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Artificial intelligence in aortic CT angiography: current applications and future perspectives.

Jingkai Xu1,2, Jinjin Liu2, Guoquan Cao2

  • 1The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, China.

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Summary

Artificial intelligence (AI) is transforming aortic computed tomography angiography (CTA) for cardiovascular imaging. AI enhances image quality, automates analysis, and improves diagnostics for aortic diseases, offering new clinical solutions.

Keywords:
aortic diseasesartificial intelligencecomputed tomography angiographyconvolutional neural networkdeep learning

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Area of Science:

  • Cardiovascular Imaging
  • Medical Artificial Intelligence

Background:

  • Aortic computed tomography angiography (CTA) is crucial for diagnosing and managing aortic diseases.
  • Conventional CTA has limitations including radiation exposure, contrast risks, and manual interpretation challenges.

Purpose of the Study:

  • To review the current applications of AI in aortic CTA.
  • To analyze emerging AI techniques and their clinical potential in cardiovascular imaging.
  • To identify challenges and future research directions for AI in aortic CTA.

Main Methods:

  • Review of current literature on AI applications in aortic CTA.
  • Analysis of emerging AI techniques such as image synthesis, Vision Transformers, multi-task learning, and Foundation Models.
  • Discussion of AI's role in image enhancement, segmentation, diagnostics, and prognostic evaluation.

Main Results:

  • AI enhances image quality and automates segmentation in aortic CTA.
  • AI improves diagnostic accuracy for aortic emergencies and aids prognostic evaluation post-intervention.
  • Emerging AI techniques show significant potential for clinical translation in cardiovascular imaging.

Conclusions:

  • AI offers innovative solutions to limitations of conventional aortic CTA.
  • AI integration promises to advance cardiovascular imaging, diagnostics, and patient management.
  • Further research is needed to address challenges and fully realize AI's potential in aortic CTA.