1 |
Pazin-Filho A, Schmidt A, Almeida-Filho OC, et al. [Ultrasound myocardial tissue characterization][J]. Arqu Bras Cardiol, 2003, 81(3):319-325.
|
2 |
Skorton DJ, Collins SM. Quantitation in echocardiography[J].Cardiovasc Intervent Radiol, 1987, 10(6): 316-331.
|
3 |
Picano E, Paterni M. Ultrasound tissue characterization of vulnerable atherosclerotic plaque[J]. Int J Mol Sci, 2015, 16(5): 10121-10133.
|
4 |
Huang Q, Zeng Z. A review on real-time 3D ultrasound imaging technology[J]. Biomed Res Int, 2017, 2017: 6027029.
|
5 |
Valeri A, Nguyen TA. Research on texture images and radiomics in urology: a review of urological MR imaging applications[J]. Curr Opin Urol, 2023, 33(6): 428-436.
|
6 |
Martin-Isla C, Campello VM, Izquierdo C, et al. Deep learning segmentation of the right ventricle in cardiac MRI: The M&Ms challenge[J]. IEEE J Biomed Health Inform, 2023, 27(7): 3302-3313.
|
7 |
Mannil M, Von Spiczak J, Manka R, et al. Texture analysis and machine learning for detecting myocardial infarction in noncontrast low-dose computed tomography: unveiling the invisible[J]. Invest Radiol, 2018, 53(6): 338-343.
|
8 |
Manabe O, Ohira H, Hirata K, et al. Use of 18F-FDG PET/CT texture analysis to diagnose cardiac sarcoidosis[J]. Eur J Nucl Med Mol Imaging, 2019, 46(6): 1240-1247.
|
9 |
Mannil M, Eberhard M, Von Spiczak J, et al. Artificial intelligence and texture analysis in cardiac imaging[J]. Curr Cardiol Rep, 2020, 22(11): 131.
|
10 |
俞霏. 心肌纹理分析与深度学习对左心室肥厚疾病的智能超声诊断[D]. 上海:同济大学, 2022.
|
11 |
杨友常, 殷若涵, 汤晓强, 等. 冠状动脉CT血管造影结合机器学习算法预测心肌缺血的价值研究[J]. 临床放射学杂志, 2023, 42(2):258-262.
|
12 |
Li YL, Leu HB, Ting CH, et al. Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks[J]. Sci Rep, 2024, 14(1): 3802.
|
13 |
Shameer K, Johnson KW, Glicksberg BS, et al. Machine learning in cardiovascular medicine: are we there yet?[J]. Heart, 2018, 104(14):1156-1164.
|
14 |
Allegra A, Mirabile G, Tonacci A, et al. Machine learning approaches in diagnosis, prognosis and treatment selection of cardiac amyloidosis[J]. Int J Mol Sci, 2023, 24(6): 5680.
|
15 |
Zerouaoui H, Idri A. Reviewing machine learning and image processing based decision-making systems for breast cancer imaging[J]. J Med Syst,2021, 45(1): 8.
|
16 |
Li Z, Qin Y, Liu X, et al. Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning[J].Funct Integr Genomics, 2023, 23(2): 83.
|
17 |
Apte AP, Iyer A, Thor M, et al. Library of deep-learning image segmentation and outcomes model-implementations[J]. Phys Med, 2020,73: 190-196.
|
18 |
Xu J, Ma J, Gao X, et al. Adaptive progressive continual learning[J].IEEE Trans Pattern Anal Mach Intell, 2022, 44(10): 6715-6728.
|
19 |
Bauer R, Gharabaghi A. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation[J]. Front Neurosci, 2015, 9: 36.
|
20 |
Huang Q, Zhang F, Li X. Machine learning in ultrasound computeraided diagnostic systems: a survey[J]. Biomed Res Int, 2018, 2018:5137904.
|
21 |
陈凯玲, 王文平. 基于超声影像的人工智能在肝脏局灶性病变中的研究进展[J]. 中华超声影像学杂志, 2021, 30(9): 824-828.
|
22 |
候全飞, 朱业, 张紫桑, 等. 人工智能在心力衰竭超声影像学分析中的应用[J]. 中华超声影像学杂志, 2022, 31(9): 824-828.
|
23 |
Zamzmi G, Rajaraman S, Hsu LY, et al. Real-time echocardiography image analysis and quantification of cardiac indices[J]. Med Image Anal, 2022, 80: 102438.
|
24 |
Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies[J]. BMJ, 2020, 368: m689.
|
25 |
杨菲菲, 王秋霜, 何昆仑. 人工智能在超声心动图中的应用现状及展望[J/OL]. 中华医学超声杂志(电子版), 2022, 19(2): 186-189.
|
26 |
Kusunose K, Haga A, Inoue M, et al. Clinically feasible and accurate view classification of echocardiographic images using deep learning[J].Biomolecules, 2020, 10(5): 665.
|
27 |
Johri AM, Singh KV, Mantella LE, et al. Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization[J]. Comput Biol Med, 2022, 150: 106018.
|
28 |
Li Y, Li H, Wu F, et al. Semi-supervised learning improves the performance of cardiac event detection in echocardiography[J].Ultrasonics, 2023, 134: 107058.
|
29 |
Maruyama K, Imanaka-Yoshida K. The pathogenesis of cardiac fibrosis: a review of recent progress[J]. Int J Mol Sci, 2022, 23(5): 2617.
|
30 |
Hathaway QA, Yanamala N, Siva NK, et al. Ultrasonic texture features for assessing cardiac remodeling and dysfunction[J]. J Am Coll Cardiol, 2022, 80(23): 2187-2201.
|
31 |
Kagiyama N, Shrestha S, Cho JS, et al. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound[J]. EBioMedicine, 2020, 54: 102726.
|
32 |
Chaganti BT, Negishi K, Okajima K. Role of myocardial strain imaging in cancer therapy-related cardiac dysfunction[J]. Curr Cardiol Rep, 2022, 24(6): 739-748.
|
33 |
Potter E, Marwick TH. Assessment of left ventricular function by echocardiography: the case for routinely adding global longitudinal strain to ejection fraction[J]. JACC Cardiovasc Imaging, 2018, 11(2 Pt 1): 260-274.
|
34 |
Babaei H, Mendiola EA, Neelakantan S, et al. A machine learning model to estimate myocardial stiffness from EDPVR[J]. Sci Rep,2022, 12(1): 5433.
|
35 |
Zhang X, Liang T, Su C, et al. Deep learn-based computer-assisted transthoracic echocardiography: approach to the diagnosis of cardiac amyloidosis[J]. Int J Cardiovasc Imaging, 2023, 39(5): 955-965.
|
36 |
Deng W, Zhang J, Jia Z, et al. Myocardial involvement characteristics by cardiac MR imaging in neurological and non-neurological Wilson disease patients[J]. Insights Imaging, 2024, 15(1): 24.
|
37 |
Yu F, Huang H, Yu Q, et al. Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy[J]. Ann Transl Med, 2021, 9(2): 108.
|
38 |
Vidya KS, Ng EYK, Acharya UR, et al. Computer-aided diagnosis of myocardial infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study[J]. Computers Biol Med, 2015,62: 86-93.
|
39 |
Zhou W, Wang T, He Y, et al. Contrast U-Net driven by sufficient texture extraction for carotid plaque detection[J]. Math Biosci Eng,2023, 20(9): 15623-15640.
|