Advances in the Diagnosis of Aortic Disease
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 50
Special Issue Editor
Interests: aortic pathology; cardiovascular surgery; sciences of longevity; epigenetics
Special Issue Information
Dear Colleagues,
Background
Aortic diseases, namely aortic aneurysms, acute aortic syndromes (penetrating aortic ulcer, aortic dissection, aortic pseudoaneurysm, contained ruptured aortic aneurysm, intramural aortic hematoma, and traumatic aortic injury), aortoiliac occlusive disease, inflammatory aortic diseases, and genetic acropathies have long been a diagnostic and therapeutic challenge for patients and healthcare professionals.
Sadly, the diagnosis of these ailments is often made incidentally by tests performed for other reasons or in the final moments when the aorta has ruptured, dissected, or occluded. Because of this, health professionals have been investing heavily into finding an efficient, cost-effective, and standardized diagnostic approach to these pathologies.
As technology advances at a vertiginous pace, it sometimes feels that a healthcare singularity of sorts will take place, where all of our health issues will be managed in a personalized manner with AI-powered decision making and orally administered gene editing.
Indeed, there is a plethora of scientific literature describing potent AI algorithms that assist in the diagnosis of aortic conditions. Many promising biomarkers that reflect aortic suffering are also being described.
Relevance
Aortic pathologies account for 2–3 deaths per 100,000 inhabitants globally.2 Expedited diagnosis and treatment are never easy tasks. The different features of each aortic disease, their high overall morbidity and mortality, the frequent difficulties in their initial diagnosis, and the absence of a definitive “aortic diagnostic test” still make these conditions a major hurdle to overcome for health services everywhere.
Objectives
In this Special Issue, we will try to summarize the current advances in aortic pathology diagnosis, specifically those relating to modern data processing technologies such as machine learning (ML) and artificial intelligence (AI) applied to radiological imaging, aortic tissue biochemical markers, and genetic and proteomic applications for aortic pathology screening, diagnosis, and surveillance.
Dr. James Henry Taylor
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- aortic aneurysm
- aortic dissection
- acute aortic syndrome
- aortic imaging
- aortic biomarkers
- machine learning (ML)
- artificial intelligence (AI)
