New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning
This review article presents an appraisal of pioneering technologies poised to revolutionize the diagnosis and management of aortic aneurysm disease, with a primary focus on the thoracic aorta while encompassing insights into abdominal manifestations. Our comprehensive analysis is rooted in an exhau...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Journal of Clinical Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0383/13/3/818 |
| _version_ | 1797318536484356096 |
|---|---|
| author | Kyle C. Alexander John S. Ikonomidis Adam W. Akerman |
| author_facet | Kyle C. Alexander John S. Ikonomidis Adam W. Akerman |
| author_sort | Kyle C. Alexander |
| collection | DOAJ |
| description | This review article presents an appraisal of pioneering technologies poised to revolutionize the diagnosis and management of aortic aneurysm disease, with a primary focus on the thoracic aorta while encompassing insights into abdominal manifestations. Our comprehensive analysis is rooted in an exhaustive survey of contemporary and historical research, delving into the realms of machine learning (ML) and computer-assisted diagnostics. This overview draws heavily upon relevant studies, including Siemens’ published field report and many peer-reviewed publications. At the core of our survey lies an in-depth examination of ML-driven diagnostic advancements, dissecting an array of algorithmic suites to unveil the foundational concepts anchoring computer-assisted diagnostics and medical image processing. Our review extends to a discussion of circulating biomarkers, synthesizing insights gleaned from our prior research endeavors alongside contemporary studies gathered from the PubMed Central database. We elucidate the prevalent challenges and envisage the potential fusion of AI-guided aortic measurements and sophisticated ML frameworks with the computational analyses of pertinent biomarkers. By framing current scientific insights, we contemplate the transformative prospect of translating fundamental research into practical diagnostic tools. This narrative not only illuminates present strides, but also forecasts promising trajectories in the clinical evaluation and therapeutic management of aortic aneurysm disease. |
| first_indexed | 2024-03-08T03:53:50Z |
| format | Article |
| id | doaj.art-93f6463f253d453b86a240208e0bc881 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-08T03:53:50Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-93f6463f253d453b86a240208e0bc8812024-02-09T15:16:13ZengMDPI AGJournal of Clinical Medicine2077-03832024-01-0113381810.3390/jcm13030818New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine LearningKyle C. Alexander0John S. Ikonomidis1Adam W. Akerman2Department of Surgery, Division of Cardiothoracic Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USADepartment of Surgery, Division of Cardiothoracic Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USADepartment of Surgery, Division of Cardiothoracic Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USAThis review article presents an appraisal of pioneering technologies poised to revolutionize the diagnosis and management of aortic aneurysm disease, with a primary focus on the thoracic aorta while encompassing insights into abdominal manifestations. Our comprehensive analysis is rooted in an exhaustive survey of contemporary and historical research, delving into the realms of machine learning (ML) and computer-assisted diagnostics. This overview draws heavily upon relevant studies, including Siemens’ published field report and many peer-reviewed publications. At the core of our survey lies an in-depth examination of ML-driven diagnostic advancements, dissecting an array of algorithmic suites to unveil the foundational concepts anchoring computer-assisted diagnostics and medical image processing. Our review extends to a discussion of circulating biomarkers, synthesizing insights gleaned from our prior research endeavors alongside contemporary studies gathered from the PubMed Central database. We elucidate the prevalent challenges and envisage the potential fusion of AI-guided aortic measurements and sophisticated ML frameworks with the computational analyses of pertinent biomarkers. By framing current scientific insights, we contemplate the transformative prospect of translating fundamental research into practical diagnostic tools. This narrative not only illuminates present strides, but also forecasts promising trajectories in the clinical evaluation and therapeutic management of aortic aneurysm disease.https://www.mdpi.com/2077-0383/13/3/818aortic aneurysmdiagnosticAImachine learningbiomarkersprecision medicine |
| spellingShingle | Kyle C. Alexander John S. Ikonomidis Adam W. Akerman New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning Journal of Clinical Medicine aortic aneurysm diagnostic AI machine learning biomarkers precision medicine |
| title | New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning |
| title_full | New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning |
| title_fullStr | New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning |
| title_full_unstemmed | New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning |
| title_short | New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning |
| title_sort | new directions in diagnostics for aortic aneurysms biomarkers and machine learning |
| topic | aortic aneurysm diagnostic AI machine learning biomarkers precision medicine |
| url | https://www.mdpi.com/2077-0383/13/3/818 |
| work_keys_str_mv | AT kylecalexander newdirectionsindiagnosticsforaorticaneurysmsbiomarkersandmachinelearning AT johnsikonomidis newdirectionsindiagnosticsforaorticaneurysmsbiomarkersandmachinelearning AT adamwakerman newdirectionsindiagnosticsforaorticaneurysmsbiomarkersandmachinelearning |