Artificial Intelligence in Cardiovascular Diseases
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 1997
Special Issue Editors
Interests: machine learning; deep learning; medical imaging; cardiovascular disease risk assessment
Special Issues, Collections and Topics in MDPI journals
2. Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cardiovascular disease (CVD) is the leading cause of global mortality and morbidity, which accounts for the global deaths of 17.9 million people every year. It is generally accepted that cardiovascular diseases are a group of disorders that include a variety of diseases, such as coronary artery disease, coronary heart disease, cerebrovascular disease, and myocardial infarction. Several factors can contribute to the initiation and progression of CVD. These factors include traditional risk factors, image-based risk factors, and genetic factors that are all related to CVD. Early diagnostic methods and the implementation of appropriate treatment plans as well as lifestyle modifications are some of the most effective means of preventing CVD-related deaths. The field of artificial intelligence (AI), which has been gaining momentum over the past few years, is revolutionizing medical diagnosis and has demonstrated promising results in preventing cardiovascular diseases. A new generation of CVD prevention diagnostic tools can be developed through the use of AI-based algorithms that can be automated, accurate, and affordable. An understanding of both physiological signals and imaging techniques, such as invasive and non-invasive imaging modalities, is crucial in order to diagnose and prognose cardiovascular disease. Hence, we would like to cordially invite you to share your innovations and observations in the field of AI and cardiovascular disease with the global medical and research communities. The goal of this Special Issue is to publish comprehensive reviews and original research as well as information on recent advancements in artificial-intelligence-based diagnostic strategies for preventing cardiovascular disease.
We invite you to a fascinating scientific adventure.
Dr. Ankush D. Jamthikar
Dr. Jasjit S. Suri
Guest Editors
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
- diagnosis of cardiovascular disease
- atherosclerotic cardiovascular disease
- physiological signals
- medical Imaging
- machine learning
- deep learning
- cardiovascular disease risk assessment
