Why the study?
Manual segmentation of epicardial and pericardial fat on CT images requires considerable time, leading to the proposal of artificial intelligence as an automatic, reliable, accurate, and fast tool.
Do artificial intelligence algorithms provide reliable, accurate, and fast segmentation of epicardial and pericardial adipose tissues on CT images compared to manual segmentation?
Do artificial intelligence algorithms provide reliable, accurate, and fast segmentation of epicardial and pericardial adipose tissues on CT images compared to manual segmentation?
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Artificial intelligence offers an efficient and accurate alternative to manual segmentation for quantifying epicardial and pericardial fat on CT, potentially aiding in cardiovascular risk assessment.
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.
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Greco et al. (2021) studied this question.
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