[1]艾克力亚尔·艾尼瓦尔 马翔.机器学习CNN模型在心血管疾病诊疗中的临床应用及研究进展[J].心血管病学进展,2021,(6):488.[doi:10.16806/j.cnki.issn.1004-3934.2021.06.003]
 Aikeliyaer.ainiwaerMA xiang.Application of Convolutional Neural Network Model of Machine Learning for the Diagnosis and Treatment of Cardiovascular Diseases[J].Advances in Cardiovascular Diseases,2021,(6):488.[doi:10.16806/j.cnki.issn.1004-3934.2021.06.003]
点击复制

机器学习CNN模型在心血管疾病诊疗中的临床应用及研究进展()
分享到:

《心血管病学进展》[ISSN:51-1187/R/CN:1004-3934]

卷:
期数:
2021年6期
页码:
488
栏目:
综述
出版日期:
2021-06-25

文章信息/Info

Title:
Application of Convolutional Neural Network Model of Machine Learning for the Diagnosis and Treatment of Cardiovascular Diseases
作者:
艾克力亚尔·艾尼瓦尔1 马翔12
(1.新疆医科大学研究生院,新疆 乌鲁木齐 830054;2.新疆医科大学第一附属医院,新疆 乌鲁木齐 830054)
Author(s):
Aikeliyaer.ainiwaer1MA xiang 12
(1.Xinjiang Medical University Graduate SchoolUrumqi 830054,Xinjiang,China;2.The First Affiliated Hospital of Xinjiang Medical UniversityUrumqi 830054,Xinjiang,China)
关键词:
机器学习深度学习卷积神经网络心血管疾病
Keywords:
Deep learningConvolutional neural networkCardiovascular?disease
DOI:
10.16806/j.cnki.issn.1004-3934.2021.06.003
摘要:
近年来随着计算机技术的发展,机器学习领域的发展越来越迅速,因具有模仿人脑解释数据的功能,借助计算机强大的整合数据的能力,被广泛地应用于临床,为疾病的诊断、预后分析、诊疗决策的制定等方面提供了方便。现简要概述用于构建推论和预测数据驱动的CNN模型在心血管疾病诊疗中的临床应用及研究进展,分析该模型在心血管疾病应用上的优势以及不足。
Abstract:
In recent yearswith the development of computer technology,developments in the field of machine learning speadly,with the advantage of imitating the human brain to explain the function of data,strong ability to integrate data,computer is widely used in clinical diagnosis?and?treatment. In this review,we briefly summarized the clinical application and research progress of the machine learning CNN model used to construct inference and prediction data-driven model in the diagnosis and treatment of cardiovascular diseases,and proposed its advantages and disadvantages in the application of cardiovascular diseases

参考文献/References:

