Article ; Online: Machine learning models for positron emission tomography myocardial perfusion imaging.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
2024 Volume 32, Page(s) 101805
Abstract: Machine learning has the potential to improve patient care by automating the assessment of medical imaging. Machine learning models have been developed to identify ischaemia and scar on rest and stress myocardial perfusion imaging from positron emission ... ...
Abstract | Machine learning has the potential to improve patient care by automating the assessment of medical imaging. Machine learning models have been developed to identify ischaemia and scar on rest and stress myocardial perfusion imaging from positron emission tomography (PET). Application of these tools could aid reporting of PET by highlighting patients and vessels likely to have abnormalities. How this information should be integrated into clinical practice and the impact on patient management or outcomes is not currently known. |
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MeSH term(s) | Humans ; Coronary Artery Disease/diagnostic imaging ; Myocardial Perfusion Imaging/methods ; Coronary Angiography/methods ; Tomography, X-Ray Computed ; Predictive Value of Tests ; Positron-Emission Tomography/methods |
Language | English |
Publishing date | 2024-01-18 |
Publishing country | United States |
Document type | Editorial |
ZDB-ID | 1212505-2 |
ISSN | 1532-6551 ; 1071-3581 |
ISSN (online) | 1532-6551 |
ISSN | 1071-3581 |
DOI | 10.1016/j.nuclcard.2024.101805 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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