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中华医学超声杂志(电子版) ›› 2023, Vol. 20 ›› Issue (08) : 875 -878. doi: 10.3877/cma.j.issn.1672-6448.2023.08.016

综述

左心室舒张功能评估现状及人工智能应用的研究进展
陈煦, 杨菲菲, 林锡祥   
  1. 100850 北京,解放军总医院第二医学中心心内科;100048 北京,解放军总医院第四医学中心心内科;100853 北京,解放军总医院创新医学部大数据中心
  • 收稿日期:2022-06-17 出版日期:2023-08-01
  • 基金资助:
    北京市自然科学基金(7202198); 中华人民共和国科学技术部(2021ZD014040400)
  • Received:2022-06-17 Published:2023-08-01
引用本文:

陈煦, 杨菲菲, 林锡祥. 左心室舒张功能评估现状及人工智能应用的研究进展[J]. 中华医学超声杂志(电子版), 2023, 20(08): 875-878.

左心室舒张功能减退是心脏功能障碍的早期表现之一,诸多心脏疾病,包括高血压性心脏病、冠心病和肥厚型心肌病等,均会表现出心肌舒张功能减弱。左心室舒张功能评估在多种心血管疾病预后评估、药物疗效评价、未来可能发生的心血管事件及死亡率预测等方面应用广泛,尤其在出现呼吸困难症状或诊断为心力衰竭的患者中,超声心动图评估左心室舒张功能至关重要。近来研究表明,在射血分数下降型心力衰竭(heart failure with reduced ejection fraction,HFrEF)、射血分数保留型心力衰竭(heart failure with preserved ejection fraction,HFpEF)和急性心肌梗死等情况下,左心室舒张功能障碍分级可提供独立的和渐进的预后信息。然而,舒张功能评估流程复杂,测量耗时,且依赖有经验的医师解读,在临床中往往未得到重视和正确的应用。近年来,人工智能(artificial intelligence,AI)技术在医学影像自动分析领域得到了快速发展,其在图像信息的提取和结构化数据处理方面具有独特优势,尤其在超声心动图方面具有良好的应用价值。应用AI可以减少观察者间及观察者内测量指标的差异,提高诊断的准确性和一致性;此外可以大幅缩短诊断时间,提高诊断效率,为临床医师减负。本文重点对目前左心室舒张功能评价的现状、难点以及AI在此领域的应用做一综述。

