心血管影像人工智能的研究进展
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摘要
心血管疾病是我国居民的首位死因,发病率呈逐年上升趋势。近年来人工智能技术快速发展,如何将人工智能技术与心血管医学影像更好地结合,并深入地参与到心血管疾病的诊治是未来研究的重点与热点。作者将人工智能在心血管影像方面的应用与发展初概进行综述。
Cardiovascular disease is the first cause of death in China, accompanied with increasing incidence year by year. In recent years, with the rapid development of artificial intelligence, how to better integrate artificial intelligence with cardiovascular imaging, and subsequently participate in the diagnosis and treatment of cardiovascular diseases is the key point and hotspots of future research. This paper will review the application and development of artificial intelligence in cardiovascular imaging.
Cardiovascular disease is the first cause of death in China, accompanied with increasing incidence year by year. In recent years, with the rapid development of artificial intelligence, how to better integrate artificial intelligence with cardiovascular imaging, and subsequently participate in the diagnosis and treatment of cardiovascular diseases is the key point and hotspots of future research. This paper will review the application and development of artificial intelligence in cardiovascular imaging.
引文
[1]Esteva A,Kuprel B,Novoa R A,et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature,2017,542(7639):115-118.
[2]Ting D,Cheung CY,Lim G,et al.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA,2017,318(22):2211-2223.
[3]Miller DD,Brown EW.Artificial intelligence in medical practice:the question to the answer?Am J Med,2018,131(2):129-133.
[4]Antman EM,Loscalzo J.Precision medicine in cardiology.Nat Rev Cardiol,2016,13(10):591-602.
[5]Brandstetter A,Artusi A.Radial basis function networks GPU-based implementation.IEEE Trans Neural Netw,2008,19(12):2150-2154.
[6]Agoston DV,Langford D.Big Data in traumatic brain injury,promise and challenges.Concussion,2017,2(4):CNC45.
[7]Podolsky MD,Barchuk AA,Kuznetcov VI,et al.Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels.Asian Pac J Cancer Prev,2016,17(2):835-838.
[8]Stulp F,Sigaud O.Many regression algorithms,one unified model:a review.Neural Netw,2015,69:60-79.
[9]Rodriguez A,Laio A.Machine learning.Clustering by fast search and find of density peaks.Science,2014,344(6191):1492-1496.
[10]Thomas M,De Brabanter K,De Moor B.New bandwidth selection criterion for Kernel PCA:approach to dimensionality reduction and classification problems.BMC Bioinformatics,2014,15:137.
[11]Kriegeskorte N.Deep neural networks:a new framework for modeling biological vision and brain information processing.Annu Rev Vis Sci,2015,1:417-446.
[12]Zhong B,Pan S,Zhang H,et al.Convolutional deep belief networks for single-cell/object tracking in computational biology and computer vision.Biomed Res Int,2016,2016:9406259.
[13]Qin C,Hajnal JV,Rueckert D,et al.Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction.IEEE Trans Med Imaging,2019,38(1):280-290.
[14]Schlemper J,Caballero J,Hajnal JV,et al.A deep cascade of convolutional neural networks for dynamic mr image reconstruction.IEEE Trans Med Imaging,2018,37(2):491-503.
[15]Knackstedt C,Bekkers SC,Schummers G,et al.Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain:the FAST-EFs multicenter study.J Am Coll Cardiol,2015,66(13):1456-1466.
[16]Avendi MR,Kheradvar A,Jafarkhani H.A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.Med Image Anal.2016,30:108-119.
[17]Lekadir K,Galimzianova A,Betriu A,et al.A Convolutional neural network for automatic characterization of plaque composition in carotid ultrasound.IEEE J Biomed Health Inform,2017,21(1):48-55.
[18]Wolterink JM,Leiner T,de Vos BD,et al.Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.Med Image Anal,2016,34:123-136.
[19]Narula S,Shameer K,Salem OA,et al.Machine-learning algorithms to automate morphological and functional assessments in 2Dechocardiography.J Am Coll Cardiol,2016,68(21):2287-2295.
[20]Sengupta PP,Huang YM,Bansal M,et al.Cognitive machinelearning algorithm for cardiac imaging:a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy.Circ Cardiovasc Imaging,2016,9(6 Pii):e004330.
[21]Baessler B,Mannil M,Oebel S,et al.Subacute and chronic left ventricular myocardial scar:accuracy of texture analysis on nonenhanced cine MR images.Radiology,2018,286(1):103-112.
[22]Zreik M,Lessmann N,van Hamersvelt RW,et al.Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis.Med Image Anal,2018,44:72-85.
[23]Motwani M,Dey D,Berman DS,et al.Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease:a 5-year multicentre prospective registry analysis.Eur Heart J,2017,38(7):500-507.
[24]van Rosendael AR,Maliakal G,Kolli KK,et al.Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification;insights from the CONFIRM registry.J Cardiovasc Comput Tomogr,2018,12(3):204-209.
