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Current and Future Applications of Artificial Intelligence in Cardiac CT

  • Cardiac PET, CT, and MRI (P Cremer, Section Editor)
  • Published:
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Abstract

Purpose of Review

In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications.

Recent Findings

Recent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calcium on cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores.

Summary

Artificial intelligence applications have been applied in all aspects of cardiovascular CT — in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.

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Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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Funding

This work is supported in part by grants from the National Institutes of Health/National Heart, Lung, and Blood Institute [1R01HL148787-01A1 and 1R01HL151266]. MCW (FS/ICRF/20/26002) is supported by the British Heart Foundation. 

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Authors and Affiliations

  1. Department of Medicine, Stanford Healthcare, Palo Alto, CA, USA

    Mugdha Joshi

  2. Division of Cardiovascular Medicine, Stanford Healthcare, Palo Alto, CA, USA

    Diana Patricia Melo

  3. Cedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, CA, USA

    David Ouyang

  4. Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Piotr J. Slomka

  5. British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK

    Michelle C. Williams

  6. Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA

    Damini Dey

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  1. Mugdha Joshi
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Correspondence to Damini Dey.

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Conflict of Interest

Outside the current work, Dr. Dey and Dr. Slomka may receive software royalties from Cedars-Sinai Medical Center. Dr. Williams also reports personal fees from Canon Medical Systems, Siemens Healthineers, and Novartis, outside the submitted work. The remaining authors have no relevant financial interests to disclose.

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Mugdha Joshi and Diana Patricia Melo are co-first authors of this manuscript.

This article is part of the Topical Collection on Cardiac PET, CT, and MRI 

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Joshi, M., Melo, D.P., Ouyang, D. et al. Current and Future Applications of Artificial Intelligence in Cardiac CT. Curr Cardiol Rep 25, 109–117 (2023). https://doi.org/10.1007/s11886-022-01837-8

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