Interpretable Aneurysm Classification via 3D Concept Bottleneck Models: Integrating Morphological and Hemodynamic Clinical Features

2026-03-08

AI Summary

This study addresses the lack of clinical interpretability in deep learning models for intracranial aneurysm classification by proposing the first end-to-end 3D concept bottleneck model. The model maps CTA image features to clinically meaningful concepts—such as morphological and hemodynamic attributes—thereby embedding interpretability aligned with neurosurgical principles directly into the architecture. Built upon pretrained 3D ResNet-34 and DenseNet-121 backbones, the framework incorporates a soft concept bottleneck layer, a composite loss function combining focal loss and concept mean squared error, and eight-fold test-time augmentation (TTA). Experimental results demonstrate that ResNet-34 achieves an accuracy of 93.33% ± 4.5%, while DenseNet-121 reaches 91.43% ± 5.8%; under TTA, the model maintains a stable accuracy of 88.31% with an accuracy–generalization gap below 0.04, effectively balancing high performance with clinical transparency.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We are concerned with the challenge of reliably classifying and assessing intracranial aneurysms using deep learning without compromising clinical transparency. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier to clinical adoption and regulatory approval. Explainability is paramount in medical modeling to ensure that AI-driven diagnoses align with established neurosurgical principles. Unlike traditional eXplainable AI (XAI) methods -- such as saliency maps, which often provide post-hoc, non-causal visual correlations -- Concept Bottleneck Models (CBMs) offer a robust alternative by constraining the model's internal logic to human-understandable clinical indices. In this article, we propose an end-to-end 3D Concept Bottleneck framework that maps high-dimensional neuroimaging features to a discrete set of morphological and hemodynamic concepts for aneurysm identification. We implemented this pipeline using a pre-trained 3D ResNet-34 backbone and a 3D DenseNet-121 to extract features from CTA volumes, which were subsequently processed through a soft bottleneck layer representing human-interpretable clinical concepts. The model was optimized using a joint-loss function to balance diagnostic focal loss and concept mean squared error (MSE), validated via stratified five-fold cross-validation. Our results demonstrate a peak task classification accuracy of 93.33% +/- 4.5% for the ResNet-34 architecture and 91.43% +/- 5.8% for the DenseNet-121 model. Furthermore, the implementation of 8-pass Test-Time Augmentation (TTA) yielded a robust mean accuracy of 88.31%, ensuring diagnostic stability during inference. By maintaining an accuracy-generalization gap of less than 0.04, this framework proves that high predictive performance can be achieved without sacrificing interpretability.

Problem

• intracranial aneurysm
• interpretable AI
• clinical transparency
• deep learning
• medical diagnosis

Innovation

• Concept Bottleneck Models
• 3D Deep Learning
• Interpretable AI
• Aneurysm Classification
• Clinical Transparency