使用智能手机和单板电脑进行远程超声检查

封面图片

如何引用文章

全文:

详细

论证。移动设备的可用性和计算能力不断提高，导致其应用不断扩大。医学也不例外：单板电脑和智能手机被积极用于远程医疗。

目的是研究使用单板计算机和智能手机进行远程超声检查的技术可行性。

材料和方法。在这项研究中，超声视频图像采集是使用USB外置视频采集设备进行的。一台树莓派（Raspberry Pi）单板电脑和一台安卓（Android）智能手机被用作远程超声检查服务器的平台。VLC、Motion和USB摄像头被用作软件。专家也在移动设备上进行了远程评估，使用的是：VLC——当在VLC软件服务器上运行时；在其他情况下，在Windows 7和安卓上使用谷歌浏览器（Google Chrome）；在树莓派上使用Chromium。

结果。与基于AMT630A芯片组的设备相比，基于UTV007芯片组的视频采集设备提供更好的图像质量。最佳视频分辨率为720x576，每秒25帧。由于通信信道带宽要求 低（0.64±0.17 Mbps），树莓派上的进行远程超声检查的最佳软件是VLC。对于安卓智能手机，远程超声检查是可以在USB摄像头软件上进行的，但需要更高的通信信道带 宽（5.2±0.3 Mbps）。

结论。使用基于单板电脑和智能手机的设备使实现不贵的远程超声系统有可能，这潜在地有助于通过远程培训和咨询医生提高所做检查的质量。这些解决方案也可用于偏远地区、野外医疗和其他可能的移动医疗领域。

作者简介

Kirill M. Arzamasov

Moscow Center for Diagnostics and Telemedicine

编辑信件的主要联系方式.
Email: ArzamasovKM@zdrav.mos.ru
ORCID iD: 0000-0001-7786-0349
SPIN 代码: 3160-8062

MD, Cand. Sci. (Med.)

俄罗斯联邦, Moscow

Viktor A. Drogovoz

Scientific and Production Association “Russian Basic Information Technologies”

Email: Vdrog@mail.ru
ORCID iD: 0000-0001-9582-7147
SPIN 代码: 1804-2636

Cand. Sci. (Tech.)

俄罗斯联邦, Moscow

Tatiana M. Bobrovskaya

Moscow Center for Diagnostics and Telemedicine

Email: BobrovskayaTM@zdrav.mos.ru
ORCID iD: 0000-0002-2746-7554
SPIN 代码: 3400-8575

MD

俄罗斯联邦, Moscow

Anton V. Vladzymyrskyy

Moscow Center for Diagnostics and Telemedicine; The First Sechenov Moscow State Medical University

Email: VladzimirskijAV@zdrav.mos.ru
ORCID iD: 0000-0002-2990-7736
SPIN 代码: 3602-7120

MD, Dr. Sci. (Med.)

俄罗斯联邦, Moscow; Moscow

参考

  1. Shi J, Wang F, Qin M, et al. New ECG compression method for portable ECG monitoring system merged with binary convolutional auto-encoder and residual error compensation. Biosensors (Basel). 2022;12(7):524. doi: 10.3390/bios12070524
  2. Palacios DR, Shen K, Baig S, et al. Wide field of view handheld smart fundus camera for telemedicine applications. J Med Imaging (Bellingham). 2021;8(2):026001. doi: 10.1117/1.JMI.8.2.026001
  3. Shewale AD, Patil SA, Patil SR. Raspberry-pi based automatic health care modelling: An iOt approach. Compliance Engineering J. 2021;12(3):99–104.
  4. Recker F, Höhne E, Damjanovic D, Schäfer VS. Ultrasound in telemedicine: A brief overview. Appl Sci. 2022;12:958. doi: 10.3390/app12030958
  5. Lim TH, Choi HJ, Kang BS. Feasibility of dynamic cardiac ultrasound transmission via mobile phone for basic emergency teleconsultation. J Telemed Telecare. 2010;5(16):281–285. doi: 10.1258/jtt.2010.091109
  6. Miyashita T, Iketani Y, Nagamine Y, Goto T. FaceTime®for teaching ultrasound-guided anesthetic procedures in remote place. J Cli Monit. Comput. 2014;2(28):211–215. doi: 10.1007/s10877-013-9514-x
  7. Kim C, Cha H, Kang BS, et al. A feasibility study of smartphone-based telesonography for evaluating cardiac dynamic function and diagnosing acute appendicitis with control of the image quality of the transmitted videos. J Digit Imaging. 2016;3(29):347–356. doi: 10.1007/s10278-015-9849-6
  8. Boissin C, Blom L, Wallis L, et al. Image-based teleconsultation using smartphones or tablets: qualitative assessment of medical experts. Emergency Med J. 2017;34(2):95–99. doi: 10.1136/emermed-2015-205258
  9. Beckhauser E, Petrolini VA, Savaris A, et al. Are single-board computers an option for a low-cost multimodal telemedicine platform? First tests in the context of santa catarina state integrated telemedicine and telehealth system. In: Conference: 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS). 2016. Р. 163–168. doi: 10.1109/CBMS.2016.57
  10. Bhojwani H, Sain GK, Sharma GP. A hybrid connectivity oriented telemedician system for Indian landscape using raspberry Pi SBC & IOT. In: Conference: 2018 3rd Technology Innovation Management and Engineering Science International Conference (TIMES-iCON). 2018. Р. 1–5. doi: 10.1109/TIMES-iCON.2018.8621799
  11. De Oliveira DC, Wehrmeister MA. Using deep learning and low-cost rgb and thermal cameras to detect pedestrians in aerial images captured by multirotor UAV. Sensors (Basel). 2018;7(18):2244. doi: 10.3390/s18072244
  12. Kim W, Jung WS, Choi HK. Lightweight driver monitoring system based on multi-task mobilenets. Sensors (Basel). 2019;14(19):3200. doi: 10.3390/s19143200
  13. Peine A, Hallawa A, Schöffski O, et al. A deep learning approach for managing medical consumable materials in intensive care units via convolutional neural networks: technical proof-of-concept study. JMIR Med Informatics. 2019;4(7):e14806–e14806. doi: 10.2196/14806
  14. Yoo SK, Kim DK, Jung SM, et al. Performance of a web-based, realtime, tele-ultrasound consultation system over high-speed commercial telecommunication lines. J Telemed Telecare. 2004;10(3):175–179. doi: 10.1258/135763304323070841
  15. Panayides A, Antoniou ZC, Mylonas Y, et al. High-resolution, low-delay, and error-resilient medical ultrasound video communication using H.264/AVC over mobile WiMAX networks. IEEE J Biomed Health Inform. 2013;17(3):619–628. doi: 10.1109/TITB.2012.2232675
  16. Arzamasov KM, Bobrovskaya TM, Drogovoz VA. Streaming technology: From games to tele-ultrasound. Digital Diagnostics. 2022;2(3):131–140. (In Russ). doi: 10.17816/DD100779
  17. Arzamasov KM, Drogovoz VA. Systematic review of technologies and methods of tele-ultrasound. Medical Technologies Assessment Choice. 2020;(3):44-54. (In Russ). doi: 10.17116/medtech20204103144
  18. Le MT, Voigt L, Nathanson R, et al. Comparison of four handheld point-of-care ultrasound devices by expert users. Ultrasound J. 2022;14(1):27. doi: 10.1186/s13089-022-00274-6

补充文件

附件文件
动作
1. JATS XML
下载
2. 图1。接线图。

下载 (262KB)
索引源数据

版权所有 © Eco-Vector, 2023

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

##common.cookie##