A Deep Learning-Based System for the Assessment of Dental Caries Using Colour Dental Photographs.

Journal: Studies in health technology and informatics
Published Date: Jan 25, 2024

Abstract

D1ental caries remains the most common chronic disease in childhood, affecting almost half of all children globally. Dental care and examination of children living in remote and rural areas is an ongoing challenge that has been compounded by COVID. The development of a validated system with the capacity to screen large numbers of children with some degree of automation has the potential to facilitate remote dental screening at low costs. In this study, we aim to develop and validate a deep learning system for the assessment of dental caries using color dental photos. Three state-of-the-art deep learning networks namely VGG16, ResNet-50 and Inception-v3 were adopted in the context. A total of 1020 child dental photos were used to train and validate the system. We achieved an accuracy of 79% with precision and recall respectively 95% and 75% in classifying 'caries' versus 'sound' with inception-v3.

Authors

  • Maryam Mehdizadeh
    The Australian e-Health Research Centre, CSIRO, Floreat, Australia.
  • Mohamed Estai
    The Australian e-Health Research Centre, CSIRO, Floreat, Australia.
  • Janardhan Vignarajan
    Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Perth, Western Australia, Australia.
  • Jilen Patel
    UWA Dental school, The University of Western Australia, Crawley, Australia.
  • Joanna Granich
    Telethon Kids Institute, The University of Western Australia, Crawley, Australia.
  • Michael Zaniovich
    Aria Dental, Perth, Australia.
  • Estie Kruger
    School of Human Sciences, The University of Western Australia, Crawley, Australia.
  • John Winters
    Department of Pediatric Dentistry, Perth Children Hospital, Nedlands, Australia.
  • Marc Tennant
    School of Human Sciences, The University of Western Australia, Crawley, Australia.
  • Sajib Saha
    Doheny Eye Institute, Los Angeles, CA, 90033, USA.

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