The Role of Multimodality Imaging in Valve Diseases
A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Cardiology".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 143
Special Issue Editor
Interests: heart failure; clinical cardiology; cardiomyopathies; cardiac; echocardiography; transesophageal echocardiography; cardiovascular imaging; three-dimensional echocardiography; cardiac computed tomography; CMR
Special Issue Information
Dear Colleagues,
Valve disease is one of the main causes of hospitalization for young and old patients, and there have been significant advancements in its diagnosis and treatment recently due to innovative technologies.
Nevertheless, valve diseases remain a major global health issue, necessitating novel approaches to improve diagnosis and patient outcomes.
This Special Issue aims to present cutting-edge research, comprehensive reviews, and expert opinions on the latest cardiovascular imaging techniques, revolutionizing the diagnosis of this disease.
Focusing on echocardiography with its new tools, cardiac magnetic resonance, cardiac CT, PET, and SPECT, this Special Issue aim to highlight advancements that enhance diagnosis and treatment. Additionally, echocardiography explores an automated multiparametric machine learning model for grading valve disease’s severity in a faster and highly accurate manner. Its implementation in clinical practice could improve patient care by facilitating referral to specialized clinics and access to evidence-based therapies while improving quality and efficiency in the echocardiography laboratory.
Also, with the idea of multidisciplinary collaboration among cardiology, nuclear radiologists, and radiology experts, this Special Issue aims to inspire further research and innovation, ultimately striving to redefine the future of the diagnosis and treatment of valve disease and improve patient care.
Dr. Maria Lo Monaco
Guest Editor
Manuscript Submission Information
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Keywords
- valve diseases
- echocardiography
- machine learning
- cardiac magnetic resonance
- computed tomography
- SPECT
- PET
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