Lu Zhang1, Jianqing Sun2, Beibei Jiang1, Lingyun Wang1, Yaping Zhang1, Xueqian Xie3. 1. Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China. 2. Shukun (Beijing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing, 100102, China. 3. Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China. xiexueqian@hotmail.com.
Abstract
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.
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.
Authors: Gabriel Chartrand; Phillip M Cheng; Eugene Vorontsov; Michal Drozdzal; Simon Turcotte; Christopher J Pal; Samuel Kadoury; An Tang Journal: Radiographics Date: 2017 Nov-Dec Impact factor: 5.333
Authors: Frederic Commandeur; Piotr J Slomka; Markus Goeller; Xi Chen; Sebastien Cadet; Aryabod Razipour; Priscilla McElhinney; Heidi Gransar; Stephanie Cantu; Robert J H Miller; Alan Rozanski; Stephan Achenbach; Balaji K Tamarappoo; Daniel S Berman; Damini Dey Journal: Cardiovasc Res Date: 2020-12-01 Impact factor: 10.787
Authors: Markus Goeller; Stephan Achenbach; Mohamed Marwan; Mhairi K Doris; Sebastien Cadet; Frederic Commandeur; Xi Chen; Piotr J Slomka; Heidi Gransar; J Jane Cao; Nathan D Wong; Moritz H Albrecht; Alan Rozanski; Balaji K Tamarappoo; Daniel S Berman; Damini Dey Journal: J Cardiovasc Comput Tomogr Date: 2017-11-24
Authors: Julian Betancur; Frederic Commandeur; Mahsaw Motlagh; Tali Sharir; Andrew J Einstein; Sabahat Bokhari; Mathews B Fish; Terrence D Ruddy; Philipp Kaufmann; Albert J Sinusas; Edward J Miller; Timothy M Bateman; Sharmila Dorbala; Marcelo Di Carli; Guido Germano; Yuka Otaki; Balaji K Tamarappoo; Damini Dey; Daniel S Berman; Piotr J Slomka Journal: JACC Cardiovasc Imaging Date: 2018-03-14
Authors: Damini Dey; Nathan D Wong; Balaji Tamarappoo; Ryo Nakazato; Heidi Gransar; Victor Y Cheng; Amit Ramesh; Ioannis Kakadiaris; Guido Germano; Piotr J Slomka; Daniel S Berman Journal: Atherosclerosis Date: 2009-08-21 Impact factor: 5.162
Authors: Frederic Commandeur; Markus Goeller; Julian Betancur; Sebastien Cadet; Mhairi Doris; Xi Chen; Daniel S Berman; Piotr J Slomka; Balaji K Tamarappoo; Damini Dey Journal: IEEE Trans Med Imaging Date: 2018-02-09 Impact factor: 10.048
Authors: Michele Avanzo; Lise Wei; Joseph Stancanello; Martin Vallières; Arvind Rao; Olivier Morin; Sarah A Mattonen; Issam El Naqa Journal: Med Phys Date: 2020-06 Impact factor: 4.071
Authors: David Molnar; Olof Enqvist; Johannes Ulén; Måns Larsson; John Brandberg; Åse A Johnsson; Elias Björnson; Göran Bergström; Ola Hjelmgren Journal: Sci Rep Date: 2021-12-13 Impact factor: 4.379