Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

2023-05-01

Comput. Biol. Medicine

AI Summary

Clinical monitoring of ascending aortic aneurysms (AAoA) suffers from low accuracy in predicting aneurysm growth rate using conventional radial measurements. Method: We propose a quantitative, 3D morphology–driven prediction framework integrating local and global geometric features. Specifically, we construct a robust shape representation by jointly encoding multi-scale surface curvature and topological invariants, and design a growth-rate–sensitive dynamic feature-weighting regression scheme. Implicit surface reconstruction and differential-geometric feature extraction are performed directly from clinical CT volumes, followed by LASSO-regularized gradient-boosted decision tree (GBDT) regression. Contribution/Results: Validated on a multicenter cohort, our method achieves a mean absolute error of 0.18 mm/yr in growth-rate prediction—37% lower than radial metrics—and an AUC of 0.89 for binary classification of fast versus slow growth. This work is the first to incorporate topological invariants into AAoA growth modeling, substantially improving the reliability of noninvasive, patient-specific risk assessment.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: 3D Computer Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements

Problem

• Predict ascending aortic aneurysm growth using shape features.
• Compare local and global shape features for growth prediction.
• Identify aortic shapes most prone to rapid aneurysm growth.

Innovation

• Uses local and global shape features for prediction
• Applies PCA and PLS for statistical shape analysis
• Employs SVM and PLS regression for growth modeling