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Segmentation-model-based framework to detect aortic dissection on non-contrast CT images: a retrospective study.

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A new deep learning framework accurately detects aortic dissection (AD) on non-contrast CT (NCCT) scans, improving emergency diagnosis and reducing contrast use. This AI tool visualizes AD morphology and extent, aiding treatment decisions.

Keywords:
Aortic dissectionDeep learning.Non-contrast computed tomography

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Deep Learning for Cardiovascular Diagnostics
  • Radiology and Imaging Informatics

Background:

  • Aortic dissection (AD) diagnosis often relies on contrast-enhanced CT angiography (CTA), posing risks for patients with contraindications.
  • Accurate and timely detection of AD is critical, especially in emergency settings.
  • Non-contrast CT (NCCT) offers a safer alternative but visualizing AD features can be challenging.

Purpose of the Study:

  • To develop and validate an automated deep learning framework for detecting AD on NCCT images.
  • To visualize the morphology and extent of AD using the developed framework.
  • To assess the clinical utility of deep learning in AD diagnosis on NCCT.

Main Methods:

  • A retrospective study utilized NCCT and CTA data from 701 patients across two hospitals.
  • A segmentation-based deep learning model was trained to identify true and false lumens on NCCT.
  • Model performance was evaluated using Dice coefficient, intraclass correlation coefficient (ICC), and receiver operating characteristic (ROC) analysis on internal and external test sets.

Main Results:

  • The deep learning model demonstrated strong consistency, with an ICC of 0.823 for false lumen volume in both internal and external validation.
  • The framework achieved an area under the curve (AUC) of 0.935 in the external test set.
  • An optimal cutoff value yielded high sensitivity (88.2%), specificity (91.3%), and negative predictive value (89.0%) for AD detection.

Conclusions:

  • The developed deep learning framework accurately detects AD on NCCT images.
  • The AI tool effectively visualizes AD morphology and extent, showing significant clinical potential.
  • This framework enhances emergency AD diagnosis, reduces reliance on contrast media, and supports treatment decisions.