TY - GEN
T1 - COMPUTER VISION ENABLED SMART TRAY FOR CENTRAL VENOUS CATHETERIZATION TRAINING
AU - Brown, Dailen
AU - Wu, Hang Ling
AU - Satpathy, Yohaan
AU - Gonzalez-Vargas, Jessica M.
AU - Tzamaras, Haroula
AU - Miller, Scarlett Rae
AU - Moore, Jason
N1 - Funding Information:
Research reported in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number R01HL127316. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Coauthors Dr. Moore and Miller own equity in Medulate, which may have a future interest in this project. Company ownership has been reviewed by the University’s Individual Conflict of Interest Committee and is currently being managed by the University.
Publisher Copyright:
© 2022 by ASME
PY - 2022
Y1 - 2022
N2 - A Computer Vision enabled Smart Tray (CVST) was designed for use in medical training for Central Venous Catheterization (CVC). The effects of background color on the ability of the computer vision algorithm to distinguish between tools and the tray was investigated. In addition, the computer vision algorithm was evaluated for accuracy in tool detection. Results indicate that a white monochromatic background is the most useful for segregating background from medical tools, and the algorithm was successfully able to detect 5 different CVC tools both individually and as a group in various arrangements, even when tools overlapped or touched. When the system was in error, it was nearly always due to one tool which has a color similar to that of the background. The CVST shows promise as a CVC training tool and demonstrates that computer vision can be used to accurately detect medical tools.
AB - A Computer Vision enabled Smart Tray (CVST) was designed for use in medical training for Central Venous Catheterization (CVC). The effects of background color on the ability of the computer vision algorithm to distinguish between tools and the tray was investigated. In addition, the computer vision algorithm was evaluated for accuracy in tool detection. Results indicate that a white monochromatic background is the most useful for segregating background from medical tools, and the algorithm was successfully able to detect 5 different CVC tools both individually and as a group in various arrangements, even when tools overlapped or touched. When the system was in error, it was nearly always due to one tool which has a color similar to that of the background. The CVST shows promise as a CVC training tool and demonstrates that computer vision can be used to accurately detect medical tools.
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U2 - 10.1115/DMD2022-1020
DO - 10.1115/DMD2022-1020
M3 - Conference contribution
AN - SCOPUS:85130258697
T3 - Proceedings of the 2022 Design of Medical Devices Conference, DMD 2022
BT - Proceedings of the 2022 Design of Medical Devices Conference, DMD 2022
PB - American Society of Mechanical Engineers
T2 - 2022 Design of Medical Devices Conference, DMD 2022
Y2 - 11 April 2022 through 14 April 2022
ER -