Tele-ultrasound imaging using smartphones and single-board PCs
- Authors: Arzamasov K.M.1, Drogovoz V.A.2, Bobrovskaya T.M.1, Vladzymyrskyy A.V.1,3
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Affiliations:
- Moscow Center for Diagnostics and Telemedicine
- Scientific and Production Association “Russian Basic Information Technologies”
- The First Sechenov Moscow State Medical University
- Issue: Vol 4, No 1 (2023)
- Pages: 15-23
- Section: Technical Reports
- URL: https://journals.rcsi.science/DD/article/view/146872
- DOI: https://doi.org/10.17816/DD111816
- ID: 146872
Cite item
Abstract
BACKGROUND: Mobile devices are widely available and their computational performance increases. Nonetheless, medicine should not be an exception: single-board computers and mobile phones are crucial aides in telehealth.
AIM: To explore tele-ultrasound scope using smartphones and single-board computers
MATERIALS AND METHODS: This study focused on capturing ultrasound videos using external video recording devices connected via USB. Raspberry Pi single-board computers and Android smartphones have been used as platforms to host a tele-ultrasound server. Used software: VLC, Motion, and USB camera. A remote expert assessment was performed with mobile devices using the following software: VLC acted as a VLC server, Google Chrome for OS Windows 7 and OS Android was used in the remaining scenarios, and Chromium browser was installed on the Raspberry Pi computer.
OUTCOMES: The UTV007 chip-based video capture device produces better images than the AMT630A-based device. The optimum video resolution was 720×576 and 25 frames per second. VLC and OBS studios are considered the most suitable for a raspberry-based ultrasound system owing to low equipment and bandwidth requirements (0.64±0.17 Mbps for VLC; 0.5 Mbps for OBS studio). For Android phone OS, the ultrasound system was set with the USB camera software, although it required a faster network connection speed (5.2±0.3 Mbps).
CONCLUSION: The use of devices based on single-board computers and smartphones implements a low-cost tele-ultrasound system, which potentially improves the quality of studies performed through distance learning and consulting doctors. These solutions can be used in remote regions for “field” medicine tasks and other possible areas of m-health.
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##article.viewOnOriginalSite##About the authors
Kirill M. Arzamasov
Moscow Center for Diagnostics and Telemedicine
Author for correspondence.
Email: ArzamasovKM@zdrav.mos.ru
ORCID iD: 0000-0001-7786-0349
SPIN-code: 3160-8062
MD, Cand. Sci. (Med.)
Russian Federation, MoscowViktor A. Drogovoz
Scientific and Production Association “Russian Basic Information Technologies”
Email: Vdrog@mail.ru
ORCID iD: 0000-0001-9582-7147
SPIN-code: 1804-2636
Cand. Sci. (Tech.)
Russian Federation, MoscowTatiana M. Bobrovskaya
Moscow Center for Diagnostics and Telemedicine
Email: BobrovskayaTM@zdrav.mos.ru
ORCID iD: 0000-0002-2746-7554
SPIN-code: 3400-8575
MD
Russian Federation, MoscowAnton 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-code: 3602-7120
MD, Dr. Sci. (Med.)
Russian Federation, Moscow; MoscowReferences
- 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
- 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
- Shewale AD, Patil SA, Patil SR. Raspberry-pi based automatic health care modelling: An iOt approach. Compliance Engineering J. 2021;12(3):99–104.
- Recker F, Höhne E, Damjanovic D, Schäfer VS. Ultrasound in telemedicine: A brief overview. Appl Sci. 2022;12:958. doi: 10.3390/app12030958
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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