Artificial Intelligence-Supported Ultrasonography in Anesthesiology: Evaluation of a Patient in the Operating Theatre

被引:2
|
作者
Mika, Slawomir [1 ]
Gola, Wojciech [2 ]
Gil-Mika, Monika [3 ]
Wilk, Mateusz [4 ]
Misiolek, Hanna [5 ]
机构
[1] Medica Co Ltd, Upper Silesian Sch Ultrasonog, PL-41500 Chorzow, Poland
[2] Jan Kochanowski Univ, Coll Med, PL-25317 Kielce, Poland
[3] Municipal Hosp Co Ltd, PL-41703 Ruda Slaska, Poland
[4] WSB Univ, Coll Med, PL-41300 Dabrowa Gornicza, Poland
[5] Med Univ Silesia, Sch Med, Dept Anaesthesiol & Crit Care, Div Dent, PL-41808 Zabrze, Poland
来源
JOURNAL OF PERSONALIZED MEDICINE | 2024年 / 14卷 / 03期
关键词
artificial intelligence; ultrasonography; regional anesthesia; PERIPHERAL-NERVE BLOCK; ULTRASOUND; IDENTIFICATION; ANATOMY;
D O I
10.3390/jpm14030310
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
Artificial intelligence has now changed regional anesthesia, facilitating, therefore, the application of the regional block under the USG guidance. Innovative technological solutions make it possible to highlight specific anatomical structures in the USG image in real time, as needed for regional block. This contribution presents such technological solutions as U-Net architecture, BPSegData and Nerveblox and the basis for independent assisting systems in the use of regional blocks, e.g., ScanNav Anatomy PNB or the training system NeedleTrainer. The article describes also the systems integrated with the USG devices, such as Mindray SmartNerve or GE cNerve as well as the robotic system Magellan which substantially increases the patient's safety, time needed for the regional block and quality of the procedure. All the solutions presented in this article facilitate the performance of regional blocks by less experienced physicians and appear as an excellent educational tool which, at the same time, improves the availability of the more and more popular regional anesthesia. Will, therefore, artificial intelligence replace physicians in regional block procedures? This seems unlikely. It will, however, assist them in a significant manner, contributing to better effectiveness and improved safety of the patient.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Line-Field Confocal Optical Coherence Tomography of Plaque Psoriasis Under IL-17 Inhibitor Therapy: Artificial Intelligence-Supported Analysis
    Wirsching, Hanna B.
    Mayer, Oliver J.
    Schlingmann, Sophia
    Thamm, Janis R.
    Schiele, Stefan
    Rubeck, Anna
    Heinz, Wera
    Welzel, Julia
    Schuh, Sandra
    APPLIED SCIENCES-BASEL, 2025, 15 (02):
  • [32] Artificial intelligence-enhanced patient evaluation: bridging art and science
    Oikonomou, Evangelos K.
    Khera, Rohan
    EUROPEAN HEART JOURNAL, 2024, : 3204 - 3218
  • [33] Antiviral activity of curcumin and its analogs selected by an artificial intelligence-supported activity prediction system in SARS-CoV-2-infected VeroE6 cells
    Teshima, Koji
    Tanaka, Takeshi
    Zhengmao, Ye
    Ikeda, Ken
    Matsuzaki, Takao
    Shiroma, Tamotsu
    Muroya, Ayumu
    Hosoda, Masato
    Yasugi, Mayo
    Komatsu, Hirotsugu
    NATURAL PRODUCT RESEARCH, 2024, 38 (05) : 867 - 872
  • [34] Development and evaluation of an artificial intelligence-based workflow for the prioritization of patient portal messages
    Yang, Jie
    So, Jonathan
    Zhang, Hao
    Jones, Simon
    Connolly, Denise M.
    Golding, Claudia
    Griffes, Esmelin
    Szerencsy, Adam C.
    Wu, Tzer
    Aphinyanaphongs, Yindalon
    Major, Vincent J.
    JAMIA OPEN, 2024, 7 (03)
  • [35] Patient selection for corneal topographic evaluation of keratoconus: A screening approach using artificial intelligence
    Ahn, Hyunmin
    Kim, Na Eun
    Chung, Jae Lim
    Kim, Young Jun
    Jun, Ikhyun
    Kim, Tae-im
    Seo, Kyoung Yul
    FRONTIERS IN MEDICINE, 2022, 9
  • [36] Using Machine Learning Technology (Early Artificial Intelligence-Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study
    White, Becky K.
    Gombert, Arnault
    Nguyen, Tim
    Yau, Brian
    Ishizumi, Atsuyoshi
    Kirchner, Laura
    Leon, Alicia
    Wilson, Harry
    Jaramillo-Gutierrez, Giovanna
    Cerquides, Jesus
    D'Agostino, Marcelo
    Salvi, Cristiana
    Sreenath, Ravi Shankar
    Rambaud, Kimberly
    Samhouri, Dalia
    Briand, Sylvie
    Purnat, Tina
    JMIR INFODEMIOLOGY, 2023, 3 (01):
  • [37] Artificial Intelligence-Supported Video Analysis as a Means to Assess the Impact of DROP-IN Image Guidance on Robotic Surgeons: Radioguided Sentinel Lymph Node versus PSMA-Targeted Prostate Cancer Surgery
    Azargoshasb, Samaneh
    de Barros, Hilda A. A.
    Rietbergen, Daphne D. D.
    Dell'Oglio, Paolo
    van Leeuwen, Pim J. J.
    Wagner, Christian
    Stricker, Phillip
    Vidal-Sicart, Sergi
    Briganti, Alberto
    Maurer, Tobias
    van der Poel, Henk G. G.
    van Oosterom, Matthias N. N.
    van Leeuwen, Fijs W. B.
    ADVANCED INTELLIGENT SYSTEMS, 2023, 5 (10)
  • [38] Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts
    Hamyoon, Hessam
    Chan, Wai Yee
    Mohammadi, Afshin
    Kuzan, Taha Yusuf
    Mirza-Aghazadeh-Attari, Mohammad
    Leong, Wai Ling
    Altintoprak, Kuebra Murzoglu
    Vijayananthan, Anushya
    Rahmat, Kartini
    Ab Mumin, Nazimah
    Leong, Sook Sam
    Ejtehadifar, Sajjad
    Faeghi, Fariborz
    Abolghasemi, Jamileh
    Ciaccio, Edward J.
    Acharya, U. Rajendra
    Ardakani, Ali Abbasian
    EUROPEAN JOURNAL OF RADIOLOGY, 2022, 157
  • [39] An Ethically Supported Framework for Determining Patient Notification and Informed Consent Practices When Using Artificial Intelligence in Health Care
    Rose, Susannah L.
    Shapiro, Devora
    CHEST, 2024, 166 (03) : 572 - 578
  • [40] An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study
    Goertz, Magdalena
    Baumgaertner, Kilian
    Schmid, Tamara
    Muschko, Marc
    Woessner, Philipp
    Gerlach, Axel
    Byczkowski, Michael
    Sueltmann, Holger
    Duensing, Stefan
    Hohenfellner, Markus
    DIGITAL HEALTH, 2023, 9