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Qualitative stress perfusion American Heart Association plot and outcome prediction using artificial intelligence.

作者信息

Alskaf Ebraham, Scannell Cian M, Crawley Richard, Suinesiaputra Avan, Masci PierGiorgio, Young Alistair, Perera Divaka, Chiribiri Amedeo

机构信息

School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.

Eindhoven University of Technology, Eindhoven, Netherlands.

出版信息

Inform Med Unlocked. 2024;49:101537. doi: 10.1016/j.imu.2024.101537.

DOI:10.1016/j.imu.2024.101537
PMID:39015506
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7616223/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/86f86bb9ad1b/EMS197166-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/9badf8b971b0/EMS197166-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/0443bbe03b39/EMS197166-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/33d2c136081b/EMS197166-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/74c77f620050/EMS197166-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/86f86bb9ad1b/EMS197166-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/9badf8b971b0/EMS197166-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/0443bbe03b39/EMS197166-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/33d2c136081b/EMS197166-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/74c77f620050/EMS197166-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ea9/7616223/86f86bb9ad1b/EMS197166-f005.jpg

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本文引用的文献

1
Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records.利用应力灌注心脏磁共振报告和电子健康记录的自然语言处理进行机器学习结果预测。
Inform Med Unlocked. 2024;44:101418. doi: 10.1016/j.imu.2023.101418.
2
Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.深度学习在心肌灌注成像中的应用：一项系统评价与荟萃分析
Inform Med Unlocked. 2022;32:101055. doi: 10.1016/j.imu.2022.101055.
3
Long-term prognostic value of stress perfusion cardiovascular magnetic resonance in patients without known coronary artery disease.
无已知冠状动脉疾病患者应激灌注心血管磁共振的长期预后价值。
J Cardiovasc Magn Reson. 2021 Apr 8;23(1):43. doi: 10.1186/s12968-021-00737-0.
4
Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN.基于深度卷积神经网络的自动心肌动脉自旋标记分割的准确性、不确定性和适应性
Magn Reson Med. 2020 May;83(5):1863-1874. doi: 10.1002/mrm.28043. Epub 2019 Nov 14.
5
The potential for artificial intelligence in healthcare.人工智能在医疗保健领域的潜力。
Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94.
6
Cardiovascular Risks Associated with Gender and Aging.与性别和衰老相关的心血管风险。
J Cardiovasc Dev Dis. 2019 Apr 27;6(2):19. doi: 10.3390/jcdd6020019.
7
Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance.操作人员培训和静息灌注对压力灌注心血管磁共振诊断准确性的重要性。
J Cardiovasc Magn Reson. 2018 Nov 19;20(1):74. doi: 10.1186/s12968-018-0493-4.
8
Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database.利用日本多中心数据库对人工神经网络进行再训练以检测心肌缺血。
Ann Nucl Med. 2018 Jun;32(5):303-310. doi: 10.1007/s12149-018-1247-y. Epub 2018 Mar 7.
9
Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance.定量压力灌注心脏磁共振的预后价值
JACC Cardiovasc Imaging. 2018 May;11(5):686-694. doi: 10.1016/j.jcmg.2017.07.022. Epub 2017 Nov 15.
10
Improved 5-year prediction of all-cause mortality by coronary CT angiography applying the CONFIRM score.应用CONFIRM评分通过冠状动脉CT血管造影改善全因死亡率的5年预测。
Eur Heart J Cardiovasc Imaging. 2017 Mar 1;18(3):286-293. doi: 10.1093/ehjci/jew195.