[1]
|
Pasterkamp, G., Den Ruijter, H.M. and Giannarelli, C. (2022) False Utopia of One Unifying Description of the Vulnerable Atherosclerotic Plaque: A Call for Recalibration That Appreciates the Diversity of Mechanisms Leading to Atherosclerotic Disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 42, e86-e95. https://doi.org/10.1161/ATVBAHA.121.316693
|
[2]
|
Roth, G.A., Mensah, G.A., Johnson, C.O., et al. (2020) Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019. Journal of the American College of Cardiology, 76, 2982-3021. https://doi.org/10.1016/j.jacc.2020.11.010
|
[3]
|
Libby, P. (2021) The Changing Landscape of Atherosclerosis. Nature, 592, 524-533. https://doi.org/10.1038/s41586-021-03392-8
|
[4]
|
Nardi, V., Benson, J., Bois, M., et al. (2022) Carotid Plaques from Symptomatic Patients with Mild Stenosis Is Associated with Intraplaque Hemorrhage. Hypertension, 79, 271-282. https://doi.org/10.1161/HYPERTENSIONAHA.121.18128
|
[5]
|
Fox, A. (1993) How to Measure Carotid Stenosis. Radiology, 186, 316-318. https://doi.org/10.1148/radiology.186.2.8421726
|
[6]
|
Merino, J.G. and Warach, S. (2010) Imaging of Acute Stroke. Nature Reviews Neurology, 6, 560-571. https://doi.org/10.1038/nrneurol.2010.129
|
[7]
|
Flachskampf, F.A., Benson, J., Bois, M., et al. (2011) Cardiac Imaging after Myocardial Infarction. European Heart Journal, 32, 272-283. https://doi.org/10.1093/eurheartj/ehq446
|
[8]
|
Su, M.Y. (2021) Editorial for “the Occurrence and Outcome of Mild Intracranial Atherosclerotic Stenosis: A Prospective High-Resolution MRI Study”. Journal of Magnetic Resonance Imaging, 54, 89-90. https://doi.org/10.1002/jmri.27571
|
[9]
|
Siepmann, T., Barlinn, K. Floegel, T., et al. (2021) CT Angiography Manual Multiplanar Vessel Diameter Measurement vs. Semiautomated Perpendicular Area Minimal Caliber Computation of Internal Carotid Artery Stenosis. Frontiers in Cardiovascular Medicine, 8, Article 740237. https://doi.org/10.3389/fcvm.2021.740237
|
[10]
|
Bae, Y., Kang, S.J., Kim, G., et al. (2019) Prediction of Coronary Thin-Cap Fibroatheroma by Intravascular Ultrasound-Based Machine Learning. Atherosclerosis, 288, 168-174. https://doi.org/10.1016/j.atherosclerosis.2019.04.228
|
[11]
|
Forssen, H., Patel, R., Fitzpatrick, N., et al. (2017) Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data. Studies in Health Technology and Informatics, 235, 111-115.
