Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature

Pouria Rouzrokh, Bardia Khosravi, Sanaz Vahdati, Mana Moassefi, Shahriar Faghani, Elham Mahmoudi, Hamid Chalian, Bradley J. Erickson

Research output: Contribution to journalReview articlepeer-review


Purpose of Review: In this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI). Recent Findings: During our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research. Summary: ML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML.

Original languageEnglish (US)
Pages (from-to)34-45
Number of pages12
JournalCurrent Radiology Reports
Issue number2
StatePublished - Feb 2023


  • Artificial intelligence
  • Cardiology
  • Cardiovascular imaging
  • Deep learning
  • Machine learning
  • Radiology
  • Scoping review

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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