留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

人工智能在超声医学领域中的应用

刘睿峰 夏宇 姜玉新

刘睿峰, 夏宇, 姜玉新. 人工智能在超声医学领域中的应用[J]. 协和医学杂志, 2018, 9(5): 453-457. doi: 10.3969/j.issn.1674-9081.2018.05.015
引用本文: 刘睿峰, 夏宇, 姜玉新. 人工智能在超声医学领域中的应用[J]. 协和医学杂志, 2018, 9(5): 453-457. doi: 10.3969/j.issn.1674-9081.2018.05.015
Rui-feng LIU, Yu XIA, Yu-xin JIANG. Application of Artificial Intelligence in Ultrasound Medicine[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(5): 453-457. doi: 10.3969/j.issn.1674-9081.2018.05.015
Citation: Rui-feng LIU, Yu XIA, Yu-xin JIANG. Application of Artificial Intelligence in Ultrasound Medicine[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(5): 453-457. doi: 10.3969/j.issn.1674-9081.2018.05.015

人工智能在超声医学领域中的应用

doi: 10.3969/j.issn.1674-9081.2018.05.015
详细信息
    通讯作者:

    夏宇   电话:010-69159311,E-mail:yuxiapumch@aliyun.com

  • 中图分类号: R445.1

Application of Artificial Intelligence in Ultrasound Medicine

More Information
  • 摘要: 人工智能(artificial intelligence,AI)近几年再度成为各领域关注的焦点,其中深度学习的提出带来了一系列革命性变化,而随着计算机视觉向深度学习过渡以及硬件和大数据的进步,AI在图像识别领域展现出更广阔的发展前景。深度学习模型使得相关图像算法甚至达到了比人眼更高的识别准确率, 这为医学影像的发展提供了巨大契机。超声医学作为影像领域的重要分支,利用AI相关算法进行声像图分析的研究不断涌现,不仅为临床科研提供了新思路,亦有助于提高超声诊断的准确性。
  • [1] Turing AM.Computing machinery and intelligence[J].Mind, 1950, 59:433-460. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=HighWire000002027644
    [2] Stuart JR, Peter N.Artificial intelligence: modern approach[M].Englewood:Prentice Hall, 1995.
    [3] Sekhar L, Wechsler L, Yonas H, et al. Value of transcranial doppler examination in the diagnosis of cerebral vasospasm after subarachnoid hemorrhage[J].Neurosurgery, 1988, 22:813-821. doi:  10.1227/00006123-198805000-00002
    [4] Baumgartner RW, Mattle HP, Schroth G. Assessment of ≥50% and < 50% intracranial stenoses by transcranial color-coded duplex sonography[J]. Stroke, 1999, 30: 87-92. doi:  10.1161/01.STR.30.1.87
    [5] Swiercz M, Swiat M, Pawlak M, et al.Narrowing of the middle cerebral artery: artificial intelligence methods and comparison of transcranial color coded duplex sonography with conventional TCD[J]. Ultrasound Med Biol, 2010, 36:17-28. doi:  10.1016/j.ultrasmedbio.2009.05.005
    [6] Zhang L, Li QY, Duan YY, et al. Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography[J]. BMC Med Inform Decis Mak, 2012, 12: 55. doi:  10.1186/1472-6947-12-55
    [7] Zhu LC, Ye YL, Luo WH, et al. A model to discriminate malignant from benign thyroid nodules using artificial neural network[J]. PLoS One, 2013: e82211. doi:  10.1371/journal.pone.0082211
    [8] Acharya UR, Swapna G, Sree SV, et al. A review on ultrasound-based thyroid cancer tissue characterization and automated classification[J].Technol Cancer Res Treat, 2014:289-301. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d6b10d2684d42c6029883f7d75f65280
    [9] Chi J, Walia E, Babyn P, et al. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network[J]. J Digit Imaging, 2017, 30: 477-486. doi:  10.1007/s10278-017-9997-y
    [10] Kessler N, Cyteval C, Gallix B, et al.Appendicitis: evaluation of sensitivity, specificity, and predictive values of US, doppler US, and laboratory findings[J].Radiology, 2004, 230:472-478. doi:  10.1148/radiol.2302021520
    [11] Kim KB, Park HJ, Song DH, et al. Developing an intelligent automatic appendix extraction method from ultrasonography based on fuzzy ART and image processing[J]. Comput Math Methods Med, 2015, 2015: 389057. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000004248632
    [12] Tajbakhsh N, Shin JY, Gurudu SR, et al.Convolutional neural networks for medical image analysis: full training or fine tuning? [J]. IEEE Trans Med Imaging, 2016, 35: 1299-1312. doi:  10.1109/TMI.2016.2535302
    [13] 孙夏, 吴蔚, 吴鹏, 等.基于卷积神经网络的颈动脉斑块超声图像特征识别[J].中国医疗器械信息, 2016, 9:4-8. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgylqxxx201609004
    [14] Salomon LJ, Winer N, Bernard JP, et al.A score-based method for quality control of fetal images at routine second-trimester ultrasound examination[J]. Prenat Diagn, 2008, 28: 822-827. doi:  10.1002/pd.2016
    [15] Chen H, Ni D, Qin J, et al. Standard plane localization in fetal ultrasound via domain transferred deep neural networks[J]. IEEE J Biomed Health Inform, 2015, 19: 1627-1636. doi:  10.1109/JBHI.2015.2425041
    [16] 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 Imaging, 2017, 36:1221-1230. doi:  10.1109/TMI.2017.2690836
    [17] Knackstedt C, Bekkers SC, Schummers G, et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the fast-EFs multicenter study[J]. J Am Coll Cardiol, 2015, 66: 1456-1466. doi:  10.1016/j.jacc.2015.07.052
    [18] Furiasse N, Thomas JD. Automated algorithmic software in echocardiography: artificial intelligence? [J]. J Am Coll Cardiol, 2015, 66:1467-1469. doi:  10.1016/j.jacc.2015.08.009
    [19] Jeganathan J, Knio Z, Amador Y, et al. Artificial intelligence in mitral valve analysis[J]. Ann Card Anaesth, 2017, 20: 129-134. doi:  10.4103/aca.ACA_243_16
    [20] Kumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: the health evidence workshop[J]. Am J Prev Med, 2013, 45: 228-236. doi:  10.1016/j.amepre.2013.03.017
    [21] 王弈, 李传富.人工智能方法在医学图像处理中的研究新进展[J].中国医学物理学杂志, 2013, 3:4138-4143. doi:  10.3969/j.issn.1005-202X.2013.03.013
    [22] Priester AM, Natarajan S, Culjat MO. Robotic ultrasound systems in medicine[J]. IEEE Trans Ultrason Ferroelectr Freq Control, 2013, 60: 507-523. doi:  10.1109/TUFFC.2013.2593
  • 加载中
计量
  • 文章访问数:  419
  • HTML全文浏览量:  83
  • PDF下载量:  1386
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-09-06
  • 刊出日期:  2018-09-30

目录

    /

    返回文章
    返回

    【温馨提醒】近日,《协和医学杂志》编辑部接到作者反映,有多名不法人员冒充期刊编辑发送见刊通知,鼓动作者添加微信,从而骗取版面费的行为。特提醒您,本刊与作者联系的方式均为邮件通知或电话,稿件进度通知邮箱为:mjpumch@126.com,编辑部电话为:010-69154261,请提高警惕,谨防上当受骗!如有任何疑问,请致电编辑部核实。谢谢!