New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning
Abstract
:1. Introduction
1.1. Background
1.2. Significance
2. Computer-Assisted Measurement of the Aorta
2.1. Artificial Intelligence and Machine Learning
2.2. Machine Learning Concepts
2.3. Considerations Regarding AI Limitations
3. Biochemical Monitoring
3.1. Background
3.2. Genomic/Proteomic Analysis
3.3. Circulating Protein Quantification
3.4. Circulating microRNA Quantification
3.5. Circulating Extracellular Vesicle Concentration, Size Distribution, and Cargo Analysis
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Alexander, K.C.; Ikonomidis, J.S.; Akerman, A.W. New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning. J. Clin. Med. 2024, 13, 818. https://doi.org/10.3390/jcm13030818
Alexander KC, Ikonomidis JS, Akerman AW. New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning. Journal of Clinical Medicine. 2024; 13(3):818. https://doi.org/10.3390/jcm13030818
Chicago/Turabian StyleAlexander, Kyle C., John S. Ikonomidis, and Adam W. Akerman. 2024. "New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning" Journal of Clinical Medicine 13, no. 3: 818. https://doi.org/10.3390/jcm13030818