Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability

Karen Andrea Lara Hernandez, Theresa Margarethe Rienmüller, Daniela Baumgartner, Christian Baumgartner

Research output: Contribution to journalReview articlepeer-review


The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing have not yet found their way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a “clinical level” study. Almost 39% of the articles achieved a “robust candidate” and as many as 61% a “proof of concept” status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method.

Original languageEnglish
Article number104200
Number of pages22
JournalComputers in Biology and Medicine
Publication statusPublished - Mar 2021


  • Cardiovascular imaging
  • Clinical usability
  • Deep learning
  • Spatiotemporal image data

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

Fields of Expertise

  • Human- & Biotechnology


Dive into the research topics of 'Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability'. Together they form a unique fingerprint.

Cite this