Time Is Aorta: Can Artificial Intelligence Improve Surgical Timelines in Acute Type A Aortic Dissection? A...

Area of Science:

• Cardiology
• Medical Imaging
• Health Informatics

Background:

• Acute Type A aortic dissection (ATAAD) is a time-sensitive emergency with high mortality.
• Delays in diagnosis, transfer, and treatment significantly worsen outcomes.
• Artificial intelligence (AI) offers potential solutions for improving efficiency in ATAAD management.

Purpose of the Study:

• To review the current literature on AI's role in shortening diagnostic and surgical timelines for ATAAD.
• To evaluate AI's potential to expedite recognition, imaging interpretation, and communication in ATAAD.

Main Methods:

• A narrative literature review was conducted using major databases and professional society websites.
• Search terms focused on ATAAD, acute aortic syndrome, AI, and workflow-related terms.
• Studies were included if they addressed AI in diagnosis, imaging, triage, transfer, or communication for ATAAD or AAS.

Main Results:

• AI models demonstrate high accuracy (AUC 0.86-0.99) in diagnosing aortic dissections using various data types.
• Automated CT triage can identify dissections in seconds, potentially reducing scan-to-report times by 26%-43%.
• Real-world application of non-contrast CT screening reduced diagnostic time for missed AAS cases significantly, but most evidence is from retrospective or simulated studies.

Conclusions:

• AI has the potential to decrease diagnostic and communication delays in ATAAD care.
• Direct evidence linking AI implementation to reduced door-to-surgery time or mortality in ATAAD is currently limited.
• Prospective studies are essential to establish AI-enabled ATAAD pathways as evidence-based standards of care.