[25]Dawes T,de Marvao A,Shi W,et al.Machine learning of threedimensional right ventricular motion enables outcome prediction in pulmonary hypertension:a cardiac MR imaging study.Radiology,2017,283(2):381-390.
[26]Arsanjani R,Dey D,Khachatryan T,et al.Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.J Nucl Cardiol,2015,22(5):877-884.
[27]Tsubaki M,Tomii K,Sese J.Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.Bioinformatics,2019,35(2):309-318.
[28]Pang S,Yu Z,Orgun MA.A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.Comput Methods Programs Biomed,2017,140:283-293.
[2]Ting D,Cheung CY,Lim G,et al.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA,2017,318(22):2211-2223.
[3]Miller DD,Brown EW.Artificial intelligence in medical practice:the question to the answer?Am J Med,2018,131(2):129-133.
[4]Antman EM,Loscalzo J.Precision medicine in cardiology.Nat Rev Cardiol,2016,13(10):591-602.
[5]Brandstetter A,Artusi A.Radial basis function networks GPU-based implementation.IEEE Trans Neural Netw,2008,19(12):2150-2154.
[6]Agoston DV,Langford D.Big Data in traumatic brain injury,promise and challenges.Concussion,2017,2(4):CNC45.
[7]Podolsky MD,Barchuk AA,Kuznetcov VI,et al.Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels.Asian Pac J Cancer Prev,2016,17(2):835-838.
[8]Stulp F,Sigaud O.Many regression algorithms,one unified model:a review.Neural Netw,2015,69:60-79.
[9]Rodriguez A,Laio A.Machine learning.Clustering by fast search and find of density peaks.Science,2014,344(6191):1492-1496.
[10]Thomas M,De Brabanter K,De Moor B.New bandwidth selection criterion for Kernel PCA:approach to dimensionality reduction and classification problems.BMC Bioinformatics,2014,15:137.
[11]Kriegeskorte N.Deep neural networks:a new framework for modeling biological vision and brain information processing.Annu Rev Vis Sci,2015,1:417-446.
[12]Zhong B,Pan S,Zhang H,et al.Convolutional deep belief networks for single-cell/object tracking in computational biology and computer vision.Biomed Res Int,2016,2016:9406259.
[13]Qin C,Hajnal JV,Rueckert D,et al.Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction.IEEE Trans Med Imaging,2019,38(1):280-290.
[14]Schlemper J,Caballero J,Hajnal JV,et al.A deep cascade of convolutional neural networks for dynamic mr image reconstruction.IEEE Trans Med Imaging,2018,37(2):491-503.
[15]Knackstedt C,Bekkers SC,Schummers G,et al.Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain:the FAST-EFs multicenter study.J Am Coll Cardiol,2015,66(13):1456-1466.
[16]Avendi MR,Kheradvar A,Jafarkhani H.A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.Med Image Anal.2016,30:108-119.
[17]Lekadir K,Galimzianova A,Betriu A,et al.A Convolutional neural network for automatic characterization of plaque composition in carotid ultrasound.IEEE J Biomed Health Inform,2017,21(1):48-55.
[18]Wolterink JM,Leiner T,de Vos BD,et al.Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.Med Image Anal,2016,34:123-136.
[19]Narula S,Shameer K,Salem OA,et al.Machine-learning algorithms to automate morphological and functional assessments in 2Dechocardiography.J Am Coll Cardiol,2016,68(21):2287-2295.
[20]Sengupta PP,Huang YM,Bansal M,et al.Cognitive machinelearning algorithm for cardiac imaging:a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy.Circ Cardiovasc Imaging,2016,9(6 Pii):e004330.
[21]Baessler B,Mannil M,Oebel S,et al.Subacute and chronic left ventricular myocardial scar:accuracy of texture analysis on nonenhanced cine MR images.Radiology,2018,286(1):103-112.
[22]Zreik M,Lessmann N,van Hamersvelt RW,et al.Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis.Med Image Anal,2018,44:72-85.
[23]Motwani M,Dey D,Berman DS,et al.Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease:a 5-year multicentre prospective registry analysis.Eur Heart J,2017,38(7):500-507.
[24]van Rosendael AR,Maliakal G,Kolli KK,et al.Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification;insights from the CONFIRM registry.J Cardiovasc Comput Tomogr,2018,12(3):204-209.
[25]Dawes T,de Marvao A,Shi W,et al.Machine learning of threedimensional right ventricular motion enables outcome prediction in pulmonary hypertension:a cardiac MR imaging study.Radiology,2017,283(2):381-390.
[26]Arsanjani R,Dey D,Khachatryan T,et al.Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.J Nucl Cardiol,2015,22(5):877-884.
[27]Tsubaki M,Tomii K,Sese J.Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.Bioinformatics,2019,35(2):309-318.
[28]Pang S,Yu Z,Orgun MA.A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.Comput Methods Programs Biomed,2017,140:283-293.