Artificial intelligence in regional anaesthesia

被引：0
作者：
Balavenkatasubramanian, J. [1 ]
Kumar, Senthil [1 ,2 ]
Sanjayan, R. D. [1 ,3 ]
机构：
[1] Ganga Med Ctr & Hosp Pvt Ltd, Dept Anaesthesia, Coimbatore, Tamil Nadu, India
[2] Ganga Med Ctr & Hosp Pvt Ltd, Anaesthesiol, Coimbatore, Tamil Nadu, India
[3] Ganga Med Ctr & Hosp Pvt Ltd, Dept Anaesthesiol, Reg Anaesthesia, Coimbatore, Tamil Nadu, India
来源：
INDIAN JOURNAL OF ANAESTHESIA | 2024年 / 68卷 / 01期
关键词：
Artificial intelligence; regional anaesthesia; sonoanatomy; training; ultrasound; ULTRASOUND; IDENTIFICATION; ANATOMY;
D O I：
10.4103/ija.ija_1274_23
中图分类号：
R614 [麻醉学];
学科分类号：
100217 ;
摘要：
Ultrasound-guided regional anaesthesia is used to facilitate the real-time performance of the regional block, increase the block success and reduce the complication rate. Artificial intelligence (AI) has been studied in many medical disciplines with high success rates, especially radiology. The purpose of this article was to review the evolution of AI in regional anaesthesia. The role of AI is to identify and optimise the sonography image, display the target, guide the practitioner to advance the needle tip to the intended target and inject the local anaesthetic. AI supports non-experts in training and clinical practice and experts in teaching ultrasound-guided regional anaesthesia.
引用
收藏
页码：100 / 104
页数：5
相关论文
共 15 条
  • [1] Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia
    Bowness, J.
    El-Boghdadly, K.
    Burckett-St Laurent, D.
    [J]. ANAESTHESIA, 2021, 76 (05) : 602 - 607
  • [2] Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia
    Bowness, James
    Varsou, Ourania
    Turbitt, Lloyd
    Burkett-St Laurent, David
    [J]. CLINICAL ANATOMY, 2021, 34 (05) : 802 - 809
  • [3] Current Applications and Future Impact of Machine Learning in Radiology
    Choy, Garry
    Khalilzadeh, Omid
    Michalski, Mark
    Do, Synho
    Samir, Anthony E.
    Pianykh, Oleg S.
    Geis, J. Raymond
    Pandharipande, Pari V.
    Brink, James A.
    Dreyer, Keith J.
    [J]. RADIOLOGY, 2018, 288 (02) : 318 - 328
  • [4] SLIDE: automatic spine level identification system using a deep convolutional neural network
    Hetherington, Jorden
    Lessoway, Victoria
    Gunka, Vit
    Abolmaesumi, Purang
    Rohling, Robert
    [J]. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2017, 12 (07) : 1189 - 1198
  • [5] Ultrasound guidance as a gold standard in regional anaesthesia
    Hopkins, P. M.
    [J]. BRITISH JOURNAL OF ANAESTHESIA, 2007, 98 (03) : 299 - 301
  • [6] Applying deep learning in recognizing the femoral nerve block region on ultrasound images
    Huang, Chanyan
    Zhou, Ying
    Tan, Wulin
    Qiu, Zeting
    Zhou, Huaqiang
    Song, Yiyan
    Zhao, Yue
    Gao, Shaowei
    [J]. ANNALS OF TRANSLATIONAL MEDICINE, 2019, 7 (18)
  • [7] Backpropagation Applied to Handwritten Zip Code Recognition
    LeCun, Y.
    Boser, B.
    Denker, J. S.
    Henderson, D.
    Howard, R. E.
    Hubbard, W.
    Jackel, L. D.
    [J]. NEURAL COMPUTATION, 1989, 1 (04) : 541 - 551
  • [8] Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
    Lee, Cecilia S.
    Tyring, Ariel J.
    Wu, Yue
    Xiao, Sa
    Rokem, Ariel S.
    DeRuyter, Nicolaas P.
    Zhang, Qinqin
    Tufail, Adnan
    Wang, Ruikang K.
    Lee, Aaron Y.
    [J]. SCIENTIFIC REPORTS, 2019, 9 (1)
  • [9] Lloyd J, 2022, ADV EXP MED BIOL, V1356, P117, DOI 10.1007/978-3-030-87779-8_6
  • [10] Robust shape tracking with multiple models in ultrasound images
    Nascimento, Jacinto C.
    Marques, Jorge S.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2008, 17 (03) : 392 - 406
12