|
[12]
|
Yang, Y., Patel, R., Fitzpatrick, N., et al. (2023) Performance of Deep Learning-Based Autodetection of Arterial Stenosis on Head and Neck CT Angiography: An Independent External Validation Study. La Radiologia Medica, 128, 1103-1115. https://doi.org/10.1007/s11547-023-01683-w
|
[13]
|
Griffin, W.F., Patel, R., Fitzpatrick, N., et al. (2023) AI Evaluation of Stenosis on Coronary CTA, Comparison with Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy. JACC: Cardiovascular Imaging, 16, 193-205. https://doi.org/10.1016/j.jcmg.2021.10.020
|
[14]
|
Fu, F., Shan, Y., Yang, G., et al. (2023) Deep Learning for Head and Neck CT Angiography: Stenosis and Plaque Classification. Radiology, 307, e220996. https://doi.org/10.1148/radiol.220996
|
[15]
|
Wardlaw, J.M., Mair, G., Von Kummer, R., et al. (2022) Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke, 53, 2393-2403. https://doi.org/10.1161/STROKEAHA.121.036204
|
[16]
|
Wiklund, P., Medson, K. and Elf, J. (2023) Incidental Pulmonary Embolism in Patients with Cancer: Prevalence, Underdiagnosis and Evaluation of an AI Algorithm for Automatic Detection of Pulmonary Embolism. European Radiology, 33, 1185-1193. https://doi.org/10.1007/s00330-022-09071-0
|
[17]
|
Salerno, A., Strambo, D., Nannoni, S., et al. (2022) Patterns of Ischemic Posterior Circulation Strokes: A Clinical, Anatomical, and Radiological Review. International Journal of Stroke, 17, 714-722. https://doi.org/10.1177/17474930211046758
|
[18]
|
Langlotz, C.P. (2019) Will Artificial Intelligence Replace Radiologists? Radiology: Artificial Intelligence, 1, e190058. https://doi.org/10.1148/ryai.2019190058
|
[19]
|
Pesapane, F., Codari, M. and Sardanelli, F. (2018) Artificial Intelligence in Medical Imaging: Threat or Opportunity? Radiologists again at the Forefront of Innovation in Medicine. European Radiology Experimental, 2, Article No. 35. https://doi.org/10.1186/s41747-018-0061-6
|
[20]
|
Bizzo, B.C., Dasegowda, G. Bridge, C., et al. (2023) Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles from Experience. Journal of the American College of Radiology, 20, 352-360. https://doi.org/10.1016/j.jacr.2023.01.002
|
[21]
|
俞益洲, 石德君, 马杰超, 等. 人工智能在医学影像分析中的应用进展[J]. 中国医学影像技术, 2019, 35(12): 1808-1812.
|
[22]
|
Hosny, A., Parmar, C., Quackenbush, J., et al. (2018) Artificial Intelligence in Radiology. Nature Reviews. Cancer, 18, 500-510. https://doi.org/10.1038/s41568-018-0016-5
|
[23]
|
Kriegeskorte, N. and Golan, T. (2019) Neural Network Models and Deep Learning. Current Biology, 29, R231-R236. https://doi.org/10.1016/j.cub.2019.02.034
|
[24]
|
Sheahan, M., Ma, X., Paik, D., et al. (2018) Atherosclerotic Plaque Tissue: Noninvasive Quantitative Assessment of Characteristics with Software-Aided Measurements from Conventional CT Angiography. Radiology, 286, 622-631. https://doi.org/10.1148/radiol.2017170127
|
[25]
|
Sieren, M.M., Widmann, C., Weiss, N., et al. (2022) Automated Segmentation and Quantification of the Healthy and Diseased Aorta in CT Angiographies Using a Dedicated Deep Learning Approach. European Radiology, 32, 690-701. https://doi.org/10.1007/s00330-021-08130-2
|
[26]
|
Mu, D., Widmann, C., Weiss, N., et al. (2022) Calcium Scoring at Coronary CT Angiography Using Deep Learning. Radiology, 302, 309-316. https://doi.org/10.1148/radiol.2021211483
|
[27]
|
Luijten, S.P.R., Wolff, L., Duvekot, M.H.C., et al. (2022) Diagnostic Performance of an Algorithm for Automated Large Vessel Occlusion Detection on CT Angiography. Journal of NeuroInterventional Surgery, 14, 794-798. https://doi.org/10.1136/neurintsurg-2021-017842
|
[28]
|
