Machine learning models for PET myocardial perfusion imaging

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Article number101805
JournalJournal of Nuclear Cardiology
Early online date18 Jan 2024
Publication statusPublished - 27 Feb 2024

Keywords / Materials (for Non-textual outputs)

  • Positron emission tomography
  • Machine learning
  • Artificial intelligence
  • Coronary artery disease
  • Myocardial perfusion


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