Cardiovascular Diseases: Advances in Diagnosis and Management
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 1978
Special Issue Editor
Interests: cardiovascular diseases; predictive risk score; mortality; prognosis; artificial intelligence; machine learning; large language models
Special Issue Information
Dear Colleagues,
Cardiovascular disease remains the leading cause of mortality worldwide. With advancements in technology, molecular and genetic discoveries, and the development of novel pharmacological therapies, significant progress has been made in improving risk prediction, screening, diagnosis, and disease management. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in analyzing big data, optimizing imaging interpretation, and enhancing clinical decision-making. Simultaneously, the introduction of new medications has played a critical role in alleviating symptoms, reducing complications, and improving long-term outcomes for patients with cardiovascular disease.
This Special Issue aims to highlight innovations in both technological advancements and pharmacological therapies for cardiovascular disease, with a focus on their clinical applications in diagnosis, treatment, and long-term patient outcomes. We welcome submissions investigating the efficacy and safety of novel medications, as well as AI-driven approaches in disease diagnosis and management. Research areas may include, but are not limited to, the following:
- AI- and ML-based predictive risk scoring for major adverse cardiovascular events, including sudden cardiac death, acute decompensated heart failure, acute coronary syndrome, lethal arrhythmias, and overall cardiovascular mortality;
- AI integration in electronic devices, such as ECG machines, cardiac implantable electronic devices, and continuous ECG/vital signs monitoring systems;
- AI-assisted interpretation of imaging results and its role in guiding interventional procedures such as PCI and catheter ablation;
- the clinical efficacy and safety of novel pharmacological therapies in improving cardiovascular symptoms, reducing hospitalization rates, and enhancing survival outcomes;
- the impact of the latest guideline-recommended treatments on mortality, disease progression, and quality of life in patients with cardiovascular disease.
We look forward to receiving your contributions.
Dr. Thien Tan Tri Tai Truyen
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
- cardiovascular diseases
- predictive risk score
- mortality
- prognosis
- artificial intelligence
- machine learning
- large language models
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