• Login
    View Item 
    •   MBRU Knowledge Repository Home
    • Hamdan Bin Mohammed College of Dental Medicine (HBMCDM)
    • Faculty Publications (HBMCDM)
    • View Item
    •   MBRU Knowledge Repository Home
    • Hamdan Bin Mohammed College of Dental Medicine (HBMCDM)
    • Faculty Publications (HBMCDM)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    The validity of an artificial intelligence application for assessment of orthodontic treatment need from clinical images

    Thumbnail
    View/Open
    304-2021.42 Ahmed Ghoneima.pdf (1.797Mb)
    Date
    2021
    Author
    Ghoneima, Ahmed
    Metadata
    Show full item record
    Abstract
    Aim: To assess the validity of a Convolutional Neural Network (CNN) digital model to detect and localize orthodontic malocclusions from intraoral clinical images. Materials and methods: The sample of this study consisted of the intraoral images of 700 Subjects. All images were intraoral clinical images, in one of the following views: Left Occlusion, Right Occlusion, Front Occlusion, Upper Occlusal, and Lower Occlusal. The following malocclusion conditions were localized: crowding, spacing, increased overjet, cross bite, open bite, deep bite. The images annotations were repeated by the same investigator (S.T) with a one week interval (ICC 0.9). The CNN model used for this research study was the “You Only Look Once” model. This model can detect and localize multiple objects or multiple instances of the same object in each image. It is a fully convolutional deep neural network; 24 convolutional layers followed by 2 fully connected layers. This model was implemented using the TensorFlow framework freely available from Google. Results: The created CNN model was able to detect and localize the malocclusions with an accuracy of 99.99%, precision of 99.79%, and a recall of 100%. Conclusions: The use of computational deep convolutional neural networks to identify and localize orthodontic problems from clinical images proved valid. The built AI engine accurately detected and localized malocclusion from different views of intra-oral clinical images.
    URI
    https://repository.mbru.ac.ae/handle/1/891
    Collections
    • Faculty Publications (HBMCDM)

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of MBRU Knowledge RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV