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Application of deep learning-based magnetic resonance imaging in the diagnosis and treatment of coronary artery disease
WU Qian  GUO Hui 

Cite this article as: WU Q, GUO H. Application of deep learning-based magnetic resonance imaging in the diagnosis and treatment of coronary artery disease[J]. Chin J Magn Reson Imaging, 2024, 15(11): 190-197. DOI:10.12015/issn.1674-8034.2024.11.030.


[Abstract] With the increasing trend of population aging and changes in modern lifestyle, the prevalence of coronary artery disease is increasing year by year and gradually showing a younger trend, making it one of the most common fatal diseases in the world today. Traditional imaging technology can no longer meet the demand of the increasing and increasingly complex cases year by year, and the pressure of physicians' is increasing dramatically. In recent years, the rapid development of artificial intelligence has effectively improved the efficiency and accuracy of physicians' work, and the combination of various emerging artificial intelligence technology and imaging equipment has achieved positive results in clinical practice, showing a bright future for development, especially the non-invasive cardiac magnetic resonance technology that can simultaneously evaluate the structure and function of heart. This paper summarizes the research status, progress and limitations of the combination of deep learning and magnetic resonance in the diagnosis and treatment of coronary artery disease, aiming to improve the efficiency and accuracy of physicians' diagnosis and treatment, promote the timely diagnosis and early intervention of coronary artery disease, and promote the development and progress of artificial intelligence in the field of imaging medicine in China.
[Keywords] coronary artery disease;cardiovascular disease;magnetic resonance imaging;deep learning;artificial intelligence;imaging diagnosis

WU Qian   GUO Hui*  

The Fourth Clinical Medical College, Xinjiang Medical University, Urumqi830099, China

Corresponding author: GUO H, E-mail: guohui9804@126.com

Conflicts of interest   None.

Received  2024-07-21
Accepted  2024-11-10
DOI: 10.12015/issn.1674-8034.2024.11.030
Cite this article as: WU Q, GUO H. Application of deep learning-based magnetic resonance imaging in the diagnosis and treatment of coronary artery disease[J]. Chin J Magn Reson Imaging, 2024, 15(11): 190-197. DOI:10.12015/issn.1674-8034.2024.11.030.

[1]
National Cardiovascular Center, China Cardiovascular Health and Disease Report compilation Group. Report on cardiovascular health and diseases in China 2023: an updated summary[J]. Chin Circ J, 2024, 39(7): 625-660. DOI: 10.3969/j.issn.1000-3614.2024.07.001.
[2]
SHIFERAW K B, WALI P, WALTEMATH D, et al. Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling[J/OL]. Front Cardiovasc Med, 2023, 10: 1308668 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38235288/. DOI: 10.3389/fcvm.2023.1308668.
[3]
JAFARI M, SHOEIBI A, KHODATARS M, et al. Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: a review[J/OL]. Comput Biol Med, 2023, 160: 106998 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/37182422/. DOI: 10.1016/j.compbiomed.2023.106998.
[4]
ALIZADEHSANI R, ABDAR M, ROSHANZAMIR M, et al. Machine learning-based coronary artery disease diagnosis: a comprehensive review[J/OL]. Comput Biol Med, 2019, 111: 103346 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/31288140/. DOI: 10.1016/j.compbiomed.2019.103346.
[5]
MCDONAGH T A, METRA M, ADAMO M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC[J]. Eur J Heart Fail, 2022, 24(1): 4-131. DOI: 10.1002/ejhf.2333.
[6]
WEBERLING L D, LOSSNITZER D, FREY N, et al. Coronary computed tomography vs. cardiac magnetic resonance imaging in the evaluation of coronary artery disease[J/OL]. Diagnostics, 2022, 13(1): 125 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/36611417/. DOI: 10.3390/diagnostics13010125.
[7]
SILVA C, LOPES P, GONÇALVES M, et al. Predictive value of a positive stress single-photon emission computed tomography or stress cardiac magnetic resonance for ruling in obstructive coronary artery disease in a real-world setting[J]. Portuguese J Cardiol Off J Portuguese Soc Cardiol, 2023, 42(9): 787-793. DOI: 10.1016/j.repc.2023.01.026.
[8]
ARAI A E, SCHULZ-MENGER J, SHAH D J, et al. Stress perfusion cardiac magnetic resonance vs SPECT imaging for detection of coronary artery disease[J]. J Am Coll Cardiol, 2023, 82(19): 1828-1838. DOI: 10.1016/j.jacc.2023.08.046.
[9]
SAKUMA H, ISHIDA M. Advances in myocardial perfusion MR imaging: physiological implications, the importance of quantitative analysis, and impact on patient care in coronary artery disease[J]. Magn Reson Med Sci, 2022, 21(1): 195-211. DOI: 10.2463/mrms.rev.2021-0033.
[10]
XIA H, LI Y T, SUN J. Evaluation of the diagnostic and prognostic value of left ventricular myocardial strain assessed by cardiovascular magnetic resonance feature tracking in coronary heart disease[J]. J Electrocardiol Circ, 2024, 43(4): 332-337. DOI: 10.12124/j.issn.2095-3933.2024.7.2022-5361.
[11]
TIAN D, SUN Y, GUO J J, et al. 3.0 T unenhanced Dixon water-fat separation whole-heart coronary magnetic resonance angiography: compressed-sensing sensitivity encoding imaging versus conventional 2D sensitivity encoding imaging[J]. Int J Cardiovasc Imaging, 2023, 39(9): 1775-1784. DOI: 10.1007/s10554-023-02878-y.
[12]
DIRKSEN M S, LAMB H J, DOORNBOS J, et al. Coronary magnetic resonance angiography: technical developments and clinical applications[J]. J Cardiovasc Magn Reson, 2003, 5(2): 365-386. DOI: 10.1081/jcmr-120019419.
[13]
ZHOU R X, HUANG W, YANG Y, et al. Simple motion correction strategy reduces respiratory-induced motion artifacts for k-t accelerated and compressed-sensing cardiovascular magnetic resonance perfusion imaging[J/OL]. J Cardiovasc Magn Reson, 2018, 20(1): 6 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/29386056/. DOI: 10.1186/s12968-018-0427-1.
[14]
MAKIMOTO H, KOHRO T. Adopting artificial intelligence in cardiovascular medicine: a scoping review[J]. Hypertens Res, 2024, 47(3): 685-699. DOI: 10.1038/s41440-023-01469-7.
[15]
CHU M, WU P, LI G Y, et al. Advances in diagnosis, therapy, and prognosis of coronary artery disease powered by deep learning algorithms[J]. JACC Asia, 2023, 3(1): 1-14. DOI: 10.1016/j.jacasi.2022.12.005.
[16]
YANG Y M, XIA X, LO D, et al. A survey on deep learning for software engineering[EB/OL]. 2020: arXiv: 2011.14597. http://arxiv.org/abs/2011.14597.
[17]
LECUN Y, BENGIO Y, HINTON G. Deep learning[J/OL]. Nature, 2015, 521: 436-444 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/26017442/. DOI: 10.1038/nature14539.
[18]
DÍAZ O, RODRÍGUEZ-RUÍZ A, SECHOPOULOS I. Artificial Intelligence for breast cancer detection: technology, challenges, and prospects[J/OL]. Eur J Radiol, 2024, 175: 111457 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38640824/. DOI: 10.1016/j.ejrad.2024.111457.
[19]
LU H X, YAO Y D, WANG L, et al. Research progress of machine learning and deep learning in intelligent diagnosis of the coronary atherosclerotic heart disease[J/OL]. Comput Math Methods Med, 2022, 2022: 3016532 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/35516452/. DOI: 10.1155/2022/3016532.
[20]
ZHANG Y, MAO Y J, LU X Y, et al. From single to universal: tiny lesion detection in medical imaging[J/OL]. Artif Intell Rev, 2024, 57(8): 192 [2024-07-20]. https://link.springer.com/article/10.1007/s10462-024-10762-x. DOI: 10.1007/s10462-024-10762-x.
