Deep Learning in Personalization of Cardiovascular Stents

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Cardiovascular Medicine

Background:

• Deep learning (DL) offers significant potential for complex biomedical tasks.
• Advancements in DL algorithms and software are rapidly evolving.
• Cardiovascular disease (CAD) remains a major global health challenge.

Purpose of the Study:

• To review recent advances in DL principles, software, and design strategies.
• To discuss DL applications in cardiovascular devices, diagnostics, and treatment outcomes for CAD.
• To explore DL's role in discovering new materials and personalized medicine for cardiovascular health.

Main Methods:

• Literature review of DL algorithms and their biomedical applications.
• Analysis of current DL software and model design strategies.
• Examination of DL-based cardiovascular devices, diagnostic methods, and treatment outcomes.

Main Results:

• DL has shown potential in vessel segmentation, brain visualization, and speech recognition.
• DL-based cardiovascular stents and advanced diagnostic tools are emerging.
• DL facilitates the discovery of new materials and personalized risk prediction for CAD.

Conclusions:

• DL is a transformative technology in cardiovascular medicine.
• DL enables personalized treatment strategies by forecasting individual cardiovascular risks.
• Future medical technologies will leverage DL for tailored cardiovascular care.