Abstract
The use of quantitative analysis in single photon emission computed tomography (SPECT) and positron emission tomography (PET) has become an integral part of current clinical practice and plays a crucial role in the detection and risk stratification of coronary artery disease. Emerging technologies, new protocols, and new quantification methods have had a significant impact on the diagnostic performance and prognostic value of nuclear cardiology imaging, while reducing the need for clinician oversight. In this review, we aim to describe recent advances in automation and quantitative analysis in nuclear cardiology. Recent publications have shown that fully automatic processing is feasible, limiting human input to specific cases where aberrancies are detected by the quality control software. Furthermore, there is evidence indicating that fully quantitative analysis of myocardial perfusion imaging is feasible and can achieve at least similar diagnostic accuracy as visual interpretation by an expert clinician. In addition, the use of fully automated quantification in combination with machine learning algorithms can provide incremental diagnostic and prognostic value over the traditional method of expert visual interpretation. Emerging technologies in nuclear cardiology focus on automation and the use of artificial intelligence as part of the interpretation process. This review highlights the benefits and limitations of these applications, and outlines future directions in the field.
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Published Version
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