Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis

Area of Science:

• Medical Informatics
• Biomedical Engineering
• Computer Science

Background:

• Scientific output on artificial intelligence (AI) in vascular diseases remains underexplored.
• A comprehensive analysis of AI's role in non-cardiac vascular diseases is needed to understand current research trends and future potential.

Purpose of the Study:

• To systematically evaluate scientific publications focusing on artificial intelligence applications in non-cardiac vascular diseases.
• To identify the primary AI techniques and their applications in vascular disease research.

Main Methods:

• A systematic literature search was performed on the PubMed database.
• Included were original English articles published between January 1995 and December 2020, focusing on carotid, aortic, and peripheral artery diseases.
• Data extracted included publication details, author information, AI fields, and application areas.

Main Results:

• 171 articles were included, with the USA, China, and the UK being the most productive countries.
• Machine learning (45.0%), vision (22.5%), and robotics (21.0%) were the leading AI fields.
• Primary applications included treatment tools (29.1%), prognosis (25.1%), diagnosis/classification (21.2%), and imaging segmentation (21.2%).

Conclusions:

• AI research in non-cardiac vascular diseases is expanding, with significant contributions from machine learning and computer vision.
• The identified applications in treatment, prognosis, diagnosis, and imaging segmentation demonstrate AI's growing clinical relevance.
• Understanding current trends and limitations is crucial for forecasting future AI integration into clinical practice for vascular diseases.