| 1 |
Reller MD, Strickland MJ, Riehle-Colarusso T, et al. Prevalence of congenital heart defects in metropolitan Atlanta, 1998-2005 [J]. J Pediatr, 2008, 153(6): 807-813.
|
| 2 |
Kanwal A, Sheikh AM, Saher T. Determining the factors causing delayed referral for fetal echocardiography at a tertiary care hospital [J]. J Saudi Heart Assoc, 2018, 30(3): 205-210.
|
| 3 |
Randall P, Brealey S, Hahn S, et al. Accuracy of fetal echocardiography in the routine detection of congenital heart disease among unselected and low risk populations:a systematic review [J]. BJOG. 2005, 112(1): 24-30.
|
| 4 |
Li Y, Hua Y, Fang J, et al. Performance of different scan protocols of fetal echocardiography in the diagnosis of fetal congenital heart disease: a systematic review and meta-analysis [J]. PLoS One, 2013, 8(6): e65484.
|
| 5 |
Zuhlke L, Lawrenson J, Comitis G, et al. Congenital heart disease in low- and lower-middle-income countries: current status and new opportunities [J]. Curr Cardiol Rep, 2019, 21(12): 163.
|
| 6 |
Quartermain MD, Pasquali SK, Hill KD, et al. Variation in prenatal diagnosis of congenital heart disease in infants [J]. Pediatrics, 2015, 136(2): e378-e385.
|
| 7 |
Bakker MK, Bergman JEH, Krikov S, et al. Prenatal diagnosis and prevalence of critical congenital heart defects: an international retrospective cohort study [J]. BMJ Open, 2019, 9(7): e028139.
|
| 8 |
Oster ME, Lee KA, Honein MA, et al. Temporal trends in survival among infants with critical congenital heart defects [J]. Pediatrics, 2013, 131(5): e1502-e1508.
|
| 9 |
Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, et al. Machine learning in fetal cardiology: what to expect [J]. Fetal Diagn Ther, 2020, 47(5): 363-372.
|
| 10 |
Davis A, Billick K, Horton K, et al. Artificial intelligence and echocardiography: a primer for cardiac sonographers [J]. J Am Soc Echocardiogr, 2020, 33(9): 1061-1066.
|
| 11 |
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.
|
| 12 |
于腾飞, 何文, 甘从贵, 等. 基于深度学习超声在乳腺肿块四分类中的应用价值 [J]. 中华超声影像学杂志, 2020, 29(4): 337-342.
|
| 13 |
朱业, 谢雨霁, 谢明星, 等. 人工智能在心血管超声的应用 [J]. 中华超声影像学杂志, 2021, 30(4): 356-361.
|
| 14 |
Veronese P, Bogana G, Cerutti A, et al. A prospective study of the use of fetal intelligent navigation echocardiography (FINE) to obtain standard fetal echocardiography views [J]. Fetal Diagn Ther, 2017, 41(2): 89-99.
|
| 15 |
Garcia M, Yeo L, Romero R, et al. Prospective evaluation of the fetal heart using fetal intelligent navigation echocardiography (FINE) [J]. Ultrasound Obstet Gynecol, 2016, 47(4): 450-459.
|
| 16 |
Ma M, Li Y, Chen R, et al. Diagnostic performance of fetal intelligent navigation echocardiography (FINE) in fetuses with double-outlet right ventricle (DORV) [J]. Int J Cardiovasc Imaging, 2020, 36(11): 2165-2172.
|
| 17 |
Huang C, Zhao BW, Chen R, et al. Is fetal intelligent navigation echocardiography helpful in screening for d-transposition of the great arteries? [J]. J Ultrasound Med, 2020, 39(4): 775-784.
|
| 18 |
汪贤臣, 赵博文, 李世岩, 等. 胎儿心脏超声智能导航技术在法洛四联症中的诊断效能[J]. 中国医学影像学杂志, 2021, 29(6): 603-613.
|
| 19 |
Yeo L, Luewan S, Romero R, et al. Fetal intelligent navigation echocardiography (FINE) detects 98% of congenital heart disease [J]. J Ultrasound Med, 2018, 37(11): 2577-2593.
|
| 20 |
Baumgartner CF, Kamnitsas K, Matthew J, et al. SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound [J]. IEEE Trans Med Imaging, 2017, 36(11): 2204-2215.
|
| 21 |
Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease [J]. Nat Med, 2021, 27(5): 882-891.
|
| 22 |
Abdi AH, Luong C, Tsang T, et al. Automatic quality assessment of echocardiograms using convolutional neural networks: Feasibility on the apical four chamber view [J]. IEEE Trans Med Imag, 2017, 36(6): 1221-1230.
|
| 23 |
Abdi AH, Luong C, Tsang T, et al. Quality assessment of echocardiographic cine using recurrent neural networks: feasibility on five standard view planes [C]//International Conference on Medical Image Computing & Computer-assisted Intervention, 2017: 302-310.
|
| 24 |
Dong J, Liu S, Liao Y, et al. A generic quality control framework for fetal ultrasound cardiac four-chamber planes [J]. IEEE J Biomed Health Inform, 2020, 24(4): 931-942.
|
| 25 |
Sulas E, Ortu E, Raffo L, et al. Automatic recognition of complete atrioventricular activity in fetal pulsed-wave Doppler signals [J]. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 917-920.
|
| 26 |
Yu L, Guo Y, Wang Y, et al. Determination of fetal left ventricular volume based on two-dimensional echocardiography [J]. J Healthc Eng, 2017, 2017: 4797315.
|
| 27 |
Bridge CP, Ioannou C, Noble JA, et al. Automated annotation and quantitative description of ultrasound videos of the fetal heart [J]. Med Image Anal, 2017, 36: 147-161.
|
| 28 |
Cai Q, Chen R, Li L, et al. The application of knowledge distillation toward fine-grained segmentation for three-vessel view of fetal heart ultrasound images [J]. Comput Intell Neurosci, 2022: 1765550.
|
| 29 |
Xu L, Liu M, Shen Z, et al. DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography [J]. Comput Med Imaging Graph, 2020, 80: 101690.
|
| 30 |
Komatsu M, Sakai A, Komatsu R, et al. Detection of cardiac structural abnormalities in fetal ultrasound videos using deep learning [J]. Appl Sci, 2021,11(1): 371.
|
| 31 |
周小雪, 张莹莹, 张烨, 等. 人工智能技术在胎儿超声心动图四腔心切面筛查中的应用[J]. 中华超声影像学杂志, 2020, 29(8): 668-672.
|
| 32 |
Gong Y, Zhang Y, Zhu H, et al. Fetal congenital heart disease echocardiogram screening based on DGACNN: adversarial one-class classification combined with video transfer learning [J]. IEEE Trans Med Imaging, 2020, 39(4): 1206-1222.
|
| 33 |
Nurmaini S, Rachmatullah MN, Sapitri AI, et al. Accurate detection of septal defects with fetal ultrasonography images using deep learning-based multiclass instance segmentation [J]. IEEE Access, 2020, 8: 196160-196174.
|
| 34 |
Anda U, Andreea-Sorina M, Laurentiu PC, et al. Learning deep architectures for the interpretation of first-trimester fetal echocardiography (LIFE)—A study protocol for developing an automated intelligent decision support system for early fetal fechocardiography [J]. BMC Pregnancy Childbirth, 2023, 23(1): 20.
|
| 35 |
Diller GP, Vahle J, Radke R, et al. Utility of deep learning networks for the generation of artificial cardiacmagnetic resonance images in congenital heart disease [J]. BMCMed Imaging, 2020, 20(1): 113.
|