Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Abstract

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

“…In particular, a deep convolutional NN improved the detection rate of myocardial perfusion abnormalities from stress/rest SPECT performed with 99m Tc-Tetrofosmin or 99m Tc-Sestamibi with an AUC of 0.872 [76]. ML may also be used in analyzing PET myocardial perfusion data to predict the risk of adverse cardiovascular events [77].…”

Artificial intelligence with deep learning in nuclear medicine and radiology

“…Artificial intelligence techniques are applied to many cardiac diagnostic applications including myocardial infarction, cardiomyopathies, coronary artery diseases, valvular heart diseases, etc. [ 164 , 165 ]. An important step in the detection and diagnosis of CVD is motion tracking and segmentation of the main chambers [ 166 – 175 ].…”

Artificial intelligence in nuclear cardiology: Preparing for the fifth industrial revolution

“…13 Although some have, 13 most of these applications have yet to make it to widespread commercial distribution due to the recency of their developments, most reported in 2020. Excellent, recent comprehensive articles 18,19 provide a more complete review of the AI applications that have arrived in cardiovascular imaging.…”