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ARXIV:2603.07399 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.07399MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy.
Opportunity summary
Pain An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier to clinical adoption and regulatory approval.
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy.
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10.48550/arXiv.2603.07399An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy.
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.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier...
METHOD
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...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier to clinical adoption and regulatory approval.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier to clinical adoption and regulatory approval.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier to clinical adoption and regulatory approval.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An interpretable AI model for aneurysm classification using 3D Concept Bottleneck Models, offering clinical transparency and high accuracy.
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Medical AI
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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