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Sex Specific Analysis of Artificial Intelligence-enabled Quantitative Coronary Plaque and Hemodynamic Analysis in Predicting Acute Coronary Syndrome

Bon-Kwon Koo,Seokhun Yang, Jae Wook Jung,Jinlong Zhang,Keehwan Lee,Doyeon Hwang,Kyu-Sun Lee,Joon-Hyung Doh,Chang-Wook Nam, Tae Hyun Kim,Eun-Seok Shin,Eun Ju Chun,Su-Yeon Choi,Hyun Kuk Kim,Young Joon Hong,Hun-Jun Park,Song-Yi Kim,Mirza Husic,Jess Lambrechtsen,Jesper M. Jensen,Bjarne L. Norgaard,Daniele Andreini,Pal Maurovich-Horvat,Bela Merkely,Martin Penicka,Bernard de Bruyne,Abdul Ihdayhid,Brian Ko,Georgios Tzimas,Jonathon Leipsic,Javier Sanz,Mark G. Rabbat, Farhan Katchi,Moneal Shah,Nobuhiro Tanaka,Ryo Nakazato,Taku Asano,Mitsuyasu Terashima,Hiroaki Takashima,Tetsuya Amano,Yoshihiro Sobue,Hitoshi Matsuo,Hiromasa Otake,Takashi Kubo,Masahiro Takahata,Takashi Akasaka,Teruhito Kido,Teruhito Mochizuki,Hiroyoshi Yokoi,Taichi Okonogi,Tomohiro Kawasaki,Koichi Nakao,Tomohiro Sakamoto,Taishi Yonetsu,Tsunekazu Kakuta,Yohei Yamauchi,Jeroen J. Bax,Leslee J. Shaw,Peter H. Stone,Jagat Narula

JACC-CARDIOVASCULAR IMAGING（2024）

Cited 1|Views32
Key words
acute coronary syndrome,artificial intelligence,coronary CT angiography,hemodynamics,plaque characteristics
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