respectively. SHAPFire AI was comparable in performance with the model using all features.

CONCLUSIONS: This study successfully developed a SHAPFire AI tool to identify AAAs at increased risk of rupture with significantly higher accuracy than diameter alone. External validation of the model is warranted before clinical implementation.

Original languageEnglish
JournalJournal of Vascular Surgery
Volume81
Issue number3
Pages (from-to)613-622.e5
ISSN0741-5214
DOIs
Publication statusPublished - Mar 2025

Bibliographical note

Copyright © 2024. Published by Elsevier Inc.

Keywords

  • Abdominal aortic aneurysm
  • Artificial Intelligence
  • Case-control study
  • Risk assessment
  • Ruptured abdominal aortic aneurysm

Fingerprint

Dive into the research topics of 'Individual risk assessment for rupture of abdominal aortic aneurysm using artificial intelligence'. Together they form a unique fingerprint.

Cite this