Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

被引：30

作者：

Tatsugami, Fuminari ^{[1
]}

Nakaura, Takeshi ^{[2
]}

Yanagawa, Masahiro ^{[3
]}

Fujita, Shohei ^{[4
,5
]}

Kamagata, Koji ^{[6
]}

Ito, Rintaro ^{[7
]}

Kawamura, Mariko ^{[7
]}

Fushimi, Yasutaka ^{[8
]}

Ueda, Daiju ^{[9
]}

Matsui, Yusuke ^{[10
]}

Yamada, Akira ^{[11
]}

Fujima, Noriyuki ^{[12
]}

Fujioka, Tomoyuki ^{[13
]}

Nozaki, Taiki ^{[14
]}

Tsuboyama, Takahiro ^{[3
]}

Hirata, Kenji ^{[15
]}

Naganawa, Shinji ^{[7
]}

机构：

[1] Hiroshima Univ, Dept Diagnost Radiol, Minami Ku, 1-2-3 Kasumi, Hiroshima 7348551, Japan

[2] Kumamoto Univ, Grad Sch Med, Dept Diagnost Radiol, Chuo Ku, 1-1-1 Honjo, Kumamoto 8608556, Japan

[3] Osaka Univ, Grad Sch Med, Dept Radiol, 2-2 Yamadaoka, Suita, Osaka 5650871, Japan

[4] Univ Tokyo, Dept Radiol, Grad Sch Med, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138655, Japan

[5] Univ Tokyo, Fac Med, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138655, Japan

[6] Juntendo Univ, Grad Sch Med, Dept Radiol, Bunkyo Ku, Tokyo 1138421, Japan

[7] Nagoya Univ, Grad Sch Med, Dept Radiol, Showa Ku, 65 Tsurumai Cho, Nagoya, Aichi 4668550, Japan

[8] Kyoto Univ, Grad Sch Med, Dept Diagnost Imaging & Nucl Med, Sakyo Ku, 54 Shogoin Kawaharacho, Kyoto 6068507, Japan

[9] Osaka Metropolitan Univ, Grad Sch Med, Dept Diagnost & Intervent Radiol, Abeno Ku, 1-4-3 Asahi Machi, Osaka 5458585, Japan

[10] Okayama Univ, Fac Med Dent & Pharmaceut Sci, Dept Radiol, Kita Ku, 2-5-1 Shikata Cho, Okayama 7008558, Japan

[11] Shinshu Univ, Sch Med, Dept Radiol, 3-1-1 Asahi, Matsumoto, Nagano 3908621, Japan

[12] Hokkaido Univ Hosp, Dept Diagnost & Intervent Radiol, Kita Ku, N15,W5, Sapporo 0608638, Japan

[13] Tokyo Med & Dent Univ, Dept Diagnost Radiol, Bunkyo Ku, 1-5-45 Yushima, Tokyo 1138519, Japan

[14] Keio Univ, Sch Med, Dept Radiol, Shinjuku Ku, 35 Shinanomachi, Tokyo 1600016, Japan

[15] Hokkaido Univ, Grad Sch Med, Dept Diagnost Imaging, Kita Ku, Kita 15, Nishi 7, Sapporo, Hokkaido 0608648, Japan

来源：

DIAGNOSTIC AND INTERVENTIONAL IMAGING | 2023年 / 104卷 / 11期

关键词：

Arti ficial intelligence; Cardiac computed tomography; Cardiac imaging; Deep learning; Machine learning; FRACTIONAL FLOW RESERVE; CORONARY-ARTERY-DISEASE; CONVOLUTIONAL NEURAL-NETWORK; MOTION CORRECTION ALGORITHM; IMAGE QUALITY; COMPUTED-TOMOGRAPHY; ANGIOGRAPHY; RECONSTRUCTION; PERFORMANCE; SEGMENTATION;

D O I：

10.1016/j.diii.2023.06.011

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges. (c) 2023 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.

引用

页码：521 / 528

页数：8

共 114 条

[1] Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review [J].