[21]
LIN M W, CHEN L W, YANG S M, et al. CT-based deep-learning model for spread-through-air-spaces prediction in ground glass-predominant lung adenocarcinoma[J]. Ann Surg Oncol, 2024, 31(3): 1536-1545. DOI: 10.1245/s10434-023-14565-2.
[22]
HASENSTAB K A, YUAN N, RETSON T, et al. Erratum: automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network[J/OL]. Radiol Cardiothorac Imaging, 2022, 4(1): e219002 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/35782763/. DOI: 10.1148/ryct.219002.
[23]
PARK H, YUN J, LEE S M, et al. Deep learning-based approach to predict pulmonary function at chest CT[J/OL]. Radiology, 2023, 307(2): e221488 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/36786699/. DOI: 10.1148/radiol.221488.
[24]
AL-SELWI S M, HASSAN M F, ABDULKADIR S J, et al. RNN-LSTM: from applications to modeling techniques and beyond - Systematic review[J/OL]. J King Saud Univ Comput Inf Sci, 2024, 36: 102068 [2024-07-20]. https://www.sciencedirect.com/science/article/pii/S1319157824001575. DOI: 10.1016/j.jksuci.2024.102068.
[25]
REN D, LI F L, SUN H, et al. Local-enhanced multi-scale aggregation swin transformer for semantic segmentation of high-resolution remote sensing images[J]. Int J Remote Sens, 2024, 45(1): 101-120. DOI: 10.1080/01431161.2023.2292550.
[26]
ALOHALI M A, EL-RASHIDY N, ALAKLABI S, et al. Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification[J/OL]. Front Oncol, 2024, 14: 1392301 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/39099689/. DOI: 10.3389/fonc.2024.1392301.
[27]
ASIRI A A, SHAF A, ALI T, et al. Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis[J/OL]. PeerJ Comput Sci, 2024, 10: e1867 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38435590/. DOI: 10.7717/peerj-cs.1867.
[28]
AKÇAKAYA M, BASHA T A, CHAN R H, et al. Accelerated contrast-enhanced whole-heart coronary MRI using low-dimensional-structure self-learning and thresholding[J]. Magn Reson Med, 2012, 67(5): 1434-1443. DOI: 10.1002/mrm.24242.
[29]
DAWOOD T, CHEN C, SIDHU B S, et al. Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images[J/OL]. Med Image Anal, 2023, 88: 102861 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/37327613/. DOI: 10.1016/j.media.2023.102861.
[30]
JALILI M H, YU T, HASSANI C, et al. Contrast-enhanced MR angiography without gadolinium-based contrast material: clinical applications using ferumoxytol[J/OL]. Radiol Cardiothorac Imaging, 2022, 4(4): e210323 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/36059381/. DOI: 10.1148/ryct.210323.
[31]
DONG Z, SI G X, ZHU X M, et al. Diagnostic performance and safety of a novel ferumoxytol-enhanced coronary magnetic resonance angiography[J]. Circ Cardiovasc Imaging, 2023, 16(7): 580-590. DOI: 10.1161/CIRCIMAGING.123.015404.
[32]
YANG H J, DEY D, SYKES J, et al. Heart rate-independent 3D myocardial blood oxygen level-dependent MRI at 3.0 T with simultaneous 13N-ammonia PET validation[J]. Radiology, 2020, 295(1): 82-93. DOI: 10.1148/radiol.2020191456.
[33]
HAJHOSSEINY R, MUNOZ C, CRUZ G, et al. Coronary magnetic resonance angiography in chronic coronary syndromes[J/OL]. Front Cardiovasc Med, 2021, 8: 682924 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/34485397/. DOI: 10.3389/fcvm.2021.682924.
[34]
WOOD G, PEDERSEN A U, KUNZE K P, et al. Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography[J/OL]. J Cardiovasc Magn Reson, 2023, 25(1): 52 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/37779192/. DOI: 10.1186/s12968-023-00962-9.