[1] Lee G,Fujita H . Deep learning in medical image analysis: challenges and applications[M]. Springer Nature,2020,1213:18-19.
[2] Lin S,Li Z,Fu B,et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo[J].?Eur Heart J,2020,41(46):4400-4411.
[3] Avram R,Olgin JE,Kuhar P,et al. A digital biomarker of diabetes from smartphone-based vascular signals[J].?Nat Med,2020,26(10):1576-1582.
[4] Srinivasan S,Greenspan RJ,Stevens CF,et al. Deep(er) Learning[J].J Neurosci,2018,38(34):7365-7374.
[5] LeCun Y,Bengio Y,Hinton G. Deep learning[J]. Nature,2015,521(7553):436–444.
[6] Kolossváry M,de Cecco CN,Feuchtner G,et al. Advanced atherosclerosis imaging by CT: Radiomics,machine learning and deep learning[J]. J Cardiovasc Comput Tomogr,2019,13(5):274-280.
[7] Lundervold AS,Lundervold A. An overview of deep learning in medical imaging focusing on MRI[J]. Z Med Phys,2019,29(2):102-127.
[8] Gandhi S,Mosleh W,Shen J,et al. Automation,machine learning,and artificial intelligence in echocardiography: A brave new world. Echocardiography[J]. 2018,35(9):1402-1418.
[9] Khatibi T,Rabinezhadsadatmahaleh N. Proposing feature engineering method based on deep learning and K-NNs for ECG beat classification and arrhythmia detection[J]. Phys Eng Sci Med,2020,43:49-68.
[10]Kusunose K. Radiomics in echocardiography: deep learning and echocardiographic analysis[J]. Curr Cardiol Rep,2020,22(9):89.
[11] Rawat W,Wang Z. Deep convolutional neural networks for image classification: a comprehensive review[J]. Neural Comput,2017,29(9):2352-2449.
[12] Schwendicke F,Golla T,Dreher M,et al. Convolutional neural networks for dental image diagnostics:a scoping review[J]. J Dent,2019,99(7):769-774.
[13] Tatsugami F,Higaki T,Nakamura Y,et al. Deep learning–based image restoration algorithm for coronary CT angiography[J]. Eur Radiol,2019,29(10):5322-5329.
[14] Bruns S,Wolterink JM,Takx RAP,et al. Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT[J].Med Phys,2020,47(10):5048-5060.
[15] 蒋建慧,姚静,张艳娟,等.基于深度学习的超声自动测量左室射血分数的研究[J].临床超声医学杂志,2019,021(1):70-74.
[16] Kusunose K,Abe T,Haga A,et al.A Deep learning approach for assessment of regional wall motion?abnormality from echocardiographic images[J]. JACC Cardiovasc Imaging,2020,13(2 Pt 1):374-381.
[17]Zhang,J,Gajjala S,Agrawal P,et al. Fully automated echocardiogram interpretation in clinical practice feasibility and diagnostic accuracy[J]. Circulation,2018,138(16):1623-1635.
[18] Liu W,Wang F,Huang Q,et al. MFB-CBRNN: a hybrid network for MI detection using 12-lead ECGs[J]. IEEE J Biomed Health Inform,2020,24(2):503-514.
[19]Siontis KC,Yao X,Pirruccello JP,et al. How will machine learning inform the clinical care of atrial fibrillation? [J].?Circ Res,2020,127(1):155-169.
[20] Belo D,Bento N,Silva H,et al. ECG biometrics using deep learning and relative score threshold classification[J].?Sensors (Basel),2020,20(15):4078.
[21] Zhao Z, Liu C, Li Y,et al. Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolutional neural networks [J]. IEEE Access,2019,7(1): 34060-34067.
[22] Zhao L,Liu C,Wei S,et al.Enhancing detection accuracy for clinical heart failure utilizing pulse transit time variability and machine learning[J]. IEEE Access,2019,7(1):17716-17724.
[23] Huang J,Chen B,Yao B,et al.ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network[J]. IEEE Access,2019,7(1): 92871-92880.
[24] Wo?k K,Wo?k A.Early and remote detection of possible heartbeat problems with convolutional neural networks and multipart interactive training[J]. IEEE Access,2019,7(1):145921-145927.
[25] Khatibi T,Rabinezhadsadatmahaleh N. Proposing feature engineering method based on deep learning and K-NNs for ECG beat classification and arrhythmia detection[J]. Australas Phys Eng Sci Med,2020,43(1):49-68.
[26]Wang P,Hou B,Shao S,et al. ECG arrhythmias detection using auxiliary classifier generative adversarial network and residual network[J]. IEEE Access,2019,7(99):100910-100922.
[27] Poldervaart JM,Langedijk M,Backus BE,et al. Comparison of the GRACE,HEART and TIMI score to predict major adverse cardiac events in chest pain patients at the emergency department[J]. Int J Cardiol,2017,227:656-661.
[28] Kwon J,Jeon K,Kim HM,et al. Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction[J]. PLoS One,2019,14(10): e0224502.
[29] Huo D,Kou B,Zhou Z,et al. A machine learning model to classify aortic dissection patients in the early diagnosis phase[J]. Sci Rep,2019,9(1):1-8.
[30]Santos-Ferreira C,Baptista R,Oliveira-Santos M,et al.A 10- and 15-year performance analysis of ESC/EAS and ACC/AHA cardiovascular risk scores in a Southern European cohort[J]. BMC Cardiovasc Disord,2020,20(1):301.
[31] Wu CC,Hsu WD,Islam MM,et al. An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain[J]. Comput Methods Programs Biomed,2019,173:109-117.
[32] Kwon JM,Kim KH,Jeon KH,et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure[J].PLoS One,2019,14(7):e0219302
[33] Zack CJ, Senecal C, Kinar Y,et al. Leveraging machine learning techniques to forecast patient prognosis after percutaneous coronary intervention[J]. JACC Cardiovasc Interv,2019,12(14):1304-1311.
[34] Commandeur F,Slomka PJ,Goeller M,et al.Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk,coronary calcium,and epicardial adipose tissue: a prospective study[J]. Cardiovasc Res,2020,116(14):2216-2225.?
[35] Novak R,Xiao L,Hron J,et al. Neural tangents: fast and easy infinite neural networks in python[C]. Seattle:International conference on learning representations, arXiv,2020.

