Artificial intelligence provides accurate and rapid methods for segmenting and quantifying epicardial and pericoronary adipose tissue, demonstrating potential value in cardiovascular disease diagnosis and risk prediction.
Systematic Review (n=19)
Does artificial intelligence improve the segmentation, quantification, and clinical risk prediction of epicardial and pericoronary adipose tissue imaging?
Artificial intelligence technology provides accurate and rapid methods for segmenting and quantifying cardiac adipose tissue, showing potential to improve cardiovascular disease risk prediction.
BACKGROUND: Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. METHODS: We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation. CONCLUSION: AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging.
Zhang et al. (Mon,) conducted a systematic review in Cardiovascular diseases (epicardial and pericoronary adipose tissue imaging) (n=19). Artificial intelligence (machine learning, deep learning, radiomics) vs. Manual segmentation or traditional clinical risk models was evaluated on Image segmentation, quantification, and clinical application performance (e.g., Dice similarity coefficient, AUC). Artificial intelligence provides accurate and rapid methods for segmenting and quantifying epicardial and pericoronary adipose tissue, demonstrating potential value in cardiovascular disease diagnosis and risk prediction.