New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning

Area of Science:

• Cardiovascular Medicine
• Medical Imaging
• Artificial Intelligence

Background:

• Aortic aneurysm disease, particularly thoracic aneurysms, presents significant diagnostic and management challenges.
• Existing diagnostic methods require enhancement for improved accuracy and efficiency.

Purpose of the Study:

• To review and appraise emerging technologies for aortic aneurysm diagnosis and management.
• To explore the role of machine learning (ML), computer-assisted diagnostics, and biomarkers in advancing aortic disease evaluation.

Main Methods:

• Comprehensive literature survey of contemporary and historical research on aortic aneurysm diagnosis.
• In-depth analysis of ML algorithms and computer-assisted diagnostic concepts.
• Synthesis of findings on circulating biomarkers from prior research and PubMed Central database.

Main Results:

• Machine learning and AI offer significant potential for revolutionizing aortic aneurysm diagnosis and management.
• Integration of AI-guided measurements and ML frameworks with biomarker analysis shows promise.
• Current research highlights challenges and future directions for translating AI into clinical practice.

Conclusions:

• The fusion of AI, ML, and biomarker analysis represents a transformative prospect for aortic aneurysm disease.
• Continued research and development are crucial for translating these technologies into practical diagnostic tools.
• This review provides a framework for understanding current advancements and future trajectories in aortic disease management.