相似文献/References:

[1]渠海贤 李涛 程流泉.人工智能在心脏磁共振成像中的应用进展[J].心血管病学进展,2019,(5):659.[doi:10.16806/j.cnki.issn.1004-3934.2019.05.001]
[2]沈文茜 杜国庆.机器学习在超声心动图中的应用进展[J].心血管病学进展,2021,(1):43.[doi:10.16806/j.cnki.issn.1004-3934.2021.01.000]
 SHEN Wenqian,DU Guoqing.Machine Learning in Echocardiography[J].Advances in Cardiovascular Diseases,2021,(6):43.[doi:10.16806/j.cnki.issn.1004-3934.2021.01.000]
[3]凯赛尔江·卡地尔 艾克力亚尔·艾尼瓦尔 秦练 热娜·热合木丁 马翔.深度学习在冠心病影像学诊断的研究进展[J].心血管病学进展,2022,(4):335.[doi:10.16806/j.cnki.issn.1004-3934.2022.04.012]
 Kaisaierjiang Kadier,Aikeliyaer Ainiwae,rQIN Lian,et al.Deep Learning in Imaging Diagnosis of Coronary Heart Disease[J].Advances in Cardiovascular Diseases,2022,(6):335.[doi:10.16806/j.cnki.issn.1004-3934.2022.04.012]
[4]黄佳星 王猛 江洪.人工智能神经活性分析研究进展[J].心血管病学进展,2022,(6):538.[doi:10.16806/j.cnki.issn.1004-3934.20.06.015]
 HUANG JiaxingWANG MengJIANG Hong.Artificial Intelligence and Neural Activity Analysis[J].Advances in Cardiovascular Diseases,2022,(6):538.[doi:10.16806/j.cnki.issn.1004-3934.20.06.015]
[5]秦地茂 李梦依 吴霜 邓祁 姚尧 刘英杰 郑颖.人工智能在心房颤动预测中的价值[J].心血管病学进展,2022,(10):874.[doi:10.16806/j.issn.1004-3934.2022.10.003]
 QIN Dimao,LI Mengyi,WU Shuang,et al.Artificial Intelligence for Predicting Atrial Fibrillation[J].Advances in Cardiovascular Diseases,2022,(6):874.[doi:10.16806/j.issn.1004-3934.2022.10.003]
[6]林锡祥 杨菲菲 陈煦 何昆仑.人工智能赋能医学影像在先天性心脏病医学诊治中的研究进展[J].心血管病学进展,2022,(12):1063.[doi:10.16806/j.cnki.issn.1004-3934.2022.12.002]
 LIN Xixiang,YANG Feifei,CHEN Xu,et al.Artificial Intelligence Medical Imaging Technology in Medical Imaging of Congenital Heart Disease[J].Advances in Cardiovascular Diseases,2022,(6):1063.[doi:10.16806/j.cnki.issn.1004-3934.2022.12.002]
[7]屈展 刘凯.人工智能技术在高血压诊疗中的应用进展[J].心血管病学进展,2023,(1):48.[doi:10.16806/j.cnki.issn.1004-3934.2023.01.012]
 QU Zhan,LIU Kai.Applications of Artificial Intelligence for Hypertension Management[J].Advances in Cardiovascular Diseases,2023,(6):48.[doi:10.16806/j.cnki.issn.1004-3934.2023.01.012]

备注/Memo

备注/Memo:
收稿日期:2020-08-25 基金项目:基金()
更新日期/Last Update: 2021-07-22