COMPUTER VISION ENABLED SMART TRAY FOR CENTRAL VENOUS CATHETERIZATION TRAINING

Dailen Brown, Hang Ling Wu, Yohaan Satpathy, Jessica M. Gonzalez-Vargas, Haroula Tzamaras, Scarlett Rae Miller, Jason Moore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 Design of Medical Devices Conference, DMD 2022
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791885710
DOIs
StatePublished - 2022
Event2022 Design of Medical Devices Conference, DMD 2022 - Minneapolis, Virtual, United States
Duration: Apr 11 2022Apr 14 2022

Publication series

NameProceedings of the 2022 Design of Medical Devices Conference, DMD 2022

Conference

Conference2022 Design of Medical Devices Conference, DMD 2022
Country/TerritoryUnited States
CityMinneapolis, Virtual
Period4/11/224/14/22

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Medicine (miscellaneous)

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