Greco F, Mallio CA. Artificial intelligence and abdominal adipose tissue analysis: a literature review. Quant Imaging Med Surg. 2021;11:4461–74.
Article PubMed PubMed Central Google Scholar
Armato SG, Drukker K, Hadjiiski L. AI in medical imaging grand challenges: translation from competition to research benefit and patient care. Br J Radiol. 2023;96:20221152.
Article PubMed PubMed Central Google Scholar
Santhanam P, Nath T, Peng C, Bai H, Zhang H, Ahima RS, et al. Artificial intelligence and body composition. Diabetes Metab Syndr. 2023;17:102732.
Article CAS PubMed Google Scholar
Lee M-J, Wu Y, Fried SK. Adipose tissue heterogeneity: implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med. 2013;34:1–11.
Article CAS PubMed Google Scholar
Lemieux I. Energy partitioning in Gluteal-Femoral fat: does the metabolic fate of triglycerides affect coronary heart disease risk? Arterioscler Thromb Vasc Biol. 2004;24:795–7.
Article CAS PubMed Google Scholar
Neeland IJ, Ross R, Després J-P, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7:715–25.
Koenen M, Hill MA, Cohen P, Sowers JR. Obesity, adipose tissue and vascular dysfunction. Circ Res. 2021;128:951–68.
Article CAS PubMed PubMed Central Google Scholar
Ruiz-Castell M, Samouda H, Bocquet V, Fagherazzi G, Stranges S, Huiart L. Estimated visceral adiposity is associated with risk of cardiometabolic conditions in a population based study. Sci Rep. 2021;11:9121.
Article CAS PubMed PubMed Central Google Scholar
Ali MM, Hassan C, Masrur M, Bianco FM, Naquiallah D, Mirza I, et al. Adipose tissue hypoxia correlates with adipokine hypomethylation and vascular dysfunction. Biomedicines. 2021;9:1034.
Article PubMed PubMed Central Google Scholar
McCauley MD, Iacobellis G, Li N, Nattel S, Goldberger JJ. Targeting the substrate for atrial fibrillation: JACC review topic of the week. J Am Coll Cardiol. 2024;83:2015–27.
Article CAS PubMed PubMed Central Google Scholar
Neeland IJ, Yokoo T, Leinhard OD, Lavie CJ. 21st century advances in multimodality imaging of obesity for care of the cardiovascular patient. JACC Cardiovasc Imaging. 2021;14:482–94.
Pandey A, Patel KV, Segar MW, Ayers C, Linge J, Leinhard OD, et al. Effect of liraglutide on thigh muscle fat and muscle composition in adults with overweight or obesity: results from a randomized clinical trial. J Cachexia Sarcopenia Muscle. 2024;15:1072–83.
Article PubMed PubMed Central Google Scholar
Klopfenstein BJ, Kim MS, Krisky CM, Szumowski J, Rooney WD, Purnell JQ. Comparison of 3 T MRI and CT for the measurement of visceral and subcutaneous adipose tissue in humans. Br J Radiol. 2012;85:e826–830.
Article CAS PubMed PubMed Central Google Scholar
Kim J, Kim K. CT-based measurement of visceral adipose tissue volume as a reliable tool for assessing metabolic risk factors in prediabetes across subtypes. Sci Rep. 2023;13:17902.
Article CAS PubMed PubMed Central Google Scholar
Shuster A, Patlas M, Pinthus JH, Mourtzakis M. The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol. 2012;85:1–10.
Article CAS PubMed PubMed Central Google Scholar
Barnes S, Kinne E, Chowdhury S, Loong S, Moretz J, Sabate J. Comparison and precision of visceral adipose tissue measurement techniques in a multisite longitudinal study using MRI. Magn Reson Imaging. 2024;112:82–8.
Mathew DE, Jayakaran JAJ, Hansdak SG, Iyadurai R. Cost effective and adaptable measures of Estimation of visceral adiposity. Clin Epidemiol Glob Health. 2023;23:101362.
Murata H, Yagi T, Midorikawa T, Torii S, Takai E, Taguchi M. Comparison between DXA and MRI for the visceral fat assessment in athletes. Int J Sports Med. 2022;43:625–31.
Article CAS PubMed PubMed Central Google Scholar
Maskarinec G, Shvetsov YB, Wong MC, Garber A, Monroe K, Ernst TM, et al. Subcutaneous and visceral fat assessment by DXA and MRI in older adults and children. Obes Silver Spring Md. 2022;30:920–30.
Bellan M, Menegatti M, Ferrari C, Carnevale Schianca GP, Pirisi M. Ultrasound-assessed visceral fat and associations with glucose homeostasis and cardiovascular risk in clinical practice. Nutr Metab Cardiovasc Dis. 2018;28:610–7.
Article CAS PubMed Google Scholar
Hsu T-MH, Schawkat K, Berkowitz SJ, Wei JL, Makoyeva A, Legare K, et al. Artificial intelligence to assess body composition on routine abdominal CT scans and predict mortality in pancreatic cancer– A recipe for your local application. Eur J Radiol. 2021;142:109834.
Modanwal G, Dhamdhere R, Khera A, de Lemos JA, Peshock R, Browning J, et al. QuLF-CT: A Radiomics-Based tool for quantification of liver fat fraction on cardiac CT. JACC Adv. 2024;3:101175.
Article PubMed PubMed Central Google Scholar
Kuppili V, Biswas M, Sreekumar A, Suri HS, Saba L, Edla DR, et al. Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization. J Med Syst. 2017;41:152.
Elhakim T, Trinh K, Mansur A, Bridge C, Daye D. Role of machine Learning-Based CT body composition in risk prediction and prognostication: current state and future directions. Diagn Basel Switz. 2023;13:968.
Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol. 2023;22:259.
Article PubMed PubMed Central Google Scholar
Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, et al. Transforming cardiovascular care with artificial intelligence: from discovery to practice. J Am Coll Cardiol. 2024;84:97–114.
Article PubMed PubMed Central Google Scholar
Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, et al. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology. 2019;290:669–79.
Yao AD, Cheng DL, Pan I, Kitamura F. Deep learning in neuroradiology: A systematic review of current algorithms and approaches for the new wave of imaging technology. Radiol Artif Intell. 2020;2:e190026.
Article PubMed PubMed Central Google Scholar
Langner T, Hedström A, Mörwald K, Weghuber D, Forslund A, Bergsten P, et al. Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water-fat MRI. Magn Reson Med. 2019;81:2736–45.
Koitka S, Kroll L, Malamutmann E, Oezcelik A, Nensa F. Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks. Eur Radiol. 2021;31:1795–804.
Ding S, Zhu H, Jia W, Su C. A survey on feature extraction for pattern recognition. Artif Intell Rev. 2012;37:169–80.
Xu P, Xue Y, Schoepf UJ, Varga-Szemes A, Griffith J, Yacoub B, et al. Radiomics: the next frontier of cardiac computed tomography. Circ Cardiovasc Imaging. 2021;14:e011747.
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