About this Research Topic
Acute coronary syndrome (ACS) remains a major cause of morbidity and mortality worldwide. Cardiac imaging has always played an important role in diagnosis, risk stratification and treatment in coronary artery disease (CAD). Traditional cardiac imaging tests include UCG, CTA, CMR, CA, IVUS, OCT and so on. In the last decade, great progress has been made in cardiac imaging. For example, [18F]-sodium fluoride positron emission tomography (PET) imaging can detect microcalcification which may portend plaque rupture. In the meantime, the combination of artificial intelligence and traditional cardiac imaging has also shown an unprecedented potential in the field of cardiac imaging analysis and will provide new ideas for disease diagnosis and treatment.
Our aim is to achieve faster and more accurate automatic identification of relevant diagnostic and prognostic information from cardiovascular images, and to develop more effective and personalized ACS diagnosis and treatment strategies in close collaboration with artificial intelligence. In this Research Topic, we are looking for Original Research articles as well as Reviews that address current challenges and future opportunities of cardiovascular imaging and artificial intelligence in the field of ACS, and we intend to provide a forum to update and discuss the important advances and new directions of artificial intelligence in tissue characterization of cardiovascular images, imaging biomarker discovery, and clinical decision support in cardiology.
Potential topics include, but are not limited to:
1) Applications of traditional cardiac imaging in ACS.
2) The advances in intravascular imaging and their value in assessing plaque morphology.
3) The potential value of the novel invasive imaging techniques.
4) Technical improvements and recommendations in imaging.
5) Assessment of evolution of ACS and therapy response using imaging.
6) Clinical decision support with imaging and AI techniques in ACS.
7) Application of imaging in the understanding of the pathogenesis of ACS.
8) AI-aided cardiac image and intravascular image acquisition.
9) AI-based automated cardiac quantification and flow quantification.
10) Cardiac and vascular tissue characterization via AI applications.
11) Imaging biomarker discovery via machine learning.
Our aim is to achieve faster and more accurate automatic identification of relevant diagnostic and prognostic information from cardiovascular images, and to develop more effective and personalized ACS diagnosis and treatment strategies in close collaboration with artificial intelligence. In this Research Topic, we are looking for Original Research articles as well as Reviews that address current challenges and future opportunities of cardiovascular imaging and artificial intelligence in the field of ACS, and we intend to provide a forum to update and discuss the important advances and new directions of artificial intelligence in tissue characterization of cardiovascular images, imaging biomarker discovery, and clinical decision support in cardiology.
Potential topics include, but are not limited to:
1) Applications of traditional cardiac imaging in ACS.
2) The advances in intravascular imaging and their value in assessing plaque morphology.
3) The potential value of the novel invasive imaging techniques.
4) Technical improvements and recommendations in imaging.
5) Assessment of evolution of ACS and therapy response using imaging.
6) Clinical decision support with imaging and AI techniques in ACS.
7) Application of imaging in the understanding of the pathogenesis of ACS.
8) AI-aided cardiac image and intravascular image acquisition.
9) AI-based automated cardiac quantification and flow quantification.
10) Cardiac and vascular tissue characterization via AI applications.
11) Imaging biomarker discovery via machine learning.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
