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All Journal IAES International Journal of Robotics and Automation (IJRA)
Suhartina, Rahmalisa
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Automotive Engineering Electrical & Electronics Engineering

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Fuzzy logic assessment of X-ray tube risks in robotic C-arm angiography: a failure mode and effect analysis study Firdaus, Ade; Adriansyah, Andi; Ferdana, Nanda; Suhartina, Rahmalisa; Surakusumah, Rino Ferdian; Haekal, Jakfat; Zulhamidi, Zulhamidi; Shamsudin, Abu Ubaidillah
IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v13i4.pp506-514

Abstract

This research examines the integration of robotic C-arm technology in angiography, a critical tool for treating cardiac conditions. The robotic C-arm, which includes an X-ray tube, is essential for scanning patients during procedures. The study also investigates the associated risks, specifically in Indonesian hospitals with cardiac facilities. Angiography is used to diagnose and treat heart disease by visualizing blood vessels and facilitating catheterization procedures. However, its mobility poses hazards and can impact the process. To address potential risks, failure mode and effect analysis (FMEA) is utilized. Traditionally, risk assessment using risk priority numbers (RPN) is conducted, but these may not accurately reflect failures due to complex evaluating processes. To overcome this limitation, fuzzy logic is employed, enhancing risk assessment accuracy. Through this approach, twenty-seven failure modes are identified across two brands, with ten major ones prioritized using fuzzy logic. These findings facilitate the development of preventive measures to mitigate future failures and enhance patient safety during angiography in hospitals. In conclusion, the study underscores the importance of robust risk management in medical equipment, particularly in dynamic environments. By integrating fuzzy logic into risk assessment, the study improves prioritization accuracy, enabling effective allocation of resources for preventive actions.
Co-Authors Andi Adriansyah Ferdana, Nanda Firdaus, Ade Haekal, Jakfat Shamsudin, Abu Ubaidillah Surakusumah, Rino Ferdian ZULHAMIDI, ZULHAMIDI