About this Research Topic
Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous condition that poses significant diagnostic and therapeutic challenges. HFpEF has become a growing public health concern worldwide due to its high prevalence, especially in the old population. However, there is still a lack of understanding of the pathophysiology, diagnosis, and management of this condition. The nuanced nature of HFpEF, coupled with its multifactorial pathophysiology, necessitates developing innovative approaches for its identification, phenotyping, and evaluation of its response to treatment. Artificial Intelligence (AI) has emerged as a transformative tool in cardiology that can offer novel insights and methodologies for improving the diagnosis and management of cardiovascular diseases. Leveraging AI technologies to tackle the many challenges associated with HFpEF has the potential to revolutionize the way we understand, diagnose, and manage this condition.
The proposed collection of articles will concentrate on the role of AI in developing new markers and diagnostic tools for HFpEF. It will feature a curated selection of articles, including original research, reviews, meta-data analysis, and insightful perspectives. We aim to gather relevant information from many readers, including cardiologists, data scientists, and healthcare policymakers.
The main ideas of the articles may include, but are not limited to:
- AI Epidemiological Markers: An exploration of how AI can analyze large datasets to uncover patterns and trends in HFpEF prevalence and outcomes, potentially identifying new epidemiological markers.
- Pathophysiological Insights through AI: Discuss the use of AI to dissect the complex pathophysiology of HFpEF, including identifying novel biomarkers and the contribution of systemic factors such as diabetes and obesity.
- AI-Enhanced Diagnosis: Focusing on developing AI models that can integrate clinical, imaging, and/or molecular data to refine the diagnostic criteria for HFpEF.
- AI-Enhanced Prognostication: Applications of AI and ML models to predict patient outcomes, response to therapy, and risk stratification in HFpEF.
- Challenges and Ethical Considerations in AI Applications: A critical examination of the challenges, limitations, and ethical considerations in applying AI to HFpEF, ensuring responsible and equitable use of technology.
- Future Directions in AI-Based Research in HFpEF: Discussing the current gaps in knowledge and future directions in clinical applications of AI-based research in HFpEF.
The proposed collection of articles will concentrate on the role of AI in developing new markers and diagnostic tools for HFpEF. It will feature a curated selection of articles, including original research, reviews, meta-data analysis, and insightful perspectives. We aim to gather relevant information from many readers, including cardiologists, data scientists, and healthcare policymakers.
The main ideas of the articles may include, but are not limited to:
- AI Epidemiological Markers: An exploration of how AI can analyze large datasets to uncover patterns and trends in HFpEF prevalence and outcomes, potentially identifying new epidemiological markers.
- Pathophysiological Insights through AI: Discuss the use of AI to dissect the complex pathophysiology of HFpEF, including identifying novel biomarkers and the contribution of systemic factors such as diabetes and obesity.
- AI-Enhanced Diagnosis: Focusing on developing AI models that can integrate clinical, imaging, and/or molecular data to refine the diagnostic criteria for HFpEF.
- AI-Enhanced Prognostication: Applications of AI and ML models to predict patient outcomes, response to therapy, and risk stratification in HFpEF.
- Challenges and Ethical Considerations in AI Applications: A critical examination of the challenges, limitations, and ethical considerations in applying AI to HFpEF, ensuring responsible and equitable use of technology.
- Future Directions in AI-Based Research in HFpEF: Discussing the current gaps in knowledge and future directions in clinical applications of AI-based research in HFpEF.
Keywords: AI, Artificial Intelligence, HFpEF, Markers, Heart Failure, Heart Failure with Preserved Ejection Fraction
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