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This study introduces an AI framework for detecting aortic dissection (AD) using non-contrast CT scans. The model improves diagnostic accuracy and segmentation, assisting radiologists in this critical cardiovascular emergency.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Diseases

Background:

  • Aortic dissection (AD) is a critical cardiovascular emergency requiring rapid diagnosis.
  • Non-contrast-enhanced CT (NCE-CT) offers a safer screening alternative to contrast-enhanced CT angiography (CE-CT).
  • NCE-CT imaging limitations lead to diagnostic challenges, including missed diagnoses and increased radiologist workload.

Purpose of the Study:

  • To develop and validate an end-to-end multi-task framework for automated aortic segmentation and AD detection using NCE-CT images.
  • To enhance the diagnostic capabilities of NCE-CT for AD screening.
  • To provide an AI-driven tool to assist radiologists in AD detection.

Main Methods:

  • A novel multi-task framework integrating a deformable feature extractor, adaptive geometric information extraction module with transformer cross-attention, and knowledge distillation.
  • Training and validation using multi-center datasets (3 internal, 2 external) for both aortic segmentation and AD detection.
  • Comparison with existing methods using metrics such as Dice, Jaccard index, MIoU, FWIoU, accuracy, sensitivity, and F1-score.

Main Results:

  • The framework achieved high performance in aortic segmentation (Dice: 0.928 internal, 0.909 external) and AD detection (Accuracy: 0.911 internal, 0.840 external).
  • Demonstrated superior performance over existing methods in both segmentation and detection tasks across internal and external validation datasets.
  • Ablation experiments confirmed the effectiveness of individual modules within the proposed framework.

Conclusions:

  • The proposed AI framework effectively automates aortic segmentation and AD detection from NCE-CT images.
  • This model shows significant potential as a diagnostic assistant for radiologists, improving AD screening efficiency and accuracy.
  • The framework offers a valuable tool to mitigate diagnostic errors and reduce radiologist workload in identifying this life-threatening condition.