[35]
WOOD G, HAJHOSSEINY R, PEDERSEN A U, et al. iNav-based, automated coronary magnetic resonance angiography for the detection of coronary artery stenosis (iNav-AUTO CMRA)[J/OL]. J Cardiovasc Magn Reson, 2024: 101097 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/39293786/. DOI: 10.1016/j.jocmr.2024.101097.
[36]
AKÇAKAYA M, BASHA T A, CHAN R H, et al. Accelerated isotropic sub-millimeter whole-heart coronary MRI: compressed sensing versus parallel imaging[J]. Magn Reson Med, 2014, 71(2): 815-822. DOI: 10.1002/mrm.24683.
[37]
LU H F, GUO J J, ZHAO S H, et al. Assessment of non-contrast-enhanced Dixon water-fat separation compressed sensing whole-heart coronary MR angiography at 3.0 T: a single-center experience[J/OL]. Acad Radiol, 2022, 29(Suppl 4): S82-S90 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/34127363/. DOI: 10.1016/j.acra.2021.05.009.
[38]
WU X, DENG L P, LI W J, et al. Deep learning-based acceleration of compressed sensing for noncontrast-enhanced coronary magnetic resonance angiography in patients with suspected coronary artery disease[J]. J Magn Reson Imaging, 2023, 58(5): 1521-1530. DOI: 10.1002/jmri.28653.
[39]
WU X, TANG L, LI W J, et al. Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MR angiography by deep learning-constrained compressed sensing[J]. Eur Radiol, 2023, 33(11): 8180-8190. DOI: 10.1007/s00330-023-09740-8.
[40]
YOON S, NAKAMORI S, AMYAR A, et al. Accelerated cardiac MRI cine with use of resolution enhancement generative adversarial inline neural network[J/OL]. Radiology, 2023, 307(5): e222878 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/37249435/. DOI: 10.1148/radiol.222878.
[41]
PEZZOTTI N, YOUSEFI S, ELMAHDY M S, et al. An adaptive intelligence algorithm for undersampled knee MRI reconstruction[J/OL]. IEEE Access, 2020, 8: 204825-204838 [2024-07-20]. https://ieeexplore.ieee.org/document/9241039. DOI: 10.1109/ACCESS.2020.3034287.
[42]
MASUTANI E M, BAHRAMI N, HSIAO A. Deep learning single-frame and multiframe super-resolution for cardiac MRI[J]. Radiology, 2020, 295(3): 552-561. DOI: 10.1148/radiol.2020192173.
[43]
ZUCKER E J, SANDINO C M, KINO A, et al. Free-breathing accelerated cardiac MRI using deep learning: validation in children and young adults[J]. Radiology, 2021, 300(3): 539-548. DOI: 10.1148/radiol.2021202624.
[44]
DEMIREL O B, YAMAN B, SHENOY C, et al. Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR[J]. Magn Reson Med, 2023, 89(1): 308-321. DOI: 10.1002/mrm.29453.
[45]
WANG J Y, SALERNO M. Deep learning-based rapid image reconstruction and motion correction for high-resolution Cartesian first-pass myocardial perfusion imaging at 3T[J]. Magn Reson Med, 2024, 92(3): 1104-1114. DOI: 10.1002/mrm.30106.
[46]
MORALES M A, ASSANA S, CAI X Y, et al. An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance[J/OL]. J Cardiovasc Magn Reson, 2022, 24(1): 47 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/35948936/. DOI: 10.1186/s12968-022-00879-9.
[47]
MORALES M A, GHANBARI F, NAKAMORI S, et al. Deformation-encoding deep learning transformer for high-frame-rate cardiac cine MRI[J/OL]. Radiol Cardiothorac Imaging, 2024, 6(3): e230177 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38722232/. DOI: 10.1148/ryct.230177.
[48]
SUMITHRA M G, VENKATESAN C. SwinDFU-Net: deep learning transformer network for infection identification in diabetic foot ulcer[J/OL]. Technol Health Care, 2024 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/39269872/. DOI: 10.3233/THC-241444.
