Experimental validation of computer-vision methods for the successful detection of endodontic treatment obturation and progression from noisy radiographs.

Journal: Oral radiology
Published Date: Apr 25, 2023

Abstract

PURPOSE: (1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics.

Authors

  • Habib Al Hasan
    Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
  • Farhan Hasin Saad
    Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
  • Saif Ahmed
    Lecturer, Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
  • Nabeel Mohammed
    Apurba NSU R&D Lab, Department of Electrical and Computer Engineering North South University, Dhaka, Bangladesh.
  • Taseef Hasan Farook
    Maxillofacial Prosthetic Service, Prosthodontic Unit, School of Dental Sciences, UniversitiSains Malaysia, Health Campus, Kelantan 16150, Malaysia.
  • James Dudley
    Associate Professor, Adelaide Dental School, The University of Adelaide, South Australia, Australia.

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