Ambient Media and Systems. Second International ICST Conference, AMBI-SYS 2011, Porto, Portugal, March 24-25, 2011, Revised Selected Papers

Research Article

Development of Computer Vision Algorithm for Surgical Skill Assessment

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  • @INPROCEEDINGS{10.1007/978-3-642-23902-1_6,
        author={Gazi Islam and Kanav Kahol and John Ferrara and Richard Gray},
        title={Development of Computer Vision Algorithm for Surgical Skill Assessment},
        proceedings={Ambient Media and Systems. Second International ICST Conference, AMBI-SYS 2011, Porto, Portugal, March 24-25, 2011, Revised Selected Papers},
        proceedings_a={AMBI-SYS},
        year={2012},
        month={5},
        keywords={Skill Assessment Surgical Training Computer Vision Motion Tracking},
        doi={10.1007/978-3-642-23902-1_6}
    }
    
  • Gazi Islam
    Kanav Kahol
    John Ferrara
    Richard Gray
    Year: 2012
    Development of Computer Vision Algorithm for Surgical Skill Assessment
    AMBI-SYS
    Springer
    DOI: 10.1007/978-3-642-23902-1_6
Gazi Islam1,*, Kanav Kahol1,*, John Ferrara2,*, Richard Gray3,*
  • 1: Arizona State University
  • 2: Phoenix Integrated Surgical Residency
  • 3: Mayo Clinic
*Contact email: gislam@asu.edu, kanav@asu.edu, johnj@ferrara.cc, gray.richard@mayo.edu

Abstract

Advances in medical field have introduced new and progressive ways to intensify surgical resident training and surgical skills learning by developing systematic simulator training programs alongside traditional training. Both training methods need constant presence of a competent surgeon to subjectively assess the surgical dexterity of the trainee. Several studies have been done to measure user’s skill objectively and quantitatively, but all use sensors which could interfere with skill execution. Also the sterilization process in an actual surgery makes the use of sensors impossible. This paper proposes a novel video-based approach for observing surgeon’s hand and surgical tool movements in both surgical operation and training. Data is captured by video camera and then explored using computer vision algorithm. Finally by analyzing basic statistical parameters, observer-independent model has been developed through objective and quantitative measurement of surgical skills.