[49]
CHEN Y S, WANG L Z, DING B J, et al. Radiologically based automated segmentation of cardiac MRI using an improved U-Net neural algorithm[J/OL]. J Radiat Res Appl Sci, 2023, 16(4): 100704 [2024-07-20]. https://www.sciencedirect.com/science/article/pii/S1687850723001826. DOI: 10.1016/j.jrras.2023.100704.
[50]
YAN Z S, SU Y J, SUN H X, et al. SegNet-based left ventricular MRI segmentation for the diagnosis of cardiac hypertrophy and myocardial infarction[J/OL]. Comput Methods Programs Biomed, 2022, 227: 107197 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/36351349/. DOI: 10.1016/j.cmpb.2022.107197.
[51]
SANTOS DA SILVA I F, SILVA A C, DE PAIVA A C, et al. A cascade approach for automatic segmentation of cardiac structures in short-axis cine-MR images using deep neural networks[J/OL]. Expert Syst Appl, 2022, 197: 116704 [2024-07-20]. https://www.sciencedirect.com/science/article/abs/pii/S0957417422001828. DOI: 10.1016/j.eswa.2022.116704.
[52]
BAI W J, SINCLAIR M, TARRONI G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks[J/OL]. J Cardiovasc Magn Reson, 2018, 20(1): 65 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/30217194/. DOI: 10.1186/s12968-018-0471-x.
[53]
GUO R, WEINGÄRTNER S, ŠIURYTĖ P, et al. Emerging techniques in cardiac magnetic resonance imaging[J]. J Magn Reson Imaging, 2022, 55(4): 1043-1059. DOI: 10.1002/jmri.27848.
[54]
XUE H, DAVIES R H, BROWN L A E, et al. Automated inline analysis of myocardial perfusion MRI with deep learning[J/OL]. Radiol Artif Intell, 2020, 2(6): e200009 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/33330849/. DOI: 10.1148/ryai.2020200009.
[55]
SADHANANDAN C K, THARCIS MARIAPUSHPAM I, SURESH S. An efficient deep learning algorithm for the segmentation of cardiac ventricles[J]. Int J Imaging Syst Tech, 2023, 33(6): 2044-2060. DOI: 10.1002/ima.22929.
[56]
ZHAO S H, GUO W F, YAO Z F, et al. Fully automated pixel-wise quantitative CMR-myocardial perfusion with CMR-coronary angiography to detect hemodynamically significant coronary artery disease[J]. Eur Radiol, 2023, 33(10): 7238-7249. DOI: 10.1007/s00330-023-09689-8.
[57]
SCANNELL C M, VETA M, VILLA A D M, et al. Deep-learning-based preprocessing for quantitative myocardial perfusion MRI[J]. J Magn Reson Imaging, 2020, 51(6): 1689-1696. DOI: 10.1002/jmri.26983.
[58]
JACOBS M, BENOVOY M, CHANG L C, et al. Automated segmental analysis of fully quantitative myocardial blood flow maps by first-pass perfusion cardiovascular magnetic resonance[J/OL]. IEEE Access, 2021, 9: 52796-52811 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/33996344/. DOI: 10.1109/access.2021.3070320.
[59]
FADIL H, TOTMAN J J, HAUSENLOY D J, et al. A deep learning pipeline for automatic analysis of multi-scan cardiovascular magnetic resonance[J/OL]. J Cardiovasc Magn Reson, 2021, 23(1): 47 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/33896419/. DOI: 10.1186/s12968-020-00695-z.
[60]
KIM Y C, CHUNG Y, CHOE Y H. Deep learning for classification of late gadolinium enhancement lesions based on the 16-segment left ventricular model[J]. Phys Med, 2024, 117: 103193. DOI: 10.1016/j.ejmp.2023.103193.
[61]
ALAM S, PEPINE C J. Physiology and functional significance of the coronary microcirculation: an overview of its implications in health and disease[J/OL]. Am Heart J Plus, 2024, 40: 100381 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38586427/. DOI: 10.1016/j.ahjo.2024.100381.
