|
|
基于人工智能的医学影像学检查在动脉粥样硬化性狭窄定量评估的研究进展
|
Abstract:
动脉狭窄是动脉粥样硬化疾病的主要结局之一，其进展和稳定性对动脉粥样硬化疾病的预后和治疗产生重大影响。现有的影像学方法对于动脉粥样硬化性狭窄(Atherosclerotic stenosis, AS)的准确评估存在局限性，而人工智能(Artificial intelligence, AI)在医学影像分析中发挥重要作用，可以实现对病变严重程度和进展速度的定量评估及风险预测。目前，基于AI的医学影像学在动脉狭窄定量评估方面取得了显著进展，尤其是基于深度学习(Deep learning, DL)的算法在血管狭窄预测、斑块分类和识别中表现出良好的性能。本文对基于AI的医学影像学检查在AS定量评估中的研究进展进行综述，并对未来基于AI技术在动脉狭窄定量评估中可能存在的挑战和机遇进行展望。
Arterial stenosis is one of the main outcomes of atherosclerotic diseases, and its progression and stability have a significant impact on the prognosis and treatment of atherosclerotic diseases. Existing imaging methods have limitations for accurate assessment of atherosclerotic stenosis (AS), and artificial intelligence (AI) plays an important role in medical image analysis. The quantitative evaluation and risk prediction of the severity and progression of the disease can be realized. At present, AI-based medical imaging has made remarkable progress in the quantitative assessment of arterial stenosis, especially the algorithm based on deep learning (DL) has shown good performance in the prediction of arterial stenosis, plaque classification and recognition. This article reviews the research progress of AI-based medical imaging in quantitative assessment of AS, and looks forward to the possible challenges and opportunities of AI-based quantitative assessment of arterial stenosis in the future.
| [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 |