Torres, C., Lum, C., Puac-Polanco, P., et al. (2021) Differentiating Carotid Free-Floating Thrombus from Atheromatous Plaque Using Intraluminal Filling Defect Length on CTA: A Validation Study. Neurology, 97, e785-e793. https://doi.org/10.1212/WNL.0000000000012368
|
[29]
|
Fu, F., Wei, J., Zhang, M., et al. (2020) Rapid Vessel Segmentation and Reconstruction of Head and Neck Angiograms Using 3D Convolutional Neural Network. Nature Communications, 11, Article No. 4829. https://doi.org/10.1038/s41467-020-18606-2
|
[30]
|
Borst, J., Marquering, H.A., Kappelhof, M., et al. (2015) Diagnostic Accuracy of 4 Commercially Available Semiautomatic Packages for Carotid Artery Stenosis Measurement on CTA. American Journal of Neuroradiology, 36, 1978-1987. https://doi.org/10.3174/ajnr.A4400
|
[31]
|
Budoff, M.J., Dowe, D., Jollis, J.G., et al. (2008) Diagnostic Performance of 64-Multidetector Row Coronary Computed Tomographic Angiography for Evaluation of Coronary Artery Stenosis in Individuals without Known Coronary Artery Disease: Results from the Prospective Multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) Trial. Journal of the American College of Cardiology, 52, 1724-1732. https://doi.org/10.1016/j.jacc.2008.07.031
|
[32]
|
Lin, A., Manral, N., McElhinney, P., et al. (2022) Deep Learning-Enabled Coronary CT Angiography for Plaque and Stenosis Quantification and Cardiac Risk Prediction: An International Multicentre Study. The Lancet Digital Health, 4, E256-E265. https://doi.org/10.1016/S2589-7500(22)00022-X
|
[33]
|
Liu, X., Mo, X., Zhang, H., et al. (2021) A 2-Year Investigation of the Impact of the Computed Tomography-Derived Fractional Flow Reserve Calculated Using a Deep Learning Algorithm on Routine Decision-Making for Coronary Artery Disease Management. European Radiology, 31, 7039-7046. https://doi.org/10.1007/s00330-021-07771-7
|
[34]
|
Schuessler, M., Saner, F., Al-Rashid, F., et al. (2022) Diagnostic Accuracy of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve (CT-FFR) in Patients before Liver Transplantation Using CT-FFR Machine Learning Algorithm. European Radiology, 32, 8761-8768. https://doi.org/10.1007/s00330-022-08921-1
|
[35]
|
Saba, L., Yuan, C., Hatsukami, T.S., et al. (2018) Carotid Artery Wall Imaging: Perspective and Guidelines from the ASNR Vessel Wall Imaging Study Group and Expert Consensus Recommendations of the American Society of Neuroradiology. American Journal of Neuroradiology, 39, E9-E31. https://doi.org/10.3174/ajnr.A5488
|
[36]
|
Van Der Kolk, A.G., Hendrikse, J., Brundel, M., et al. (2013) Multi-Sequence Whole-Brain Intracranial Vessel Wall Imaging at 7.0 Tesla. European Radiology, 23, 2996-3004. https://doi.org/10.1007/s00330-013-2905-z
|
[37]
|
Yang, H., Zhang, X., Qin, Q., et al. (2016) Improved Cerebrospinal Fluid Suppression for Intracranial Vessel Wall MRI. Journal of Magnetic Resonance Imaging, 44, 665-672. https://doi.org/10.1002/jmri.25211
|
[38]
|
Fan, Z., Zhang, Z., Chung, Y.C., et al. (2010) Carotid Arterial Wall MRI at 3T Using 3D Variable-Flip-Angle Turbo Spin-Echo (TSE) with Flow-Sensitive Dephasing (FSD). Journal of Magnetic Resonance Imaging, 31, 645-654. https://doi.org/10.1002/jmri.22058
|
[39]
|
Shi, F., Yang, Q., Guo, X., et al. (2019) Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 66, 2840-2847. https://doi.org/10.1109/TBME.2019.2896972
|
[40]
|
Niu, P.P., Yu, Y., Zhou, H.W., et al. (2016) Vessel Wall Differences Between Middle Cerebral Artery and Basilar Artery Plaques on Magnetic Resonance Imaging. Scientific Reports, 6, Article No. 38534. https://doi.org/10.1038/srep38534
|
[41]
|
