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Published on: September 19, 2018
Individual risk assessment for rupture of abdominal aortic aneurysm using artificial intelligence
Joachim Sejr Skovbo1, Nicklas Sindlev Andersen2, Lasse Møllegaard Obel1
1Department of Cardiac, Thoracic and Vascular Surgery, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
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.
