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The machine learning approach: Artificial intelligence is coming to support critical clinical thinking.

作者信息

Nappi Carmela, Cuocolo Alberto

机构信息

Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy.

出版信息

J Nucl Cardiol. 2020 Feb;27(1):156-158. doi: 10.1007/s12350-018-1344-2. Epub 2018 Jun 19.

DOI:10.1007/s12350-018-1344-2
PMID:29923100
Abstract
摘要

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本文引用的文献

1
Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.机器学习在简单变量整合中用于识别心肌缺血患者。
J Nucl Cardiol. 2020 Feb;27(1):147-155. doi: 10.1007/s12350-018-1304-x. Epub 2018 May 22.
2
Machine learning in cardiac CT: Basic concepts and contemporary data.心脏 CT 中的机器学习：基本概念与当代数据。
J Cardiovasc Comput Tomogr. 2018 May-Jun;12(3):192-201. doi: 10.1016/j.jcct.2018.04.010. Epub 2018 Apr 30.
3
Warranty period of normal stress myocardial perfusion imaging in hypertensive patients: A parametric survival analysis.
基于 FDG PET-CT 图像纹理分析的人工智能辅助活动性大动脉炎诊断的方法学框架：初步分析。
J Nucl Cardiol. 2022 Dec;29(6):3315-3331. doi: 10.1007/s12350-022-02927-4. Epub 2022 Mar 23.
4
"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in F-sodium fluoride PET/CT scans: Head-to-head comparison.基于人工智能与手动分割的 F-氟脱氧葡萄糖 PET/CT 扫描评估的“全球”心脏动脉粥样硬化负荷：头对头比较。
J Nucl Cardiol. 2022 Oct;29(5):2531-2539. doi: 10.1007/s12350-021-02758-9. Epub 2021 Aug 12.
5
Nuclear cardiac imaging between implementation and globalization: The key role of integration.核心脏成像从应用到全球化：整合的关键作用。
J Nucl Cardiol. 2021 Jun;28(3):793-795. doi: 10.1007/s12350-021-02633-7. Epub 2021 Apr 30.
6
Behind Traditional Semi-quantitative Scores of Myocardial Perfusion Imaging: An Eye on Niche Parameters.心肌灌注成像传统半定量评分背后：关注小众参数
Eur Cardiol. 2019 Apr;14(1):13-17. doi: 10.15420/ecr.2019.5.1.
高血压患者正常应激心肌灌注成像的保修期：参数生存分析。
J Nucl Cardiol. 2020 Apr;27(2):534-541. doi: 10.1007/s12350-018-1285-9. Epub 2018 Apr 20.
4
Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning.基于机器学习的腺苷心肌灌注 SPECT 后心脏性死亡预测。
J Nucl Cardiol. 2019 Oct;26(5):1746-1754. doi: 10.1007/s12350-018-1250-7. Epub 2018 Mar 14.
5
Machine learning & artificial intelligence in the quantum domain: a review of recent progress.机器学习与量子领域中的人工智能：近期进展综述。
Rep Prog Phys. 2018 Jul;81(7):074001. doi: 10.1088/1361-6633/aab406. Epub 2018 Mar 5.
6
Combined evaluation of regional coronary artery calcium and myocardial perfusion by Rb PET/CT in the identification of obstructive coronary artery disease.放射性核素心肌灌注显像和 CT 冠状动脉成像联合评价在识别阻塞性冠状动脉疾病中的应用。
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7
Added prognostic value of left ventricular shape by gated SPECT imaging in patients with suspected coronary artery disease and normal myocardial perfusion.门控 SPECT 成像对怀疑患有冠心病和正常心肌灌注患者左心室形态的预后价值。
J Nucl Cardiol. 2019 Aug;26(4):1148-1156. doi: 10.1007/s12350-017-1090-x. Epub 2017 Oct 25.
8
Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning.采用机器学习方法的联合临床和心肌灌注成像数据的预后价值。
JACC Cardiovasc Imaging. 2018 Jul;11(7):1000-1009. doi: 10.1016/j.jcmg.2017.07.024. Epub 2017 Oct 18.
9
Prognostic value of atherosclerotic burden and coronary vascular function in patients with suspected coronary artery disease.疑似冠心病患者的动脉粥样硬化负担和冠状动脉功能的预后价值。
Eur J Nucl Med Mol Imaging. 2017 Dec;44(13):2290-2298. doi: 10.1007/s00259-017-3800-7. Epub 2017 Aug 16.
10
Comparison of left ventricular shape by gated SPECT imaging in diabetic and nondiabetic patients with normal myocardial perfusion: A propensity score analysis.门控 SPECT 成像在正常心肌灌注的糖尿病和非糖尿病患者左心室形态比较：倾向评分分析。
J Nucl Cardiol. 2018 Apr;25(2):394-403. doi: 10.1007/s12350-017-1009-6. Epub 2017 Aug 14.