Purpose: This study was performed to predict individualized treatment strategies in ruptured abdominal aortic aneurysm (rAAA) by estimating the survival benefit of endovascular aneurysm repair (EVAR) and open surgical repair (OSR) based on anatomical and physiological features using a causal inference model. Methods: This retrospective study included 45 patients with de novo rAAA who underwent EVAR or OSR between 2012 and 2024. Thirty-three variables were analyzed. The model estimated individualized treatment effects (ITE) for 30-day survival.Model interpretability was assessed using Shapley Additive Explanations (SHAP) analysis. Five-fold cross-validation, receiver operating characteristic (ROC) analysis, and calibration plots were used for model evaluation. A clinical decision tree was developed to derive simplified decision rules. Results: The mean ITE was 0.22 ± 0.42, with 33% of patients classified as OSR-benefit candidates. SHAP analysis revealed that suprarenal angle, infrarenal angle, iliac anatomy, and proximal neck characteristics strongly influenced treatment effects. However, some predictors, such as low hemoglobin and systolic blood pressure favoring OSR, conflicted with clinical intuition. ROC analysis showed an area under the curve of 1.00, but calibration suggested overfitting due to a small sample size. Treatment-matched patients had a higher 30-day mortality rate than mismatched patients, suggesting potential bias or unmeasured confounding. The decision tree identified clinically relevant features but displayed structural inconsistencies and impractical cutoff values due to the limited sample size. Conclusion The X-learner model demonstrated the feasibility of individualized treatment prediction in rAAA but suffered from overfitting and limited generalizability. Validation with larger multicenter cohorts is necessary to confirm clinical applicability.

Purpose: This study evaluated the feasibility of detecting peripheral arterial lesions in plane-reconstructed lower extremity CT angiograms using object detection algorithms. Methods: We retrospectively collected 1,241 contrast-enhanced lower extremity CT images from patients with peripheral arterial disease. One-stage (YOLOv5: v5s, v5m, v5l, v5x) and 2-stage (Faster R-CNN) detectors were used to classify stent, stenosis, and occlusion. A one-by-one comparison between manual test image annotations and algorithmic detection results was conducted to evaluate model performance and errors. Performance was evaluated by mean average precision (mAP@.5) and precision-recall curves. Results: Among YOLOv5 models, v5l showed the highest overall accuracy (77% mAP@.5). While stent classification was excellent (≥ 96.9% mAP@.5 in YOLOv5 and 99.8% in Faster R-CNN), classification accuracies for stenosis (53.8%–58.7% in YOLOv5 vs. 37.2% in Faster R-CNN) and occlusion (69%–80.9% in YOLOv5 vs. 67.7% in Faster R-CNN) were moderate.Stenosis was frequently missed, resulting in high false-negative rates. Occlusions at arterial bifurcations were often not detected, and stent edges were misclassified as occlusions. Overfitting emerged in some YOLOv5 models beyond 75 epochs. Conclusion This pilot study supports the feasibility of applying object detection algorithms as a preliminary step toward developing clinical decision support tools for peripheral arterial disease. Further refinements, including additional training data and more granular lesion annotation, are essential for improved classification of stenosis and occlusion.