Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

[1] Abe I(2018)Association of fibrotic remodeling and cytokines/chemokines content in epicardial adipose tissue with atrial myocardial fibrosis in patients with atrial fibrillation Heart Rhythm 15 1717-1727

[2] Teshima Y(2017)Detecting human coronary inflammation by imaging perivascular fat Sci Transl Med 9 eaal2658-e202

[3] Kondo H(2020)Machine and deep learning methods for radiomics Med Phys 47 e185-1663

[4] Kaku H(2018)Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study JACC Cardiovasc Imaging 11 1654-2131

[5] Kira S(2017)Deep learning: a primer for radiologists RadioGraphics 37 2113-274

[6] Ikebe Y(2008)Adipocytokines and proinflammatory mediators from abdominal and epicardial adipose tissue in patients with coronary artery disease Int J Obes 32 268-E949

[7] Saito S(2012)Cellular cross-talk between epicardial adipose tissue and myocardium in relation to the pathogenesis of cardiovascular disease Am J Physiol Endocrinol Metab 303 E937-1846

[8] Fukui A(2018)Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT IEEE Trans Med Imaging 37 1835-2225

[9] Shinohara T(2020)Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study Cardiovasc Res 116 2216-141

[10] Yufu K(2010)Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and metabolic syndrome Atherosclerosis 209 136-1082

[1] Abe I(2018)Association of fibrotic remodeling and cytokines/chemokines content in epicardial adipose tissue with atrial myocardial fibrosis in patients with atrial fibrillation Heart Rhythm 15 1717-1727

[2] Teshima Y(2017)Detecting human coronary inflammation by imaging perivascular fat Sci Transl Med 9 eaal2658-e202

[3] Kondo H(2020)Machine and deep learning methods for radiomics Med Phys 47 e185-1663

[4] Kaku H(2018)Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study JACC Cardiovasc Imaging 11 1654-2131

[5] Kira S(2017)Deep learning: a primer for radiologists RadioGraphics 37 2113-274

[6] Ikebe Y(2008)Adipocytokines and proinflammatory mediators from abdominal and epicardial adipose tissue in patients with coronary artery disease Int J Obes 32 268-E949

[7] Saito S(2012)Cellular cross-talk between epicardial adipose tissue and myocardium in relation to the pathogenesis of cardiovascular disease Am J Physiol Endocrinol Metab 303 E937-1846

[8] Fukui A(2018)Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT IEEE Trans Med Imaging 37 1835-2225

[9] Shinohara T(2020)Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study Cardiovasc Res 116 2216-141

[10] Yufu K(2010)Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and metabolic syndrome Atherosclerosis 209 136-1082