AI Article Synopsis
- Recent advancements in cardiac CT have significantly enhanced image quality while minimizing radiation exposure for patients.
- Key findings from large studies have clarified the relationship between cardiovascular disease risk, overall coronary plaque burden, and the characteristics of individual plaques.
- The integration of machine learning into cardiac CT is anticipated to evolve the modality further, with potential improvements in diagnostic accuracy and patient outcomes over the next decade.
Article Abstract
Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066830 | PMC |
| http://dx.doi.org/10.15420/ecr.2019.14.2 | DOI Listing |
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