Reading PAGE

Peer Evaluation activity

Downloads 4
Views 34
Full text requests 1
Followed by 1

Total impact ?

    Send a

    Dan has...

    Trusted 0
    Reviewed 0
    Emailed 0
    Shared/re-used 0
    Discussed 0
    Invited 0
    Collected 0

     

    This was brought to you by:

    block this user Dan Morris Trusted member

    Other

    Microsoft Research

    Providing metrics and performance feedback in a surgical simulator.

    Export to Mendeley

    One of the most important advantages of computer simulators for surgical training is the opportunity they afford for independent learning. However, if the simulator does not provide useful instructional feedback to the user, this advantage is significantly blunted by the need for an instructor to supervise and tutor the trainee while using the simulator. Thus, the incorporation of relevant, intuitive metrics is essential to the development of efficient simulators. Equally as important is the presentation of such metrics to the user in such a way so as to provide constructive feedback that facilitates independent learning and improvement. This paper presents a number of novel metrics for the automated evaluation of surgical technique. The general approach was to take criteria that are intuitive to surgeons and develop ways to quantify them in a simulator. Although many of the concepts behind these metrics have wide application throughout surgery, they have been implemented specifically in the context of a simulation of mastoidectomy. First, the visuohaptic simulator itself is described, followed by the details of a wide variety of metrics designed to assess the user's performance. We present mechanisms for presenting visualizations and other feedback based on these metrics during a virtual procedure. We further describe a novel performance evaluation console that displays metric-based information during an automated debriefing session. Finally, the results of several user studies are reported, providing some preliminary validation of the simulator, the metrics, and the feedback mechanisms. Several machine learning algorithms, including Hidden Markov Models and a Na ve Bayes Classifier, are applied to our simulator data to automatically differentiate users' expertise levels.

    Oh la laClose

    Your session has expired but don’t worry, your message
    has been saved.Please log in and we’ll bring you back
    to this page. You’ll just need to click “Send”.

    Your evaluation is of great value to our authors and readers. Many thanks for your time.

    Review Close

    Short review
    Select a comment
    Select a grade
    You and the author
    Anonymity My review is anonymous( Log in  or  Register )
    publish
    Close

    When you're done, click "publish"

    Only blue fields are mandatory.

    Relation to the author*
    Overall Comment*
    Anonymity* My review is anonymous( Log in  or  Register )
     

    Focus & Objectives*

    Have the objectives and the central topic been clearly introduced?

    Novelty & Originality*

    Do you consider this work to be an interesting contribution to knowledge?

    Arrangement, Transition and Logic

    Are the different sections of this work well arranged and distributed?

    Methodology & Results

    Is the author's methodology relevant to both the objectives and the results?

    Data Settings & Figures

    Were tables and figures appropriate and well conceived?

    References and bibliography

    Is this work well documented and has the bibliography been properly established?

    Writing

    Is this work well written, checked and edited?

    Write Your Review (you can paste text as well)
    Please be civil and constructive. Thank you.


    Grade (optional, N/A by default)

    N/A 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
    Close

    Your mailing list is currently empty.
    It will build up as you send messages
    and links to your peers.

     No one besides you has access to this list.
    Close
    Enter the e-mail addresses of your recipients in the box below.  Note: Peer Evaluation will NOT store these email addresses   log in
    Your recipients

    Your message:

    Your email : Your email address will not be stored or shared with others.

    Your message has been sent.

    Description

    Title : Providing metrics and performance feedback in a surgical simulator.
    Author(s) : Christopher Sewell, Dan Morris, Nikolas H Blevins, Sanjeev Dutta, Sumit Agrawal, Federico Barbagli, Kenneth Salisbury
    Abstract : One of the most important advantages of computer simulators for surgical training is the opportunity they afford for independent learning. However, if the simulator does not provide useful instructional feedback to the user, this advantage is significantly blunted by the need for an instructor to supervise and tutor the trainee while using the simulator. Thus, the incorporation of relevant, intuitive metrics is essential to the development of efficient simulators. Equally as important is the presentation of such metrics to the user in such a way so as to provide constructive feedback that facilitates independent learning and improvement. This paper presents a number of novel metrics for the automated evaluation of surgical technique. The general approach was to take criteria that are intuitive to surgeons and develop ways to quantify them in a simulator. Although many of the concepts behind these metrics have wide application throughout surgery, they have been implemented specifically in the context of a simulation of mastoidectomy. First, the visuohaptic simulator itself is described, followed by the details of a wide variety of metrics designed to assess the user's performance. We present mechanisms for presenting visualizations and other feedback based on these metrics during a virtual procedure. We further describe a novel performance evaluation console that displays metric-based information during an automated debriefing session. Finally, the results of several user studies are reported, providing some preliminary validation of the simulator, the metrics, and the feedback mechanisms. Several machine learning algorithms, including Hidden Markov Models and a Na ve Bayes Classifier, are applied to our simulator data to automatically differentiate users' expertise levels.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2008

    Affiliations Microsoft Research
    Journal : Computer aided surgery official journal of the International Soc
    Volume : 13
    Issue : 2
    Pages : 63-81
    Url : http://www.ncbi.nlm.nih.gov/pubmed/18317956

    Leave a comment

    This contribution has not been reviewed yet. review?

    You may receive the Trusted member label after :

    • Reviewing 10 uploads, whatever the media type.
    • Being trusted by 10 peers.
    • If you are blocked by 10 peers the "Trust label" will be suspended from your page. We encourage you to contact the administrator to contest the suspension.

    Does this seem fair to you? Please make your suggestions.

    Please select an affiliation to sign your evaluation:

    Cancel Evaluation Save

    Please select an affiliation:

    Cancel   Save

    Dan's Peer Evaluation activity

    Downloads 4
    Views 34
    Full text requests 1
    Followed by 1
    • Habiba Hassan Wassef, Senior professional, Independent international expert, United Nations, WHO, National Coordinator for the 7th European Framework Research Programme, National Research Center in Cairo, Egypt.

    Dan has...

    Trusted 0
    Reviewed 0
    Emailed 0
    Shared/re-used 0
    Discussed 0
    Invited 0
    Collected 0
    Invite this peer to...
    Title
    Start date (dd/mm/aaaa)
    Location
    URL
    Message
    send
    Close

    Full Text request

    Your request will be sent.

    Please enter your email address to be notified
    when this article becomes available

    Your email


     
    Your email address will not be shared or spammed.