[62]
YANG Z H, LIU Y X, LI Z Z, et al. Coronary microvascular dysfunction and cardiovascular disease: Pathogenesis, associations and treatment strategies[J/OL]. Biomedecine Pharmacother, 2023, 164: 115011 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/37321056/. DOI: 10.1016/j.biopha.2023.115011.
[63]
KRAMER C M, BARKHAUSEN J, BUCCIARELLI-DUCCI C, et al. Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update[J/OL]. J Cardiovasc Magn Reson, 2020, 22(1): 17 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/32089132/. DOI: 10.1186/s12968-020-00607-1.
[64]
ZHANG C, LI X, MOU A N, et al. Assessment of late gadolinium enhancement-negative chronic total occlusion by longitudinal strain analysis using cardiac magnetic resonance imaging[J]. Acta Radiol, 2022, 63(12): 1634-1642. DOI: 10.1177/02841851211055395.
[65]
FERDIAN E, SUINESIAPUTRA A, FUNG K, et al. Fully automated myocardial strain estimation from cardiovascular MRI-tagged images using a deep learning framework in the UK biobank[J/OL]. Radiol Cardiothorac Imaging, 2020, 2(1): e190032 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/32715298/. DOI: 10.1148/ryct.2020190032.
[66]
MASUTANI E M, CHANDRUPATLA R S, WANG S, et al. Deep learning synthetic strain: quantitative assessment of regional myocardial wall motion at MRI[J/OL]. Radiol Cardiothorac Imaging, 2023, 5(3): e220202 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/37404797/. DOI: 10.1148/ryct.220202.
[67]
WANG Y, SUN C Y, GHADIMI S, et al. StrainNet: improved myocardial strain analysis of cine MRI by deep learning from DENSE[J/OL]. Radiol Cardiothorac Imaging, 2023, 5(3): e220196 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/37404792/. DOI: 10.1148/ryct.220196.
[68]
XU C C, XU L, GAO Z F, et al. Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2017: 240-249. DOI: 10.1007/978-3-319-66179-7_28.
[69]
ZHANG N, YANG G, GAO Z F, et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI[J]. Radiology, 2019, 291(3): 606-617. DOI: 10.1148/radiol.2019182304.
[70]
KNOTT K D, SERAPHIM A, AUGUSTO J B, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping[J]. Circulation, 2020, 141(16): 1282-1291. DOI: 10.1161/CIRCULATIONAHA.119.044666.
[71]
NAPOLI G, PERGOLA V, BASILE P, et al. Epicardial and pericoronary adipose tissue, coronary inflammation, and acute coronary syndromes[J/OL]. J Clin Med, 2023, 12(23): 7212 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38068263/. DOI: 10.3390/jcm12237212.
[72]
GUGLIELMO M, PENSO M, CARERJ M L, et al. DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR[J/OL]. Atherosclerosis, 2024, 397: 117549 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38679562/. DOI: 10.1016/j.atherosclerosis.2024.117549.
[73]
SMILOWITZ N R, SCHLAMP F, HAUSVATER A, et al. Coronary microvascular dysfunction is associated with a proinflammatory circulating transcriptome in patients with nonobstructive coronary arteries[J]. Arterioscler Thromb Vasc Biol, 2024, 44(4): 997-999. DOI: 10.1161/ATVBAHA.123.320471.
[74]
GAUTAM N, SALUJA P, MALKAWI A, et al. Current and future applications of artificial intelligence in coronary artery disease[J]. Healthcare, 2022, 10(2): 232. DOI: 10.3390/healthcare10020232.
[75]
ZHANG Q, FOTAKI A, GHADIMI S, et al. Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation[J/OL]. J Cardiovasc Magn Reson, 2024, 26(2): 101025 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/38909656/. DOI: 10.1016/j.jocmr.2024.101051.
[76]
KHOZEIMEH F, SHARIFRAZI D, IZADI N H, et al. RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance[J/OL]. Sci Rep, 2022, 12(1): 11178 [2024-07-20]. https://pubmed.ncbi.nlm.nih.gov/35778476/. DOI: 10.1038/s41598-022-15374-5.

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