Wan, L., Li, H., Zhang, L., et al. (2022) Automated Morphologic Analysis of Intracranial and Extracranial Arteries Using Convolutional Neural Networks. British Journal of Radiology, 95, Article ID: 20210031. https://doi.org/10.1259/bjr.20210031
|
[42]
|
Gao, S., Van’t Klooster, R., Kitslaar, P.H., et al. (2017) Learning-Based Automated Segmentation of the Carotid Artery Vessel Wall in Dual-Sequence MRI Using Subdivision Surface Fitting. Medical Physics, 44, 5244-5259. https://doi.org/10.1002/mp.12476
|
[43]
|
Wu, J., Xin, J., Yang, X., et al. (2023) Segmentation of Carotid Artery Vessel Wall and Diagnosis of Carotid Atherosclerosis on Black Blood Magnetic Resonance Imaging with Multi-Task Learning. Medical Physics, 51, 1775-1797. https://doi.org/10.1002/mp.16728
|
[44]
|
Azzopardi, C., Camilleri, K.P. and Hicks, Y.A. (2020) Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks. IEEE Journal of Biomedical and Health Informatics, 24, 1004-1015. https://doi.org/10.1109/JBHI.2020.2965088
|
[45]
|
Liu, J., Cui, X., Wang, D., et al. (2017) Relationship of Thyroid Function with Intracranial Arterial Stenosis and Carotid Atheromatous Plaques in Ischemic Stroke Patients with Euthyroidism. Oncotarget, 8, 46532-46539. https://doi.org/10.18632/oncotarget.14883
|
[46]
|
Boyd, C., Brown, G., Kleinig, T., et al. (2021) Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications. Diagnostics, 11, Article 551. https://doi.org/10.3390/diagnostics11030551
|
[47]
|
Hassan, M., Chaudhry, A., Khan, A., et al. (2014) Robust Information Gain Based Fuzzy C-Means Clustering and Classification of Carotid Artery Ultrasound Images. Computer Methods and Programs in Biomedicine, 113, 593-609. https://doi.org/10.1016/j.cmpb.2013.10.012
|
[48]
|
Huang, X., Zhang, Y., Meng, L., et al. (2018) Identification of Ultrasonic Echolucent Carotid Plaques Using Discrete Fréchet Distance between Bimodal Gamma Distributions. IEEE Transactions on Biomedical Engineering, 65, 949-955. https://doi.org/10.1109/TBME.2017.2676129
|
[49]
|
Roy-Cardinal, M.H., Destrempes, F., Soulez, G., et al. (2019) Assessment of Carotid Artery Plaque Components with Machine Learning Classification Using Homodyned-K Parametric Maps and Elastograms. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66, 493-504. https://doi.org/10.1109/TUFFC.2018.2851846
|
[50]
|
Golemati, S., Patelaki, E., Gastounioti, A., et al. (2020) Motion Synchronisation Patterns of the Carotid Atheromatous Plaque from B-Mode Ultrasound. Scientific Reports, 10, Article No. 11221. https://doi.org/10.1038/s41598-020-65340-2
|
[51]
|
廖熙妍, 邹佳妮, 黄文才. 冠状动脉CT血管成像联合人工智能在冠状动脉疾病诊疗中的应用进展[J]. 联勤军事医学, 2023, 37(10): 899-902.
|
[52]
|
Hilbert, A., Ramos, L.A., Van Os, H.J.A., et al. (2019) Data-Efficient Deep Learning of Radiological Image Data for Outcome Prediction after Endovascular Treatment of Patients with Acute Ischemic Stroke. Computers in Biology and Medicine, 115, Article ID: 103516. https://doi.org/10.1016/j.compbiomed.2019.103516
|
[53]
|
Nam, Y., Jang, J., Lee, H.Y., et al. (2020) Estimating Age-Related Changes in Vivo Cerebral Magnetic Resonance Angiography Using Convolutional Neural Network. Neurobiology of Aging, 87, 125-131. https://doi.org/10.1016/j.neurobiolaging.2019.12.008
|
[54]
|
Johnson, K.M., Johnson, H.E., Zhao, Y., et al. (2019) Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning. Radiology, 292, 354-362. https://doi.org/10.1148/radiol.2019182061
|
[55]
|
Sarkar, D. and Saha, S. (2019) Machine-Learning Techniques for the Prediction of Protein-Protein Interactions. Journal of Biosciences, 44, Article No. 104. https://doi.org/10.1007/s12038-019-9909-z
|