Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review

Federico Greco, Rodrigo Salgado, Wim Van Hecke, Romualdo Del Buono, Paul M. Parizel, Carlo Augusto Mallio

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)


The present review summarizes the available evidence on artificial intelligence (AI) algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on computed tomography (CT) images. Body composition imaging is a novel concept based on quantitative analysis of body tissues. Manual segmentation of medical images allows to obtain quantitative and qualitative data on several tissues including epicardial and pericardial fat. However, since manual segmentation requires a considerable amount of time, the analysis of adipose tissue compartments based on AI has been proposed as an automatic, reliable, accurate and fast tool. The literature research was performed on March 2021 using MEDLINE PubMed Central and "adipose tissue artificial intelligence", "adipose tissue deep learning" or "adipose tissue machine learning" as keywords for articles search. Relevant articles concerning epicardial adipose tissue, pericardial adipose tissue and AI were selected. The evaluation of adipose tissue compartments can provide additional information on the pathogenesis and prognosis of several diseases, including cardiovascular. AI can assist physicians to obtain important information, possibly improving the patient's quality of life and identifying patients at risk of developing variable disorders.

Original languageEnglish
Pages (from-to)2075-2089
Number of pages15
JournalQuantitative Imaging in Medicine and Surgery
Issue number3
Early online dateDec 2021
Publication statusPublished - Mar 2022


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