Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery

被引:16
作者
Liu, Yubo [1 ]
Cheng, Liangzhen [2 ]
机构
[1] Jiangxi Armed Police Corps Hosp, Dept Anesthesiol, Nanchang 330000, Jiangxi, Peoples R China
[2] Jiangxi Armed Police Corps Hosp, Dept Surg 2, Nanchang 330000, Jiangxi, Peoples R China
关键词
MACHINE; SEGMENTATION;
D O I
10.1155/2021/6231116
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
In order to discuss the clinical characteristics of patients with scapular fracture, deep learning model was adopted in ultrasound images of patients to locate the anesthesia point of patients during scapular fracture surgery treated with the regional nerve block. 100 patients with scapular fracture who were hospitalized for emergency treatment in the hospital were recruited. Patients in the algorithm group used ultrasound-guided regional nerve block puncture, and patients in the control group used traditional body surface anatomy for anesthesia positioning. The ultrasound images of the scapula of the contrast group were used for the identification of the deep learning model and analysis of anesthesia acupuncture sites. The ultrasound images of the scapula anatomy of the patients in the contrast group were extracted, and the convolutional neural network model was employed for training and test. Moreover, the model performance was evaluated. It was found that the adoption of deep learning greatly improved the accuracy of the image. It took an average of 7.5 +/- 2.07 minutes from the time the puncture needle touched the skin to the completion of the injection in the algorithm group (treated with artificial intelligence ultrasound positioning). The operation time of the control group (anatomical positioning) averaged 10.2 +/- 2.62min. Moreover, there was a significant difference between the two groups (p<0.05). The method adopted in the contrast group had high positioning accuracy and good anesthesia effect, and the patients had reduced postoperative complications of patients (all P<0.005). The deep learning model can effectively improve the accuracy of ultrasound images and measure and assist the treatment of future clinical cases of scapular fractures. While improving medical efficiency, it can also accurately identify patient fractures, which has great adoption potential in improving the effect of surgical anesthesia.
引用
收藏
页数:10
相关论文
共 22 条
  • [1] Artificial intelligence in cancer imaging: Clinical challenges and applications
    Bi, Wenya Linda
    Hosny, Ahmed
    Schabath, Matthew B.
    Giger, Maryellen L.
    Birkbak, Nicolai J.
    Mehrtash, Alireza
    Allison, Tavis
    Arnaout, Omar
    Abbosh, Christopher
    Dunn, Ian F.
    Mak, Raymond H.
    Tamimi, Rulla M.
    Tempany, Clare M.
    Swanton, Charles
    Hoffmann, Udo
    Schwartz, Lawrence H.
    Gillies, Robert J.
    Huang, Raymond Y.
    Aerts, Hugo J. W. L.
    [J]. CA-A CANCER JOURNAL FOR CLINICIANS, 2019, 69 (02) : 127 - 157
  • [2] Chen Q.R., 2018, COMPUTER TECHNOLOGY, V28, DOI [10.3969/j.issn.1673-629X.2018.04.043, DOI 10.3969/J.ISSN.1673-629X.2018.04.043]
  • [3] Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer
    Fave, Xenia
    Zhang, Lifei
    Yang, Jinzhong
    Mackin, Dennis
    Balter, Peter
    Gomez, Daniel
    Followill, David
    Jones, Aaron Kyle
    Stingo, Francesco
    Liao, Zhongxing
    Mohan, Radhe
    Court, Laurence
    [J]. SCIENTIFIC REPORTS, 2017, 7
  • [4] Machine learning in anaesthesia: reactive, proactive ... predictive!
    Gambus, Pedro L.
    Jaramillo, Sebastian
    [J]. BRITISH JOURNAL OF ANAESTHESIA, 2019, 123 (04) : 401 - 403
  • [5] Ultrasound-guided pudendal nerve block in children: A new technique of ultrasound-guided transperineal approach
    Gaudet-Ferrand, Isabelle
    De La Arena, Pablo
    Bringuier, Sophie
    Raux, Olivier
    Hertz, Laurent
    Kalfa, Nicolas
    Sola, Chrystelle
    Dadure, Christophe
    [J]. PEDIATRIC ANESTHESIA, 2018, 28 (01) : 53 - 58
  • [6] The practical implementation of artificial intelligence technologies in medicine
    He, Jianxing
    Baxter, Sally L.
    Xu, Jie
    Xu, Jiming
    Zhou, Xingtao
    Zhang, Kang
    [J]. NATURE MEDICINE, 2019, 25 (01) : 30 - 36
  • [7] Brain SegNet: 3D local refinement network for brain lesion segmentation
    Hu, Xiaojun
    Luo, Weijian
    Hu, Jiliang
    Guo, Sheng
    Huang, Weilin
    Scott, Matthew R.
    Wiest, Roland
    Dahlweid, Michael
    Reyes, Mauricio
    [J]. BMC MEDICAL IMAGING, 2020, 20 (01)
  • [8] Basic principles of magnetic resonance imaging for beginner oral and maxillofacial radiologists
    Kagawa, Toyohiro
    Yoshida, Shoko
    Shiraishi, Tomoko
    Hashimoto, Marie
    Inadomi, Daisuke
    Sato, Mamoru
    Tsuzuki, Takashi
    Miwa, Kunihiro
    Yuasa, Kenji
    [J]. ORAL RADIOLOGY, 2017, 33 (02) : 92 - 100
  • [9] Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview
    Lee, Sangil
    Mohr, Nicholas M.
    Street, W. Nicholas
    Nadkarni, Prakash
    [J]. WESTERN JOURNAL OF EMERGENCY MEDICINE, 2019, 20 (02) : 219 - 227
  • [10] Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
    Liang, Huiying
    Tsui, Brian Y.
    Ni, Hao
    Valentim, Carolina C. S.
    Baxter, Sally L.
    Liu, Guangjian
    Cai, Wenjia
    Kermany, Daniel S.
    Sun, Xin
    Chen, Jiancong
    He, Liya
    Zhu, Jie
    Tian, Pin
    Shao, Hua
    Zheng, Lianghong
    Hou, Rui
    Hewett, Sierra
    Li, Gen
    Liang, Ping
    Zang, Xuan
    Zhang, Zhiqi
    Pan, Liyan
    Cai, Huimin
    Ling, Rujuan
    Li, Shuhua
    Cui, Yongwang
    Tang, Shusheng
    Ye, Hong
    Huang, Xiaoyan
    He, Waner
    Liang, Wenqing
    Zhang, Qing
    Jiang, Jianmin
    Yu, Wei
    Gao, Jianqun
    Ou, Wanxing
    Deng, Yingmin
    Hou, Qiaozhen
    Wang, Bei
    Yao, Cuichan
    Liang, Yan
    Zhang, Shu
    Duan, Yaou
    Zhang, Runze
    Gibson, Sarah
    Zhang, Charlotte L.
    Li, Oulan
    Zhang, Edward D.
    Karin, Gabriel
    Nguyen, Nathan
    [J]. NATURE MEDICINE, 2019, 25 (03) : 433 - +