Themed Issue: Call for Submissions
Theme: Artificial Intelligence/Machine Learning Applications for Advanced Diagnosis of Cardiovascular Disease.
Submission Deadline: May 30th, 2024
Submit your manuscript today! (Important: In your cover letter, please note that this manuscript is for the themed issue)
The Cardiovascular Digital Health Journal is issuing a call for submission of empirical research papers, reviews (systematic; meta-analysis) and perspectives on recent advancements and challenges in AI/ML in cardiovascular disease analysis.
Artificial intelligence (AI) and machine learning (ML) continue to play a significant role in various medical applications. AI algorithms are increasingly used to analyze medical images, such as X-rays, CT scans, and MRIs. These algorithms can help detect and diagnose various cardiovascular conditions. AI can assist radiologists by flagging suspicious findings, improving efficiency, and potentially reducing errors. AI and ML are being employed to develop personalized treatment plans based on individual patient characteristics, such as genomic data, medical history, lifestyle factors, biomarkers, imaging data, to predict an individual's risk of developing cardiovascular diseases (CVD). By analyzing large datasets, AI models can identify patterns and correlations that help predict treatment responses, optimize drug dosages, tailor therapies to specific patients, and accurate risk assessment.
In cardiovascular medicine today, AI/ML are making significant contributions across various areas. Moreover, AI/ML algorithms are employed in wearable devices and remote monitoring systems to continuously track cardiovascular parameters, such as heart rate, blood pressure, and activity levels. These technologies enable real-time monitoring, early detection of abnormalities, and timely intervention, facilitating remote patient management and reducing adverse events. Healthcare professionals play a crucial role in interpreting and applying the results generated by these technologies for patient care. AI/ML models need to adapt and evolve as new data and evidence emerge. CVD research is constantly evolving, with a variety of clinical guidelines, treatment approaches, and discoveries. AI/ML models should be able to continuously learn from new data and update their predictions and recommendations accordingly. Developing adaptive and scalable AI/ML frameworks that incorporate new knowledge is an ongoing challenge. This themed issue focuses on recent advancements and challenges in AI/ML in cardiovascular disease analysis.
Potential topics include, but are not limited to the following:
- Advanced AI/ML algorithms in medical imaging analysis for accurate cardiovascular disease prediction
- AI/ML algorithms in wearable applications for the next generation cardiovascular care
- AI/ML-based rhythm disorders and electrophysiology monitoring systems for advanced cardiac analysis
- Novel AI/ML methods for diagnosing and treating congenital heart disease
- AI/ML-enabled real-time applications and tools for remote cardiovascular health management
- AI/ML in precision medicine for atherosclerosis disease and preventive applications
- AI/ML in clinical data strategies and predictive models for cardiovascular risk assessments
- AI/ML breakthroughs for personalized therapeutics and drug development for managing increased heart failure
- Impact of AI/ML in screening high blood pressure for heart patients in treating children/adolescents/adults
- AI/ML techniques in earlier analysis of myocardial infraction in diabetic patients
Dr. Latha Parthiban
Department of Computer Science
Pondicherry University Community College, India
Prof. Prakash Deedwania
Professor of Medicine
School of Medicine, University of California, San Francisco (UCSF), USA
Dr. Jeroen J Bax
Leiden University, Netherlands