Machine Learning in Cardiac Imaging
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 368
Special Issue Editors
Interests: imaging; machine learning; biostatistics; risk analysis; modelling
Special Issues, Collections and Topics in MDPI journals
Interests: coronary artery disease; cardiac imaging; risk stratification; PET; SPECT
Special Issue Information
Dear Colleagues,
For many years, machine learning algorithms are having a significant impact on cardiac imaging. A simple query to search engines allows us to verify that, during the last few years, the number of articles published on machine learning applied to cardiac imaging has exponentially grown. Ultrasounds (US), computed tomography (CT), magnetic resonance (MR), and single photon emission computed tomography (SPECT) are some radiology techniques used in cardiology for morphological and functional evaluations. Machine learning-based methods applied to images and data provided by these techniques have allowed the development of tools to aid clinicians in the diagnosis and prognosis of cardiovascular diseases. Deep learning, adaptive algorithms, extreme gradient boosting, and decision trees are some of the machine learning procedures used for the quantitative assessment of images as well as risk analysis in cardiac patients.
In this situation, this special issue of Diagnostics is planned for providing information about the state-of-the-art of cardiac imaging by quantitative analysis obtained using machine learning approaches.
Dr. Rosario Megna
Dr. Carmela Nappi
Guest Editors
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Keywords
- machine learning
- cardiac imaging
- cardiovascular risk analysis
- coronary artery disease
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