1
Sengupta PP, Marwick TH. The many dimensions of diastolic function: a curse or a blessing [J]. JACC Cardiovasc Imaging, 2018, 11(3): 409-410.
2
Playford D, Strange G, Celermajer DS, et al. Diastolic dysfunction and mortality in 436 360 men and women: the National Echo Database Australia (NEDA) [J]. Eur Heart J Cardiovasc Imaging, 2021, 22(5): 505-515.
3
Kane GC, Karon BL, Mahoney DW, et al. Progression of left ventricular diastolic dysfunction and risk of heart failure [J]. JAMA, 2011, 306(8): 856-863.
4
Kusunose K, Haga A, Abe T, et al. Utilization of artificial intelligence in echocardiography [J]. Circ, 2019, 83(8): 1623-1629.
5
Nauta JF, Hummel YM, Van Der Meer P, et al. Correlation with invasive left ventricular filling pressures and prognostic relevance of the echocardioimagedata diastolic parameters used in the 2016 ESC heart failure guidelines and in the 2016 ASE/EACVI recommendations: a systematic review in patients with heart failure with preserved ejection fraction [J]. Eur J Heart Fai, 2018, 20(9): 1303-1311.
6
Jones R, Varian F, Alabed S, et al. Meta-analysis of echocardioimagedata quantification of left ventricular filling pressure [J]. ESC Heart Fail, 2021, 8(1): 566-576.
7
Nagueh SF, Smiseth OA, Appleton CP, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging [J]. J Am Soc Echocardiogr, 2016, 29(4): 277-314.
8
Almeida JG, Fontes-Carvalho R, Sampaio F, et al. Impact of the 2016 ASE/EACVI recommendations on the prevalence of diastolic dysfunction in general population [J]. Eur Heart J Cardiovasc Imaging, 2018, 19(4): 380-386.
9
Sanchis L, Andrea R, Falces C, et al. Differential clinical implications of current recommendations for the evaluation of left ventricular diastolic function by echocardiography [J]. J Am Soc Echocardiogr, 2018, 31(11): 1203-1208.
10
Othman F, Abushahba G, Salustri A. Adherence to the American Society of Echocardiography and European Association of Cardiovascular Imaging Recommendations for the evaluation of left ventricular diastolic function by echocardiography: a quality improvement project [J]. J Am Soc Echocardiogr, 2019, 32(12): 1619-1621.
11
Playford D, Strange G, Celermajer DS, et al. Diastolic dysfunction and mortality in 436 360 men and women: the National Echo Database Australia (NEDA) [J]. Eur Heart J Cardiovasc Imaging, 2020, 22(5): 505-515.
12
Gottbrecht M, Salerno M, Aurigemma G. Evolution of diastolic function algorithms: implications for clinical practice [J]. Echocardiography, 2018, 35(1): 39-46.
13
Zakeri R, Chamberlain AM, Roger VL, et al. Temporal relationship and prognostic significance of atrial fibrillation in heart failure patients with preserved ejection fraction [J]. Circ, 2013, 128(10): 1085-1093.
14
Ma G, Fang L, Gao P, et al. Association between the ratio of early diastolic transmitral velocity to early diastolic mitral annular velocity and invasive measured left atrial pressure in patients with atrial fibrillation and preserved left ventricular ejection fraction [J]. Chinese Journal of Cardiology, 2018, 46(4): 292-297.
15
Alsharqi M, Woodward WJ, Mumith JA, et al. Artificial intelligence and echocardiography [J]. Echo Res Pract, 2018, 5(4): R115-R125.
16
Madani A, Arnaout R, Mofrad M, et al. Fast and accurate view classification of echocardiograms using deep learning [J]. NPJ Digital Med, 2018, 1: 6.
17
Kusunose K, Abe T, Haga A, et al. A deep learning approach for assessment of regional wall motion abnormality from echocardioimagedata images [J]. JACC Cardiovasc Imaging, 2020, 13(2 Pt 1): 374-381.
18
Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms [J]. NPJ Digit Med, 2020, 3: 10.
19
Choi DJ, Park JJ, Ali T, et al. Artificial intelligence for the diagnosis of heart failure [J]. NPJ Digit Med, 2020, 3: 54.
20
Sengupta PP, Huang Y-M, Bansal M, et al. Cognitive machine-learning algorithm for cardiac imaging [J]. Circ Cardiovasc Imaging, 2016, 9(6): e004330.
21
Hubert A, Le Rolle V, Galli E, et al. New expectations for diastolic function assessment in transthoracic echocardiography based on a semi-automated computing of strain–volume loops [J]. Eur Heart J Cardiovasc Imaging, 2020, 21(12): 1366-1371.
22
Nouraei H, Rabkin SW. A new approach to the clinical subclassification of heart failure with preserved ejection fraction [J]. Int J Cardiol, 2021, 331: 138-143.
23
Segar MW, Patel KV, Ayers C, et al. Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis [J]. Eur J Heart Fail, 2020, 22(1): 148-158.
24
Horiuchi Y, Tanimoto S, Latif A, et al. Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables [J]. Int J Cardiol, 2018, 262: 57-63.
25
Mishra RK, Tison GH, Fang Q, et al. Association of machine learning-derived phenogroupings of echocardioimagedata variables with heart failure in stable coronary artery disease: the heart and soul study [J]. J Am Soc Echocardiogr, 2020, 33(3): 322-331.
26
Pecková M, Charvat J, Schuck O, et al. The association between left ventricular diastolic function and a mild-to-moderate decrease in glomerular filtration rate in patients with type 2 diabetes mellitus [J]. J Int Med Res, 2011, 39(6): 2178-2186.
27
Omar AMS, Narula S, Abdel Rahman MA, et al. Precision phenotyping in heart failure and pattern clustering of ultrasound data for the assessment of diastolic dysfunction [J]. JACC Cardiovasc Imaging, 2017, 10(11): 1291-1303.
28
Pandey A, Kagiyama N, Yanamala N, et al. Deep-learning models for the echocardioimagedata assessment of diastolic dysfunction [J]. JACC Cardiovasc Imaging, 2021, 14(10): 1887-1900.
29
Lancaster MC, Salem Omar AM, Narula S, et al. Phenotypic clustering of left ventricular diastolic function parameters [J]. JACC Cardiovasc Imaging, 2019, 12(7 Pt 1): 1149-1161.
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